Technology-focused Acquisitions: Performance and Functionality as Differentiators 9783110562095, 9783110559170

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Jan Paul Stein Technology-focused Acquisitions

Jan Paul Stein

Technology-focused Acquisitions

Performance and Functionality as Differentiators

ISBN 978-3-11-055917-0 e-ISBN (PDF) 978-3-11-056209-5 e-ISBN (EPUB) 978-3-11-055930-9 Library of Congress Cataloging-in-Publication Data A CIP catalog record for this book has been applied for at the Library of Congress. Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available on the Internet at http://dnb.dnb.de. © 2017 Walter de Gruyter GmbH, Berlin/Boston Printing and binding: CPI books GmbH, Leck ♾ Printed on acid-free paper Printed in Germany www.degruyter.com

| There are no passengers on spaceship earth. We are all crew. Marshall McLuhan

Acknowledgements Having been part of an engaging and inspiring community has been a great help for completing my PhD. Therefore, I would like to take this opportunity to thank wholeheartedly everybody who through encouragement, guidance, and support has contributed to making my PhD journey as smooth, enjoyable, and productive as possible. First and foremost I would like to thank Joachim Henkel for giving me the opportunity to work on such interesting and relevant research at his chair. Your enthusiasm, commitment and advice have enabled me to turn this research into a success. I have rarely met someone like you who is not only creative and perceptive but also caring and fair and whom I feel truly believes in the good of people and brings out the best in them. I would like to extend my gratitude to Christoph Fuchs for being so kind to chair my dissertation committee and to Thomas Hutzschenreuter for his kind agreement to act as my second PhD supervisor. I would like to thank Christina Raasch, Michael Zaggl, and Tim Schweisfurth for their advice and willingness to help whenever possible. I am grateful for the team at the Dr. Theo Schöller-Stiftungslehrstuhl für Technologie- und Innovationsmanagement. They have provided me with encouragement, kept me motivated, and made sure that there is some fun every day. Thank you especially, Evelin, for your warmheartedness and thoughtfulness. I am grateful for my master’s student Fabian, who it was particularly fun to work with due to his dedication and drive to exceed expectations. I am thankful for my student assistants Moritz, Harald, Tim, and Christian because of their hard and diligent work. All of you have enabled me to keep a high pace of progress. I am grateful to Brigitte Krenn and Sebastian Urban for providing me with relevant insights into the field of artificial intelligence. In addition, I would like to thank my mentor, Max Flötotto, for giving me advice and feedback on my research. My research would not have been possible without the collaboration of numerous interview partners. Therefore, I would like to express my gratitude for their commitment. I am deeply indebted to my wife, Ting, who has greatly supported me throughout my time as a PhD student, my son, Roman, and my daughter Juliana, who always make me happy and keep me motivated, my parents, my sister, and my friends. All of you have kept me grounded and been a true source of joy.

Contents List of figures | XIII List of tables | XV List of abbreviations | XVII Zusammenfassung | XIX Abstract | XXI 1 1.1 1.2 1.3 1.4 2 2.1 2.1.1 2.1.2 2.1.3 2.2 2.2.1 2.2.2 2.2.3 2.2.4 3 3.1 3.2 3.2.1 3.2.2

Introduction | 1 Motivation—Understanding technology-focused acquisitions as an instance of a market for technology | 1 Research objective and design | 3 Summary of results | 5 Structure of the dissertation | 7 Theoretical foundations—Technology-focused acquisitions and strategic decisions | 9 Technology-focused acquisitions | 9 Definition and characteristics of technology-focused acquisitions | 9 Process of technology-focused acquisitions | 12 Key issues in technology-focused acquisitions | 15 Strategic decision making in technology-focused acquisitions | 17 Strategic decision making | 17 Acquisitions as a decision making process | 19 Key decisions in technology-focused acquisitions | 21 Relevance of acquisition timing and deal value decisions | 22 Performance- and functionality-focus in product development and acquisitions | 25 Core concepts of product development and the relevance of uncertainty | 25 Product performance and functionality in product development | 27 Definition of product performance- and product functionality-focused innovations | 27 Challenges in delineating product performance- and product functionality-focused innovations | 32

X | Contents

3.2.3 3.2.4 3.3 3.4 3.4.1 3.4.2 3.4.3 3.5 4 4.1 4.1.1 4.1.2 4.1.3 4.1.4 4.2 4.2.1 4.2.2 4.2.3 4.2.4 4.3 5 5.1 5.1.1 5.1.2 5.1.3 5.2 5.2.1 5.2.2 5.2.3 5.3 5.3.1

Technology-level foundations of product performance- and product functionality-focused innovations | 34 Relation of product performance- and product functionality-focused innovations to other innovation typologies | 36 Product performance and product functionality in technology-focused acquisitions | 39 Derivation of hypotheses | 44 Uncertainty profiles of performance- and functionality-focused acquisitions | 45 Acquisition timing with respect to performance- and functionalityfocused acquisitions | 52 Deal value with respect to performance- and functionality-focused acquisitions | 56 Discussion of theoretical findings and conclusion | 60 Qualitative Study—Acquisitions in the ICT Industry | 63 Methodology | 63 Research design | 63 Sampling | 64 Data, data collection, and data analysis | 69 Methodological rigor in the qualitative study | 73 Results | 74 Performance- and functionality-focused acquisitions in the ICT industry | 74 Uncertainty and risk of technology-focused acquisitions in ICT | 82 Acquisition timing in technology-focused acquisitions in ICT | 91 Acquisition deal value in technology-focused acquisitions in ICT | 101 Discussion of qualitative results and conclusion | 108 Quantitative study—Performance and functionality in AI-related acquisitions | 113 Methodology | 114 Empirical setting—Acquisitions in AI | 114 Sampling of AI-related acquisitions | 117 Data and data collection | 121 Variables | 122 Dependent variables—Acquisition timing and deal value | 124 Performance- and functionality-focus of acquisitions as the independent variable | 127 Control variables related to acquirer, target, and deal | 128 Descriptive results | 131 Characterization of dataset and variables | 131

Contents | XI

5.3.2 5.3.3 5.4 5.4.1 5.4.2 5.5 6 6.1 6.2 6.3

Correlations | 135 Uni- and bi-variate analysis and variable transformations | 139 Results of hypothesis tests | 140 Acquisition timing | 140 Deal value | 149 Discussion of quantitative results and conclusion | 158 Conclusion, contributions, and outlook | 163 Contributions to theory | 163 Practical implications | 167 Outlook | 168

Part Appendix  Appendix | 173 A.1 Keyword-based filtering of acquisitions | 173 A.2 Rating approach for technology-focused acquisitions | 176 A.3 Rating approach for performance- and functionality-focused acquisitions | 177 A.4 Correlation table of transformed variables | 182 A.5 Univariate analysis—histograms | 184 A.6 Bivariate analysis—scatter plots and boxplots | 186 Bibliography | 190

List of figures Fig. 1: Phases, key decisions, and aspired outcomes in technology-focused acquisitions | 21 Fig. 2: Simplified framework distinguishing performance- and functionality-focused acquisitions on a technology level | 40 Fig. 3: Temporal evolution of technology, market, and combined uncertainty | 51 Fig. 4: Data analysis process of qualitative study | 71 Fig. 5: Acquisition typology | 79 Fig. 6: Risk components and drivers of risk in technology-focused acquisitions | 83 Fig. 7: Market and technology risk in performance- and functionality-focused acquisitions | 88 Fig. 8: Windows of opportunity for conducting an acquisition | 92 Fig. 9: Acquisition timing mechanisms applied to performance- and functionality-focused acquisitions | 97 Fig. 10: Logic governing deal value decisions | 102 Fig. 11: Deal value mechanisms in relation to performance- and functionality-focused acquisitions | 105 Fig. 12: Performance improvement of winning teams in ImageNet competition | 116 Fig. 13: Sampling approach | 118 Fig. 14: Structure of dataset—top acquirers (left) and distribution of applications and technologies (right) | 134 Fig. 15: Bi-variate hypothesis tests—differences of mean | 141 Fig. 16: Rating approach for performance- and functionality-focused acquisitions | 178 Fig. 17: Histograms of non-transformed variables | 184 Fig. 18: Histograms of transformed variables | 185 Fig. 19: Scatter plots for TAge on the vertical axis; linear fit shown as a dashed line | 186 Fig. 20: Scatter plots for TAge_log on the vertical axis; linear fit shown as a dashed line | 187 Fig. 21: Boxplots for TAge on the vertical axis | 188 Fig. 22: Boxplots for TAge_log on the vertical axis | 189

List of tables Tab. 1: Example performance dimensions—primarily in relation to products in the ICT industry (not exhaustive) | 29 Tab. 2: Overview of interviews, interview partners, and firms |68 Tab. 3: Variable names, descriptions, and sources | 122 Tab. 4: Descriptive statistics | 132 Tab. 5: Descriptive statistics of dependent variables partitioned by Deal Technology Type | 133 Tab. 6: Pairwise correlations of variables | 136 Tab. 7: Regression analysis acquisition timing (TAge_log and TProd_Avail) | 144 Tab. 8: Regression analysis acquisition timing (TSize_log) | 146 Tab. 9: Regression analysis deal value (DDeal_Val_log and DDV_TS_log) | 152 Tab. 10: Heckman two-stage regression for DDeal_Val_log | 156 Tab. 11: Excerpt of keyword list containing AI-related terms |173 Tab. 12: Sources used for generation of keyword list | 175 Tab. 13: Pairwise correlations of transformed variables | 182

List of abbreviations AI

Artificial Intelligence

B2B

Business to Business

B2C

Business to Customer

CPU

Central Processing Unit

DCF

Discounted Cash Flow

EVP

Executive Vice President

GPT

General Purpose Technology

GPU

Graphics Processing Unit

GUI

Graphical User Interface

ICT

Information and Communication Technology

IPO

Initial Public Offering

IP

Intellectual Property

IT

Information Technology

M&A

Mergers & Acquisitions

NAICS

North American Industry Classification System

NIH

Not Invented Here

NPV

Net Present Value

OLS

Ordinary Least Squares

PLM

Product Lifecycle Management

R&D

Research & Development

RBV

Resource-based View

SIC

Standard Industry Classification

SVP

Senior Vice President

TP

Technological Potential

TY

Technological Yield

US

United States

VIF

Variance Inflation Factor

VC

Venture Capital

VP

Vice President

Zusammenfassung Im Jahr 2012 kaufte das international tätige und in Akquisitionen erfahrene Unternehmen EMC1 zwei Firmen, XtremIO und Syncplicity, um deren Technologie zu erhalten. Zum Kaufzeitpunkt war XtremIO jünger als Syncplicity, besaß keine Kunden und hatte auch kein fertiges Produkt. Im Gegensatz dazu hatte Syncplicity bereits zehntausende Kunden und ein Produkt auf dem Markt. Warum würde EMC trotz großer Erfahrung solch unterschiedliche Entscheidungen bezüglich des Kaufzeitpunktes, d.h. des Reifegrads der Zielunternehmen treffen? Dieses zunächst seltsam anmutende Akquisitionsverhalten bildet die Motivation für die vorliegende Forschungsarbeit, in der bestimmte Muster in technologieorientierten Unternehmensübernahmen identifiziert werden sollen. Zu diesem Zweck wird die Unterscheidung von performanz- und funktionalitäts-orientierten Akquisitionen als neues Framework eingeführt. In performanz-orientierten Akquisitionen soll die Technologie des Kaufziels dafür eingesetzt werden, die Performanz eines Produkts des Käufers zu verbessern. Funktionalitäts-orientierte Akquisitionen dienen dem Zweck, dem Käufer neue Produktfunktionalitäten zur Verfügung zu stellen. Innerhalb der vorliegenden Arbeit soll erforscht werden, wie dieses Framework die Entscheidungen bezüglich des Kaufzeitpunktes im Sinne des Reifegrads der gekauften Firmen und des Kaufpreises beeinflusst. Aus theoretischer Sicht ist das Framework zu performanz- und funktionalitätsorientierten Akquisitionen neu und baut auf der Literatur zur Produktentwicklung, Produktqualität und Marketing auf. Die Konsequenzen des Framework werden mit Hilfe der Theorie zur strategischen Entscheidungsfindung erforscht. Die empirische Untersuchung der Dichotomie von performanz- und funktionalitäts-orientierten Akquisitionen erfolgt zuerst qualitativ und dann quantitativ. In beiden Fällen werden Firmen aus dem Bereich der Informations- und Kommunikationstechnik (IKT) betrachtet. Die qualitative Forschung basiert auf 21 Interviews mit erfahrenen Käufern anderer Unternehmen sowie gekauften und eigenständigen Startups. Das Ziel ist es dabei, performanz- und funktionalitäts-orientierte Akquisitionen zu charakterisieren und die Kernmechanismen, die die Entscheidungsfindung in Akquisitionen treiben aufzudecken. Die quantitative Analyse nutzt eine Stichprobe von 215 Unternehmensakquisitionen, bei denen die Kaufziele im Bereich der künstlichen Intelligenz aktiv sind — einer Subindustrie von IKT. Das Ziel der quantitativen Untersuchung ist es die vorgeschlagenen Implikationen des Frameworks von performanz- und funktionalitäts-orientierten Akquisitionen in Bezug auf

|| 1 EMC wurde im Jahr 2016 von Dell übernommen und in Dell EMC umbenannt; siehe https://en.wikipedia.org/wiki/Dell_EMC (Zugriff 17.02.2017).

XX | Zusammenfassung

die Entscheidungsfindung in Akquisitionen zu testen. So soll eine Verallgemeinerung der Ergebnisse ermöglicht werden. Die vorliegende Forschungsarbeit liefert zwei Kernergebnisse in Bezug auf die Entscheidungen zu Kaufzeitpunkt und Kaufpreis. Zum einen finden performanzorientierte Akquisitionen früher im Lebenszyklus eines Kaufziels statt als funktionalitäts-orientierte. Zum anderen ist der Kaufpreis von performanz-orientierte Akquisitionen bei vergleichbarem Reifegrad der Kaufziele höher als der von funktionalitätsorientierten. Diese Muster in der Entscheidungsfindung werden durch unterschiedliche Bewertung der Risiko- und Unsicherheitsniveaus von performanz- und funktionalitäts-orientierten Akquisitionen als Hauptmechanismus getrieben. Die Untersuchung von performanz- und funktionalitäts-orientierten Akquisitionen liefert wichtige Erkenntnisse für Theorie und Praxis. Beide Arten von Akquisitionen stellen bisher nicht untersuchte strategische Motivationen für Unternehmensübernahmen dar. Dies ist relevant für die Bewertung von Erfolg und Misserfolg von Akquisitionen, da die genaue Motivation eines Unternehmenskaufs die Auswahl von geeigneten Maßzahlen zur Erfolgsbewertung und – vor allem – Kaufpreis und -zeitpunkt beeinflusst. Die vorliegende Forschung erweitert das Verständnis von Entscheidungen bei Unternehmenskäufen, indem sie die wesentliche Rolle von in der Produktentwicklung vorherrschenden Risiken und Unsicherheiten auf Akquisitionen überträgt und hervorhebt. Die Unterscheidung zwischen performanz- und funktionalitäts-orientierten Akquisitionen ist auch im Rahmen von technologieorientierten Unternehmenskäufen als Ausprägung von Märkten für Technologie relevant. Die Ergebnisse einer Untersuchung solcher Märkte sind möglicherweise hinsichtlich der genannten Unterscheidung von Akquisitionstypen zu differenzieren. Eine wesentliche Implikation für die Praxis bezieht sich auf die Dynamiken zwischen etablierten Unternehmen in der gleichen Industrie. Durch Berücksichtigung des Frameworks von performanz- und funktionalitäts-orientierten Akquisitionen und dessen Auswirkungen auf Entscheidungen können Unternehmen das Verhalten ihrer Wettbewerber besser antizipieren und gegebenenfalls strategischen Akquisitionsschachzügen zuvorkommen. Für Eigner von Unternehmen, die als mögliches Übernahmeziel in Frage kommen, bietet das Framework eine verbesserte Grundlage zur Evaluation strategischer Optionen wie die einer Akquisition oder dem Verbleib als eigenständige Unternehmung.

Abstract In 2012 large multinational software company and serial acquirer EMC2 bought two companies for their technology, XtremIO and Syncplicity. At the time of acquisition XtremIO was younger than Syncplicity, did not have any customers and no product on the market. In contrast, Syncplicity did have tens of thousands of customers and a marketable product. Why would EMC make vastly different acquisition timing decisions by buying firms with largely different maturity profiles? EMC’s seemingly curious acquisition behavior motivates this thesis to identify underlying patterns in technology-focused acquisitions. It proposes the framework of performance- and functionality-focused acquisitions. In performance-focused acquisitions, the target’s technology improves the performance of an acquirer’s product. A functionalityfocused acquisition adds new product functionality. The objective of the present thesis is to gain an in-depth understanding of how this distinction influences the decisions of acquisition timing and deal value, i.e., the price paid by an acquirer. From a theoretical viewpoint, the framework of performance- and functionalityfocused acquisitions is novel and builds upon the literature streams of product development, product quality, and marketing. The investigation of this framework assumes the perspective of strategic decision making. The empirical scrutiny of the dichotomy of performance- and functionalityfocused acquisitions follows a mixed-methods approach that combines qualitative and quantitative research. Both studies focus on firms from the information and communications technology (ICT) industry. The qualitative investigation is built upon 21 interviews with serial acquirers and acquired as well as non-acquired startups. The objective is to characterize performance- and functionality-focused acquisitions and uncover the key mechanisms that drive acquisition decision making. The quantitative analysis draws from an extensive dataset of 215 acquisitions of targets that are active in the artificial intelligence industry—a sub-industry of ICT. The goal is to test the proposed implications of the framework of performance- and functionality-focused acquisitions on acquisition decision making and enable generalization. This research has two major results with respect to the decisions of acquisition timing and deal value. First, performance-focused acquisitions occur earlier in a target’s life cycle than functionality-focused ones. Second, deal value is higher for performance-focused acquisitions than functionality-focused ones at a comparable level of target maturity at the time of acquisition. This thesis finds that the evalua-

|| 2 EMC has been acquired by Dell in 2016. The new entity is now called Dell EMC; see https://en.wikipedia.org/wiki/Dell_EMC (accessed 17.02.2017).

XXII | Abstract

tion of different levels of risk and uncertainty is a key mechanism affecting these decision making patterns. There are important implications for both theory and practice. The dichotomy of performance- and functionality-focused acquisitions is a previously unrecognized distinction in strategic acquisition motives. This is relevant for assessing acquisition success or failure because acquisition motives should qualify the selection of acquisition performance measures. The present study extends the understanding of acquisition decision making highlighting the prominent role of risk and uncertainty that stems from product development and market entry. The distinction of performance- and functionality-focused acquisitions may also bear relevance to technology-focused acquisitions as an instance of a market for technology. The study of such markets may need to be conditioned on the distinction between both types of acquisitions. As a practical implication, industry incumbents may better anticipate and possible pre-empt competitor acquisition behavior taking the framework of performance- and functionality-focused acquisitions into account. This framework provides the shareholders of likely acquisition targets with an improved foundation for evaluating strategic options regarding an acquisition or a continued presence as a standalone entity.

1 Introduction 1.1 Motivation—Understanding technology-focused acquisitions as an instance of a market for technology It seems like the future is now when looking at some of the spectacular innovations that recent technological advances in the ICT industry have enabled. Siri, the virtual assistant on Apple’s iPhone, or Google’s self-driving car have or are predicted to have an enormous impact on society (Bertoncello and Wee, 2015; Brauer and Barth, 2014). Commonly these innovations are felt to be an expression of the enormous creativity, innovative power and far-sightedness of the companies associated with them. Quite surprisingly, these two innovations have in common that they originated within small startups that have been acquired for continuing the development of their technology within the larger firm. Apple acquired Siri, a spin-off of well-known SRI3 in 2010 for about $200 million4. Google acquired a startup called 510 Systems in 2011 to fuel Google’s plans of developing a self-driving car.5 These two cases are examples of much broader themes, namely that of open innovation, markets for technology and the division of labor in R&D. Open innovation refers to the paradigm that acknowledges the “wide diffusion” of knowledge (Chesbrough, 2003) and therefore places ideas and innovations that come from outside the company on an equal footing as those that emerge from within a company (Chesbrough, 2003). Hence, firms “should use external as well as internal ideas [...] as they look to advance their technology” (Chesbrough, 2006). A slightly different perspective on the phenomenon of sourcing external knowledge, for internal purposes is provided by the paradigm of “markets for technology”. A market for technology is comprised of “transactions for the use, diffusion, and creation of technology” (Arora et al., 2001) between various market participants or players such as firms or universities (Arora et al., 2001). Examples of transactions on a markets for technology are strategic R&D alliances via joint ventures or partnerships, spin-offs, corporate venture capital, technology licensing, outsourcing and acquisitions of entire firms for the purpose of technology sourcing (Arora and Gambardella, 2010; Arora et al., 2001; Arora et al., 1999). The key benefit of markets for technology lies in the division of labor in terms of its application to R&D across firm boundaries. This is the case when one market participant develops the technology while another one is responsible for its commercialization (Arora et al., 1999). || 3 Formerly Stanford Research Institute 4 https://techcrunch.com/2010/04/28/apple-siri-200-million/ (accessed 30.01.2017) 5 http://spectrum.ieee.org/robotics/artificial-intelligence/the-unknown-startup-that-builtgoogles-first-selfdriving-car (accessed 30.01.2017)

DOI 10.1515/9783110562095-001

2 | Introduction

The objective of this thesis is to gain a better understanding of technologyfocused acquisitions—also as an instance of markets for technology. A deeper understanding is warranted because technology-focused acquisitions are subject to large heterogeneity in acquisition behavior that may or may not be detrimental to the functioning of the market. Heterogeneity clouds the understanding of technology-focused acquisitions as an expression of a market for technology. In 2012 multi-national storage company and serial acquirer EMC bought two companies, XtremIO and Syncplicity for the purpose of getting access to their technologies. XtremIO is a manufacturer of all-flash storage arrays and Syncplicity a provider of a cloud file-sharing service similar to the well-known Dropbox application. At the time of the acquisition, XtremIO was rather early in its life cycle. It had neither a finished product nor any customers or revenue and its age was roughly three years. Syncplicity, on the other hand, was much more advanced in its life cycle. It already boasted more than 50,000 business customers and was 5 years old at the time of acquisition. The reasons for these two acquisitions were entirely different. With the technology purchase of XtremIO, EMC aimed at enhancing “speed, performance and flexibility in the datacenter”6 while the acquisition of Syncplicity “deepens [EMC’s] mobile collaboration solution set”7. XtremIO’s technology had a better performance than EMC’s existing technology. Syncplicity’s technology, in contrast, adds a whole new set of functionalities to EMC’s product portfolio. Therefore, both cases differ not only with respect to acquisition timing in terms of target maturity but also with respect to the plans associated with the acquired technology. EMC is a serial acquirer with more than 60 acquisitions between 2005 and 2014, highly structured and standardized acquisition routines and a clear focus on learning (Tanriverdi and Du, 2011). Therefore, immediately the question arises why EMC would acquire companies at such largely different stages in their life cycle. Acquiring rather immature firms for their technology comes with considerable risks but also benefits (Granstrand and Sjölander, 1990; Ransbotham and Mitra, 2010). Are the maturity differences in the acquisitions of XtremIO and Syncplicity simply deviations from routines (Mayer and Kenney, 2004)? Is the opposite true, i.e., are they systematic and potentially related to the motives of the acquisition? In this thesis, I introduce the distinction between performance- and functionality-focused acquisitions as a key concept describing two motives associated with a technology-focused acquisition. In a performance-focused acquisition the chief objective is to acquire technology that leads to a performance improvement for customers. The acquisition of XtremIO falls into this category because of its focus on

|| 6 http://www.zdnet.com/article/emc-acquires-xtremio-flash-storage-in-the-datacenter (accessed Jan., 30th 2017) 7 http://www.esg-global.com/blog/emc-acquires-syncplicity-deepens-mobile-collaborationsolution-set (accessed Jan., 30th 2017)

Research objective and design | 3

“speed, performance and flexibility”. A functionality-focused acquisition aims at adding a new functionality to an existing product or product portfolio. This is the case with EMC’s acquisition of Syncplicity. Thus, if the correlation between performance- and functionality-focused acquisitions and an acquisition target’s maturity at the time of acquisition is beyond mere chance, some heterogeneity in technologyfocused acquisitions may be explained. I do this by developing and testing new theory within this thesis that has the distinction between performance- and functionality-focused acquisitions at its heart. In summary, markets for technology are an important phenomenon for which technology-focused acquisitions of firms are one instance with particular importance. There is a large amount of unexplained heterogeneity within technologyfocused acquisitions as exemplified by EMC’s acquisitions of XtremIO and Syncplicity. This example suggests a correlation between the maturity of acquisition targets at the time of acquisition and the acquisition motive in terms of a performance- and functionality-focused acquisition. The development of new theory is necessary to shed light on this correlation and create a better understanding of the mechanisms that govern technology-focused acquisitions. This, in turn, may facilitate a more differentiated view of markets for technology.

1.2 Research objective and design This dissertation focuses on technology-focused acquisitions of firms as an instance of a market for technology. Choosing technology-focused acquisition as a study object is warranted in the context of markets for technology for two reasons. First, extant literature on markets for technology chiefly deals with licensing (Arora et al., 1999; Arora et al., 2001; Arora and Fosfuri, 2003; Caves et al., 1983; Kani and Motohashi, 2012; Ziedonis, 2004; Gambardella and Giarratana, 2013). Thus, studying technology-focused acquisitions in this thesis will help with addressing this gap. Second, within technology-focused acquisitions considerable heterogeneity exists. This heterogeneity likely hinders the study of the functioning and possible imperfections of technology-focused acquisitions as a market for technology (Arora et al., 1999; Arora et al., 2001; Arora and Gambardella, 2010; Caves et al., 1983; Gans and Stern, 2010). Hence, the resolution of some of this heterogeneity will likely prove to be beneficial and be a starting point for further investigations. The example given in section 1.1 on the two acquisitions conducted by EMC in 2012 suggests that there are systematic differences regarding the life cycle of a target at the time of acquisition and the underlying motive of the acquisitions in terms of technology type, i.e., improving product performance or adding new functionality. Hence, as a prerequisite for explaining how technology-focused acquisitions function as a market for technology, it is necessary to build theory that resolves the heterogeneity within technology-focused acquisitions. In addition, this theory shall

4 | Introduction

inform the management of technology-focused acquisitions. This is important because the failure rates in technology-focused acquisitions remain to be high (Bannert and Tschirky, 2004; Kitching, 1967; Chakrabarti and Souder, 1987; Hitt and Tyler, 1991). In order to study the differences in technology-focused acquisitions described above a decision making perspective is warranted. The heterogeneity in relation to EMC’s choice of XtremIO as a young target and Syncplicity as a comparatively older target is an outcome of the decision of acquisition timing. Indeed acquirers face the choice of acquiring now or later (Alvarez and Stenbacka, 2006; Brueller et al., 2015; Ransbotham and Mitra, 2010; Toxvaerd, 2008). An important element of acquisition decision making is the strategic rationale or idea provided for an acquisition endeavor (Haspeslagh and Jemison, 1991b) so that it needs to be included in the analysis of the heterogeneity presented. Looking again at the example of EMC the acquisitions of XtremIO and Syncplicity were both driven by acquiring new technology as a strategic rationale. However, looking one level deeper, the acquisition of XtremIO was product performance-focused while that of Syncplicity was product functionality-focused. For a complete picture, it is therefore necessary to use the distinction between these two technology types, i.e., performance- and functionality-focus as a structuring element when studying acquisition decision making in an attempt to explain the observed heterogeneity. The framework of performance- vs. functionality-focused acquisitions is grounded in innovation and product development literature and corresponds to the transfer of the concept of different quality criteria (Garvin, 1984) or development gaps in product development to technology-focused acquisitions. In this context, I intend to achieve three research objectives following an empirical approach: – Find and study empirical evidence for the framework of performance- and functionality-focused acquisitions. – Build theory by deriving mechanisms and hypotheses on key decisions within the acquisition decision making process that are assumed to be at the root of the observed heterogeneity. – Test these hypotheses to determine whether there is empirical evidence for the proposed theory and potential for generalization. This is expected to inform the management of technology-focused acquisitions and also bring light into the functioning of markets for technology based on acquisitions of firms To address the above research objectives this research project follows a mixed methods approach combining qualitative and quantitative data collection and analysis (Edmondson and McManus, 2007; Jick, 1979; Creswell and Plano Clark, 2011). The generation of theory to explain the heterogeneity evident in technology-focused acquisitions can be classified as nascent theory research (Edmondson and McManus, 2007) as the concept of performance- and functionality-focused acquisitions is novel. However, note that the endeavor is supported by the largely separate

Summary of results | 5

but rather mature literature streams on technology-focused acquisitions, decision making in acquisitions and product development/innovation management and—to a lesser extent—marketing. In such a context, a mixed methods approach is suitable (Edmondson and McManus, 2007) and, in addition, the combination of qualitative and quantitative methods is useful because the result can be expected to have higher validity and a higher level of confidence is achieved (Jick, 1979). Merging qualitative and quantitative methods enables triangulation (Jick, 1979; Yin, 2003) and thereby a “more complete, holistic, and contextual portrayal” of the phenomenon studied (Jick, 1979). Following a mixed method approach, this study can benefit from the advantages of each methodology individually. A qualitative approach is appropriate for building or testing theory and is especially useful for addressing research questions of why and how (Eisenhardt, 1989). This thesis uses a qualitative methodology for developing theory that attempts to discover how the framework of performance- and functionality-focused acquisitions influences acquisition decision making and to uncover the mechanisms for why a specific outcome of acquisition decision making occurs. Complementing the qualitative research with a quantitative approach will enhance validity (Jick, 1979) and provide additional methodological rigor (Edmondson and McManus, 2007). Direct testing of hypotheses using established mathematical procedures will improve generalizability and robustness if the hypotheses find support. Both qualitative and quantitative research in this thesis are fueled with data on acquisitions in the ICT industry. The qualitative study encompasses both serial acquirers and founders of acquired as well as non-acquired startups in the ICT industry with a focus on software to gain a deep understanding of the applicability of the framework of performance- and functionality-focused acquisitions as well as on acquisition decision making. Serial acquirers are interesting due to their routinized acquisition behavior (Brueller et al., 2015). Startups were selected with the goal in mind of finding rich instances of performance- and functionality-focused acquisitions. The sample used for the quantitative study is focuses on artificial intelligence software—a sub-industry of ICT. Hence, a technology perspective is taken which is warranted given the technology-centricity of the distinction of performance- and functionality-focused acquisitions.

1.3 Summary of results This thesis introduces and defines the novel and important distinction of performance- and functionality-focused acquisitions as an important dimension along which acquisitions aimed at sourcing technology externally (see e.g., Ahuja and Katila (2001)) can be categorized. This distinction is based on the concepts of performance- and functionality-focused innovations that has its roots in the literature

6 | Introduction

streams of product development (Ulrich and Eppinger, 2016), product quality (Garvin, 1984) and marketing (Zhou and Nakamoto, 2007; Zhang and Markman, 1998). This thesis provides evidence for the close relation between product development and technology-focused acquisitions (Chaudhuri et al., 2005), elaborates on relevant differences, and establishes the aim of improving product performance or adding product functionality as an acquisition’s strategic rationale or underlying motive. Taking the perspective of strategic decision making, performance- and functionality-focused acquisitions differ with respect to the temporal evolution of risk and uncertainty over an acquisition target’s life cycle viewed through a potential acquirer’s eyes. Overall, risk and uncertainty are lower for performance-focused acquisitions than functionality-focused ones. Separating risk and uncertainty into market and technology components, market risk is in general higher in functionality-focused acquisitions. This is because customer fit of functionality-focused acquisitions is less clear in relation to an acquirer’s existing customers than it is for performance-focused ones. Technology risk and uncertainty are on average somewhat higher in performance-focused acquisitions because a certain level of product performance is difficult to achieve. While this would lead to higher uncertainty, product performance is measurable, lowering risk and uncertainty. Slightly higher technology risk and uncertainty of performance-focused acquisitions do not compensate the larger differential in market risk and uncertainty. Both market and technology risk and uncertainty decrease when a potential acquisition target matures (Ransbotham and Mitra, 2010) regardless of the distinction of performance- and functionality-focused acquisitions. At some maturity level risk and uncertainty, differences between the targets of potential performance- and functionality-focused acquisitions will eventually have vanished. Risk and uncertainty differences provide the key mechanism that leads to different decision making with respect to performance- and functionality-focused acquisitions. The distinction between both types of acquisitions has important implications on acquisition timing and deal value decisions (see e.g., Brueller et al. (2015), Ransbotham and Mitra (2010), Warner (2006)). Acquisition timing is understood in terms of the target’s life cycle phase at the time of acquisition (Brueller et al., 2015). This thesis finds that performance-focused acquisitions occur earlier in a target’s life cycle than functionality-focused ones. The underlying reasoning is that performance-focused acquisitions fall below the risk and uncertainty threshold for an acquisition at an earlier life cycle stage. With respect to deal value, this thesis shows that at comparable levels of target maturity, performance-focused acquisitions have a higher deal value than their functionality-focused counterparts. This is due to lower risk and uncertainty levels of performance-focused acquisitions leading to a reduced risk and uncertainty discount (Ransbotham and Mitra, 2010; Koeplin et al., 2000).

Structure of the dissertation | 7

In summary, the present dissertation introduces the dichotomy of performanceand functionality-focused acquisitions, provides a first characterization and finds important implications of performance- and functionality-focused acquisitions on managerial decision making.

1.4 Structure of the dissertation This dissertation is structured as follows. Chapter 2 is comprised of literature review covering theoretical foundations that the remainder of this thesis is built upon. It starts with a review of the literature on technology-focused acquisitions (section 2.1) detailing aspects such as the process of technology-focused acquisitions and persisting issues. Section 2.2 elaborates on the topic of strategic decision making within technology-focused acquisitions. In this context the decision, making process in technology-focused acquisitions is explained and acquisition timing and deal value are presented as key decisions. Chapter 3 has two aims. First, the framework of performance- and functionalityfocused acquisitions is to be defined and characterized. Second, hypotheses on acquisition decisions that incorporate the framework are to be generated. The structure of this chapter follows these two aims. Section 3.1 provides a review of the core concepts of product development. In section 3.2 the concept of performance- and functionality-focused innovations is defined and grounded in extant literature. This concept is then transferred to acquisitions so that performance- and functionalityfocused acquisitions can be defined (see section 3.3). The derivation of hypotheses on acquisition timing and deal value follows in section 3.4. Theoretical findings are then summarized and discussed in section 3.5 Chapter 4 focuses on the qualitative analysis. Section 4.1 presents the research methodology including the research design, sampling and data collection approaches as well as arguments for the robustness of their choice. The description of the qualitative results in section 4.2 provides evidence for performance- and functionality-focused acquisitions, characterizes them with respect to risk and uncertainty and provides insights into the decision on acquisition timing and deal value. Thereby this section answers the question why the observed heterogeneity within technology-focused acquisitions is present and how it came about. The chapter concludes with a discussion of the qualitative results (section 4.3) in relation to the theory developed in chapters 2 and 3. Testing the hypotheses on a large dataset of acquisitions is the main aim of chapter 5. In analogy to chapter 4, the research methodology is described first in section 5.1. The quantitative analysis takes a technology perspective focusing on artificial intelligence software as a sub-industry of ICT. Dependent and independent variables and controls are presented in section 5.2. Descriptive results are provided in section 5.3. Hypothesis tests using various econometric methods and the descrip-

8 | Introduction

tion of their results follow in section 5.4. Section 5.5 discusses the results of hypothesis testing against the background of the theory developed in chapters 2 and 3 and in relation to the quantitative study presented in chapter 4. The final part of this thesis comprises the conclusion, contributions and an outlook in chapter 6. First, contributions to theory are described and discussed in section 6.1. Then implications to practice are provided in section 6.2. An outlook (section 6.3) outlines potential directions of future work.

2 Theoretical foundations—Technology-focused acquisitions and strategic decisions In this chapter, I will summarize the theoretical foundations on technology-focused acquisitions and decision making within technology-focused acquisitions. These are a necessary pre-requisite for developing a deeper understanding of performanceand functionality-focused acquisitions that are the core topic of this thesis. Within the context of technology-focused acquisitions (section 2.1) I first discuss their definition and characteristics (section 2.1.1), then explain the process of technologyfocused acquisitions (section 2.1.2) and finally elaborate on some key issues that are commonly associated with them (section 2.1.3). Section 2.2 continues with a discussion of decision making in technology-focused acquisitions. First key insights of the literature on strategic decision making in general are provided in section 2.2.1. Then acquisitions are described through the lens of a decision making process in section 2.2.2. While section 2.2.3 discusses the key decisions that drive technology-focused acquisitions, section 2.2.4 highlights the role of the acquisition timing and deal value decisions. They are especially relevant for understanding performance- and functionality-focused acquisitions.

2.1 Technology-focused acquisitions 2.1.1 Definition and characteristics of technology-focused acquisitions Mergers and acquisitions (M&A) are transactions on the market for corporate control and describe the phenomenon of two firms joining for a common purpose (Sudarsanam, 2003). Following the resource-based view of the firm (RBV) an acquisition has the objective of gaining a competitive advantage through accessing relevant assets and resources to use them more efficiently and achieve superior performance within the combined entity, i.e., achieve synergies (Ahuja and Katila, 2001; Anand and Singh, 1997; Angwin, 2007; Capron et al., 1998; Haspeslagh and Jemison, 1991b). To achieve a competitive advantage these assets need to be rare, valuable, and difficult to imitate or substitute (Barney, 1986; Carow et al., 2004; Conner, 1991; Wernerfelt, 1984). This view is complemented by the industrial organization literature that stresses gaining market share and improving productivity through M&A (Caves, 1998; McGuckin and Nguyen, 1995). The redeployment of resources in acquisitions is joined by the benefit of achieving a higher level of control with respect to the environment that the acquiring firm operates in (Burt, 1980; Finkelstein, 1997; Pfeffer, 1972). A significant advantage of acquisitions over growing assets organically is the fact that these assets are available with a speed advantage (Chaudhuri and Tabrizi, 1999; Puranam and Srikanth, 2007; Ransbotham

DOI 10.1515/9783110562095-002

10 | Theoretical foundations—Technology-focused acquisitions and strategic decisions

and Mitra, 2010). This is especially relevant in the fast-paced high technology industries (Chaudhuri, 2005). Beyond the rather general level of asset reconfiguration and control as means for achieving a competitive advantage, the literature recognizes a number of strategic rationales for engaging in M&A. Bower (2001) establishes a typology of the following five motives for acquisitions that are not sharply defined, though: consolidation to mitigate overcapacity, geographic roll-up, market or product expansion, substitution for R&D, and industry convergence. Several authors (Cloodt et al., 2006; Galpin and Herndon, 2007; Kusstatscher and Cooper, 2005; Ranft and Lord, 2002; Sudarsanam, 2003; Trautwein, 1990; Weber et al., 2013; Napier, 1989) each mention similar reasons for acquisitions with or without establishing their own typology. Angwin (2007) expands the list of “classical” motivations and adds especially “underrecognised motivations” such as exploration or ownership and imposed motivations such as reacting to customer/supplier pressure or competitor actions to the list (see Angwin (2007) for a full account). The literature also recognizes motives without a clear foundation in value creation such as empire building or managerial hubris (Kusstatscher and Cooper, 2005; Roll, 1986) as reasons for M&A. Some acquisitions—aptly called acqui-hires—are specifically aimed at hiring teams (Coyle and Polsky, 2013). Generally, multiple motives are involved (Graebner et al., 2010; Angwin, 2007). Acquisitions may even be opportunity driven, e.g., when buying undervalued firms (Angwin, 2007; Wernerfelt, 1984) or have been shown to follow “revealed” patterns such as search for “superiority” and “inferiority” in terms of the target’s innovation performance (Desyllas and Hughes, 2009). Technology-focused acquisitions8 fall somewhat in-between the categories “substitute for R&D” and “product expansion” proposed by Bower (2001). Acquirers’ objectives associated with technology-focused acquisitions are to add new products that may be at a very early development stage or obtain novel technologies and capabilities for their own product development efforts (Chaudhuri, 2005; Grimpe and Hussinger, 2009; Birkinshaw et al., 2000; Graebner and Eisenhardt, 2004; Graebner et al., 2010; Ranft and Lord, 2002; Bannert and Tschirky, 2004). The notion of filling product portfolio “gaps” or “holes” is mentioned frequently in relation to technology-focused acquisitions (Ding and Eliashberg, 2002; Chaudhuri and Tabrizi, 1999; Helfat and Lieberman, 2002; Mayer and Kenney, 2004; Danzon et al., 2007). The term “substitute for R&D” maybe somewhat misleading, though, as research has shown that internal R&D and external technology sourcing are indeed best understood as complements (Cassiman and Veugelers, 2006; Granstrand et al., 1992). Based on these findings, it becomes obvious that there is a close connection

|| 8 Note that “technology-focused” acquisitions may also be referred to as “innovation-targeted”, “technology-driven”, or “technology-oriented”. This thesis uses “technology-focused” as the default term for referring to the phenomenon at hand.

Technology-focused acquisitions | 11

between product development and technology-focused acquisitions (Chaudhuri, 2005; Higgins and Rodriguez, 2006; Ransbotham and Mitra, 2010). The novel concept of performance- and functionality-focused acquisitions that is introduced in this thesis builds upon this connection. Performance- and functionality-focused acquisitions represent a distinction within technology-focused acquisitions while the notions of performance improvement and functionality addition are rooted in product development. I will explore the relationship between technology-focused acquisitions and product development with a focus on the distinction of performance- and functionality-focused acquisitions more closely in chapter 3. There is no universal definition of a technology-focused acquisition. In several articles Puranam et al. (Puranam et al., 2006; Puranam and Srikanth, 2007; Puranam et al., 2009) provide an “operational” definition of a technology-focused acquisition describing it as large established firms acquiring small technologybased firms for their technology and capabilities (Doz, 1987; Graebner and Eisenhardt, 2004; Granstrand and Sjölander, 1990; Ranft and Lord, 2002). In other words, a technology-focused acquisition corresponds to gaining access to a technology that is packaged in a small firm (Granstrand and Sjölander, 1990). An important characteristic of technology-focused acquisitions is that a target’s technical capabilities are usually based upon difficult to measure, complex, and socially embedded knowledge (Coff, 1999). This tacitness of knowledge poses a great challenge for its transfer (Graebner 2010). Combining this fact with the possibility that an acquisition target’s innovations are at a very early stage in their life cycle, clearly shows that technology-focused acquisitions involve a high degree uncertainty, especially in relation to the market and the technology (Graebner 2010). Technology-focused acquisitions are a phenomenon of rising importance, especially in the fast-paced high technology industries (Ahuja and Katila, 2001; Chakrabarti et al., 1994; Benou and Madura, 2005; Lodh and Battaggion, 2015; Schön and Pyka, 2013). There are three key drivers for this prominence of technology-focused acquisitions. These are increasingly faster product life-cycles (Gerpott, 1995; Bannert and Tschirky, 2004), insufficiency of internal R&D due to diseconomies of time compression (Dierickx and Cool, 1989) and path dependency (Kogut and Zander, 1992; Nelson and Winter, 1982), and difficulties of externally purchasing technologies via means other than acquisition of firms (Lehto and Lehtoranta, 2004; Ranft and Lord, 2002; Zhao et al., 2005; Dierickx and Cool, 1989; Chaudhuri, 2005; Mendelson and Pillai, 1999; Makri et al., 2010). Indeed, technology-focused acquisitions appear to be a solution to all three problems as they promise to provide fast access to new products and technologies (Puranam and Srikanth, 2007; Chaudhuri and Tabrizi, 1999). Some companies, such as Cisco or EMC, serially acquire small firms for their technology to quickly satisfy market demands (Bower, 2001; Byrne and Elgin, 2002; Paulson, 2001; Li, 2009; Brueller et al., 2015) or even transform themselves and their value proposition entirely (Tanriverdi and Du, 2011).

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Thereby, they avoid time-consuming, uncertain, and prone-to-path-dependency internal development processes or at least attenuate their negative qualities (Puranam et al., 2006). Modes of accessing technology externally other than acquisitions such as alliances or joint ventures (Graebner et al., 2010; Ford and Mortara, 2012; Vanhaverbeke et al., 2002), licensing (Arora and Gambardella, 2010; Arora and Fosfuri, 2003) or simply hiring employees (Ranft and Lord, 2002) may not be suitable when capabilities and technology is rooted in dynamic, tacit knowledge of teams or when exclusivity and close coordination are key (Ranft and Lord, 2002; Kale and Puranam, 2004). In summary, technology-focused acquisitions are one specific albeit important category within the overall field of M&A. Technology-focused acquisitions have a close relation to product development. In regards to this connection, the novel concept of performance- and functionality-focused acquisitions introduced by this thesis becomes important. A practical and common definition of technology-focused acquisitions is that of large, established firms acquiring small firms in order to get access to their technology. The transferred knowledge and skills are usually tacit, socially embedded and highly context specific. Drivers of technology-focused acquisitions are shortening product life cycles, insufficiency of internal R&D and the impracticability of other technology sourcing modes.

2.1.2 Process of technology-focused acquisitions The study of the acquisition process is relevant for two reasons. First, it helps to gain a better understanding of the characteristics and challenges of acquisitions including those with a technology focus. Second, the acquisition process itself has been shown to be a significant driver of acquisition success or failure (Jemison and Sitkin, 1986). In the context of this thesis, an in-depth look at the acquisition process is crucial for resolving some of the observed heterogeneity in technology-focused acquisitions that motivates this dissertation. From a traditional perspective, the acquisition process consists of several sequential and non-overlapping phases (Angwin et al., 2015; Haspeslagh and Jemison, 1991b) with a clear focus on financial valuation and determining strategic fit (Haspeslagh and Jemison, 1991b). In contrast to this, the process perspective holds that these phases—while individually important—have considerable interactions and require consideration from a more holistic point of view (Haspeslagh and Jemison, 1991b; Weber et al., 2013). Extant literature proposes anything from three (Weber et al., 2013; Kusstatscher and Cooper, 2005; Meckl, 2004), four (Tanriverdi and Du, 2011), five (Sudarsanam, 2003), six (Galpin and Herndon, 2007) to seven phases (Haspeslagh and Jemison, 1991b; Angwin et al., 2015) when describing the M&A process. This thesis proposes a simplified view on the M&A process consisting of three phases that each subsume several sub-phases. These three phases are (1)

Technology-focused acquisitions | 13

strategic planning, (2) pre-acquisition, and (3) post-acquisition. The strategic planning phase takes place internally at the acquirer and mainly deals with the questions of strategic rationale, make-or-buy and sourcing mode (Chaudhuri and Tabrizi, 1999; Makri et al., 2010). The pre-acquisition phase encompasses all steps from searching for potential targets to reaching an acquisition agreement with one selected target (Weber et al., 2013; Galpin and Herndon, 2007). Quite naturally, the post-acquisition phase incorporates the integration of the acquired company and potential learning-oriented feedback and motivation mechanisms (Weber et al., 2013; Galpin and Herndon, 2007). In the following, each phase is described in more detail while concentrating on technology-focused acquisitions and their idiosyncrasies. (1) Strategic planning phase The expected outcome of the strategic planning phase is the strategic rationale for an acquisition such as improving or adding products, services or technologies (Weber et al., 2013; Galpin and Herndon, 2007). To this end the acquirer analyzes the same product roadmaps that are used for internal product development by the business units and searches for gaps in terms of products, technologies or capabilities (Chaudhuri and Tabrizi, 1999; Helfat and Lieberman, 2002). Building upon this analyses the acquirer decides for the appropriate sourcing mode weighing in-house development against acquisition following criteria such as speed of development (Chaudhuri and Tabrizi, 1999). If a strategic case for external sourcing via acquisition can be made, the formulation of the strategic rationale is enriched with specific acquisition criteria (Weber et al., 2013; Galpin and Herndon, 2007; Chaudhuri and Tabrizi, 1999). These include for example business performance and cost requirements (Chaudhuri and Tabrizi, 1999) or broadly defined target characteristics such as size and technology (Weber et al., 2013; Angwin et al., 2015). (2) Pre-acquisition phase The expected outcome of the pre-acquisition phase is an agreement with an acquisition target that fits the requirements derived from the strategic rationale and some additional constraints (Galpin and Herndon, 2007; Weber et al., 2013). This outcome is the result of a number of steps, i.e., search, screening and selection of targets, strategic and financial evaluation of targets, deal negotiations, target due diligence and finally the definition of the deal terms (Weber et al., 2013; Haspeslagh and Jemison, 1991b; Galpin and Herndon, 2007). The last step includes settling on the purchase price or acquisition deal value that may be determined as a price per employee in technology-focused acquisitions (Galpin and Herndon, 2007; Mayer and

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Kenney, 2004). Within these steps strategic, organizational and cultural fit as well as the target’s prior performance and acquisition resource needs9 are evaluated (Pablo, 1996; Tanriverdi and Du, 2011; Angwin et al., 2015). In technology-focused acquisitions special attention is paid to the physical and intellectual property and to the target’s product development team, specifically their “goals and aspirations [and their] ability to function as a part of a much larger firm” (Mayer and Kenney, 2004). (3) Post-acquisition phase The aspired outcome of the post-acquisition phase is in the short term an appropriately integrated acquisition target and in the long term the commercialization of the acquired technologies (Weber et al., 2013; Puranam et al., 2006). An appropriate integration is of crucial importance because the value of the acquisition hinges to a considerable extent on it (Larsson and Finkelstein, 1999; Inkpen et al., 2000; Pablo, 1994; Kitching, 1967; Zollo and Singh, 2004; Paruchuri et al., 2006). The appropriateness of the integration approach is a function of its extent, which falls inbetween the two extremes of autonomy and full integration, and its speed (Weber et al., 2013; Tanriverdi and Du, 2011; Bauer and Matzler, 2014). Decisions on extent and speed of integration are tailored to the specific acquisition situation (Galpin and Herndon, 2007) and interact with the strategic rationale for the acquisition and target characteristics (Tanriverdi and Du, 2011; Risberg, 2003; Napier, 1989). Dimensions touched upon by the post-merger integration are the target’s processes, people, technology and systems (Galpin and Herndon, 2007). Retention and continued motivation of key people is especially important in technology-focused acquisitions (Chaudhuri and Tabrizi, 1999) due to the tacitness and social embeddedness of knowledge and capabilities. Some general characteristics of the technology-focused acquisition process are noteworthy. Overall technology managers play a key role in the process (James et al., 1998). Detection of acquisition opportunities is highly driven by business units (Tanriverdi and Du, 2011; Mayer and Kenney, 2004) while business development and in-house engineers are tasked with the nuts and bolts of due diligence where engineers test the target’s technology and interview target technical staff (Chaudhuri and Tabrizi, 1999). According to the literature the typical duration of the entire process is on average 10 months on a range of 1 month to 5 years (Angwin et

|| 9 Acquisition resource needs refer especially to the “future financial and human resources required to realize the acquisition’s potential benefits” (Pablo, 1996).

Technology-focused acquisitions | 15

al., 2015). Recent anecdotal evidence suggests, however, that these durations have shortened considerably10. Based on the descriptions given above I draw two conclusions. First, there is an intimate relationship between technology-focused acquisitions and product development and decision making. These two aspects shall be explored in chapter 3. Second, the strategic rationale of the deal is a key driver in each phase. Hence, I expect that the distinction between performance- and functionality-focused acquisitions has a large impact on the specific outcomes of each phase. This is in line with the notion that “different types of acquisitions may need to be conceptualized and managed differently” (Bower, 2001). In conclusion, the acquisition process can be structured into roughly three phases. In technology-focused acquisitions there is a strong connection to the acquirer’s product development process across all phases that is maintained by the close involvement of business units and in-house R&D. The strategic deal rationale is a key driver of each phase so that the distinction of performance- vs. functionality-focused acquisitions is expected to be of high relevance.

2.1.3 Key issues in technology-focused acquisitions In the light of the enormous acquisition activities with or without a technology focus, one of the key questions is whether they generate value. In fact, the evidence paints a rather grim looking picture. Depending on the study, roughly between 40% and 90% of all acquisitions fail (Bauer and Matzler, 2014; Bagchi and Rao, 1992; Christensen et al., 2011; Kitching, 1974; Jensen and Ruback, 1983; Hunt, 1990; Jarrell and Poulsen, 1989; Cartwright and Schoenberg, 2006; Schoenberg, 2006). These results have been relatively constant over the last 40 years (Angwin, 2007). Similarly, technologically motivated acquisitions frequently do not live up to expectations (Chaudhuri and Tabrizi, 1999; Puranam and Srikanth, 2007; Steensma and Corley, 2000; Bannert and Tschirky, 2004) with failure rates of up to 80% (Puranam and Srikanth, 2007). Cisco’s CEO has once estimated that 90% of technology acquisitions fail (Graebner et al., 2010). In conjunction with the question of value creation, acquisition research has been focused on two related sub-issues, i.e., the measurement of acquisition performance and the drivers of success or failure (Larsson and Finkelstein, 1999; Angwin, 2007; Ahuja and Katila, 2001; Bannert and Tschirky, 2004; Léger and Quach, 2009; Makri et al., 2010; Schoenberg, 2006). Traditional performance

|| 10 Facebook is said to have closed its $1 billion Instagram acquisition within 24h. (http://www.businessinsider.com/confirmed-instagram-closed-a-50-million-financing-at-a-500million-valuation-before-it-was-acquired-by-facebook-2012-4 accessed 07.02.2017)

16 | Theoretical foundations—Technology-focused acquisitions and strategic decisions

measures for M&A success are accounting-based or event studies of stock returns (Larsson and Finkelstein, 1999; King et al., 2004). However, the literature recognizes also a number of different measurement approaches such as synergy realization (Larsson and Finkelstein, 1999) or more subjective measures (Meglio, 2009). In technology-focused acquisitions measures related to innovative success such as patenting frequencies (Ahuja and Katila, 2001) or the number of new product introductions (Puranam et al., 2006) appear to be more suitable, however. In terms of drivers of acquisition success or failure, the literature focuses on the effects of different integration approaches (Birkinshaw et al., 2000; Lindholm Dahlstrand, 2000; Gerpott, 1995; King et al., 2008; Paruchuri et al., 2006; Puranam and Srikanth, 2007), target selection and the pre-acquisition buyer-target relationship (Graebner et al., 2010), overpayment (Galpin and Herndon, 2007), non-value focused activities such as empire building (Hughes et al., 2003) or, more generally, absorptive capacity and the not invented here (NIH) syndrome (Cohen and Levinthal, 1990; Afuah, 2003). Picking the right integration approach has been highlighted as crucial in technology-focused acquisitions due to acquirer-target information asymmetries (Graebner et al., 2010). A notable result is that of the inverted U-shape describing the relationship between innovative performance and relatedness of acquirer target knowledge bases (Cloodt et al., 2006). Despite ongoing research on performance measurements and drivers of acquisition success the paradox of record acquisition activity combined with high failure rates remains unsolved (Angwin, 2007). A growing number of authors stress that in evaluating success or failure the underlying acquisition strategic rationale and the acquired resources need to be considered (Angwin, 2007; Bannert and Tschirky, 2004; Léger and Quach, 2009; Ranft and Lord, 2002). Hence, I conclude that measuring acquisition success in terms of performanceor functionality-focused acquisitions addresses this issue as this framework combines the aspects of strategic rationale and the type of acquired technology as a resource. While this thesis does not deal explicitly with acquisition performance measurement, the connection of performance- and functionality-focused acquisitions to acquisition performance measurement provides another motivation for the scrutiny of this framework. Indeed, there is first evidence establishing this link (Nagel, 2016). In summary, research results agree that most acquisitions including those with a technology-focus are prone to fail. Concentrating on performance measures and drivers of failure, innovation output appears to be a suitable performance measure and the integration phase seems to be especially crucial in technology-focused acquisitions. The impact of acquisition rationale and the type of resource acquired are understudied factors both of which the framework of performance- and functionality-focused acquisitions proposed in this thesis is expected to shed light on.

Strategic decision making in technology-focused acquisitions | 17

2.2 Strategic decision making in technology-focused acquisitions 2.2.1 Strategic decision making Strategic decisions are highly relevant as they influence the future direction of the firm. Therefore strategic decision making is a key activity in organizations despite their relative infrequency and non-programmed nature (Soelberg, 1967; Eisenhardt and Zbaracki, 1992). Strategic decisions are commonly characterized via the terms “novelty, complexity and open-endedness” (Mintzberg et al., 1976) and defined as “important, in terms of the actions taken, the resources committed, or the precedents set” (Mintzberg et al., 1976). Hence, strategic decisions are typically the responsibility of a firm’s key executives. The literature recognizes them as boundedly rational (Eisenhardt and Zbaracki, 1992) and highlights that merely a “manager’s eventual perception of the situation” together with her values form the basis of a strategic decision (Hambrick and Mason, 1984). Triggers for strategic decisions range from the highly regular such as organizational planning processes to the very sporadic such as opportunism, external pressures, or crises (Hambrick and Snow, 1977; Mintzberg et al., 1976; Cyert and March, 1963). From the perspective of the firm, strategic decisions are derived from strategic problems that can roughly be categorized as issues regarding the product-market domain, technologies that cater to that particular domain, or the creation of organizational structures (Hambrick and Snow, 1977; Miles et al., 1974). It is in the nature of such strategic problems that their description is unclear and they are beset by uncertainty and ambiguity. The definition of suitable criteria for the evaluation of decision alternatives is very challenging (Schwenk, 1984). The rational decision making model proposes that decisions start with a predetermined objective that sets the standard by which alternative solutions to the decision problem are judged. Once sufficient information on alternative solutions are collected, the optimal one is chosen (Eisenhardt and Zbaracki, 1992; Allison, 1971). Noting the limitations of this model, Mintzberg et al. (1976) characterize the decision making process by three phases, identification, development, and selection, where selection is usually performed iteratively zooming in on alternative solutions. These phases are relatively constant across the work of different authors (Schwenk, 1984; Mintzberg et al., 1976; Mazzolini, 1981). The information used within each phase represent what is perceived by management or in the words of Weick (1969) “decision-making [...] means selecting some interpretation of the world and some set of extrapolations form that interpretation”. Building upon this point, Hambrick and Snow (1977) conclude that an organization’s strategy influences a decision maker’s perceptions clouding a full appreciation of some facts. It is commonly accepted that decision makers typically take shortcuts via the use of heuristics during the decision making process (Eisenhardt and Zbaracki,

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1992; Mintzberg et al., 1976; Janczak and Thompson, 2005). Important in this context is the concept of satisficing (Simon, 1956; Simon, 1979). Satisficing refers to the practice of decision makers evaluating an alternative against an aspiration level or threshold and selecting the first alternative that exceeds the aspiration level (Simon, 1979). Satisficing has been frequently opposed to maximizing (Mintzberg et al., 1976), i.e., deciding for the optimal alternative solution (Janczak and Thompson, 2005). Soelberg (1967) proposes a more complex model where maximizing is used for primary goals and satisficing for secondary constraints in a comparison of solution alternatives (Soelberg, 1967; Mintzberg et al., 1976). Somewhat related is the decision making concept of “elimination by aspects” (Tversky, 1972) whereby decision makers eliminate alternatives if they do not rise above a threshold for certain criteria (March, 1994). Two important factors that inform the decision making process are risk and uncertainty (March, 1988; Schwenk, 1984). Strategic changes are risky by nature so that risk is at the heart of strategic decision making (Greve, 1998). Generally, risk is defined as the variance of the probability distribution of the utility of outcomes for a chosen alternative solution (March and Shapira, 1987). Risk is closely related to uncertainty. March and Shapira (1987) define risk as “the probabilistic uncertainty of outcomes stemming from a choice”, while Sitkin and Pablo (1992) refer to risk as “a characteristic of decisions that is defined […] as the extent to which there is uncertainty about whether potentially significant and/or disappointing outcomes of decisions will be realized.”11 According to the president of an electronics firm “risk taking is synonymous with decision making under uncertainty” (March and Shapira, 1987). Risk behavior in decision making is determined by a manager’s risk propensity and her perceptions of risk (Sitkin and Pablo, 1992). As risk is usually confused with “downside risk” (March and Shapira, 1987; Yoe, 2012) I primarily use the term uncertainty instead of risk as a decision making criterion12. According to Lipshitz and Strauss (1997) there are three ways of dealing with uncertainty, i.e., reduction, acknowledgment, and suppression. Uncertainty reduction can be achieved, e.g., via gathering additional information, delaying a decision, or forestalling (Lipshitz and Strauss, 1997; Hirst and Schweitzer, 1990). In summary, strategic decision making is important because it shapes the future of the firm. The strategic decision making process is usually subdivided into three phases, identification, development and selection. Across all phases, only information as perceived by the decision maker enter the process. Quite commonly, decisions are made following the concepts of satisficing or elimination by aspects where

|| 11 In yet another definition of risk, Yoe (2012) describes risk as “a measure of the probability and consequence of uncertain future events”. 12 In chapter 4 the term “risk” is used instead of “uncertainty” to reflect the interviewees’ preference of the term. Chapters 5 and 6 use “risk” and “uncertainty” in combination.

Strategic decision making in technology-focused acquisitions | 19

solution alternatives need to meet a specific aspiration level or threshold. Risk and uncertainty are key criteria in strategic decision making, as they are inherent elements of strategic change. The concepts explained in this section are fundamental to developing an understanding of the differences between performance- and functionality-focused acquisitions. Therefore, the heterogeneity within the acquisition behavior of EMC with respect to performance- and functionality-focused acquisitions (see section 1.1) can be resolved by taking a decision making perspective.

2.2.2 Acquisitions as a decision making process As explained in the previous section the salient attributes of strategic decisions are their importance as well as their “novelty, complexity, and open-endedness” (Mintzberg et al., 1976). This characterization applies well to acquisition decisions as they do not simply consist of the allocation of resources but instead have a tremendous impact potential on firm performance (Wally and Baum, 1994; Pablo, 1996). In addition an acquisition decision is inherently complex (Duhaime and Schwenk, 1985) involving considerations on many levels such as strategy, organization and culture and each decision typically differs tremendously from the previous one (Zollo, 2009). Without doubt, acquisition decisions are open-ended. As acquisitions encompass strategic decisions with potentially a high impact it is not surprising that an increasing number of scholars have chosen to study these decisions by focusing on the acquisition decision making process—the so called process perspective of acquisitions (Angwin et al., 2015; Jemison and Sitkin, 1986; Haspeslagh and Jemison, 1991a; Haspeslagh and Jemison, 1991b; Pablo, 1994; Pablo, 1996). The process perspective posits that acquisition success rests to a significant extent on the proper decision making that leads there (Haspeslagh and Jemison, 1991a; Haspeslagh and Jemison, 1991b; Pablo, 1996). Pablo (1996) differentiates the content of acquisitions decisions from the acquisition process and proposes that content imposes an upper limit on acquisition success while process determines the extent to which an acquisition realizes this limit. As decisions and the process leading to them are intimately linked, the acquisition process perspective suggests conceptualizing acquisitions “as a series of decision processes that have a cascading influence throughout the various stages of the acquisition event” (Pablo, 1996). This view is expected to advance researchers’ understanding of the mechanisms shaping acquisition activities and their outcomes (Pablo, 1994; Pablo, 1996; Duhaime and Schwenk, 1985; Haspeslagh and Jemison, 1991b; Jemison and Sitkin, 1986). The process perspective of acquisition decision making highlights the cardinal role of risk and uncertainty in this context (Haspeslagh and Jemison, 1991b; Haspeslagh and Jemison, 1991a; Pablo, 1996; DePamphilis, 2012). The role of risk is exacerbated in acquisition decision making as opposed to other strategic decisions

20 | Theoretical foundations—Technology-focused acquisitions and strategic decisions

such as internal investments due to a unique blend of strategic, organizational and cultural challenges with issues such as information asymmetry and high outcome uncertainty (Haspeslagh and Jemison, 1991b; Pablo, 1996). For example, Pablo (1996) posits that decision maker risk propensities and perceptions combined with target risk characteristics are crucial for understanding the underlying mechanisms of the process leading to a decision on the level of integration. Specifically in the context of technology-focused acquisitions the concepts of technology and market uncertainty are highly relevant for decision making because both types of uncertainty are linked to innovation management and product development (Chaudhuri, 2005) for which a technology-focused acquisition is one ingredient. Especially technology uncertainty is driven by the difficulties associated with evaluating causally ambiguous, context-dependent and tacit knowledge and capabilities inherent in technology-focused acquisitions (James et al., 1998). Technology and market uncertainty are both cases of outcome uncertainty connecting them to risk in decision making (Sitkin and Pablo, 1992). Abstracting from technologyfocused acquisitions as providing fuel for product development and looking at uncertainties associated with an acquisition in general, quality uncertainty, transferability uncertainty, and synergy uncertainty emerge as relevant factors (Coff, 1999). Based on this discussion of acquisition decision making I conclude that the heterogeneity observed with respect to the acquisition of XtremIO and Syncplicty by EMC and their distinction in terms of a performance- or functionality-focus needs to be studied from an acquisition decision making process perspective. The key reason is that the process perspective generates insights into the mechanisms that drive decision making outcomes. Assuming that the different maturity profiles of XtremIO and Syncplicity at the time of acquisition are not the result of pure randomness, they must have been the result of a more or less conscious decision. Quite obviously, the risky nature of acquisition decisions and especially market and technology uncertainty as antecedents of risk in technology-focused acquisitions shall play a key role in the study of these mechanisms. In summary, acquisitions clearly involve strategic decisions so that the study of acquisition decision making processes has emerged as a relevant field. Especially within technology-focused acquisitions, market and technology uncertainty play an important role with respect to decision making. Assuming a decision making perspective likely facilitates a better understanding of an acquirer’s heterogeneous behavior with respect to performance- and functionality-focused acquisitions.

Strategic decision making in technology-focused acquisitions | 21

2.2.3 Key decisions in technology-focused acquisitions 1 Phase Strat. plan. Key (1a) Makedecior-buy sions Out- Make-or-buy come rationale

2 Pre-acquisition (2a) Selection of target

(2b) Timing of (2c) Deal acquisition value

Acquisition agreement with suitable target based on proper timing and deal value

3 Post-acquisition (3a) Degree (3b) Speed of integration of integration Integration of target (short term) and financial gains from acquired products/ technologies (long term)

Fig. 1: Phases, key decisions, and aspired outcomes in technology-focused acquisitions

As stated in the previous section, the acquisition process can be conceptualized through a series of sub-decisions that each have an influence on the outcome of the subsequent one (Pablo, 1996). In this section, I will shortly give an overview of the key decisions in the acquisition process and then focus on the decisions of timing and deal value. The literature recognizes six major decisions within the acquisition process. Following the description in section 2.2.2, I group them according to the three phases of the acquisition process as shown in figure 1. The key decision in the strategic planning phase (1) is that of “make-or-buy” (1a), i.e., whether a technology or capability gap should be addressed internally or via acquisition as an external mode of sourcing (Helfat and Lieberman, 2002). The decisions in the pre-acquisition phase (2) are target selection (2a) (Pablo, 1996; Kaul and Wu, 2016; Desyllas and Hughes, 2009; Paulson, 2001; Yu et al., 2016; Capron and Shen, 2007; Stellner, 2015), acquisition timing (2b) (Brueller et al., 2015; Carow et al., 2004; Ransbotham and Mitra, 2010; Warner, 2006; Toxvaerd, 2008; Alvarez and Stenbacka, 2006), and deal value (2c) (Coff, 1999; Alvarez and Stenbacka, 2006; Brueller et al., 2015; Grimpe and Hussinger, 2007; Ransbotham and Mitra, 2010; Toxvaerd, 2008)13. Regarding the post-acquisition (3) phase an acquirer decides on extent of integration (3a) (Bauer and Matzler, 2014; James et al., 1998; Puranam et al., 2009; Bannert and Tschirky, 2004; Birkinshaw et al., 2000) and speed of integration (3b) (Angwin, 2004; Bauer and Matzler, 2014; Homburg and Bucerius, 2005; Homburg and Bucerius, 2006; Ranft and Lord, 2002). Acquisition decisions do not necessarily take place in that particular phase in that order but are rather circular (Angwin et al., 2015). Neverthe|| 13 The choice of deal structure, i.e., the proportion of the purchase price that the acquirer pays in cash vs. stock is also a relevant decision in the context of acquisition decision making (Dittmar et al., 2012). I argue, however, that it is related to but not on the same level as the decision on deal value so that it is not considered here further.

22 | Theoretical foundations—Technology-focused acquisitions and strategic decisions

less the proposed grouping seems most natural because the decisions within one phase limit the options space of the decisions in the subsequent phase (Pablo, 1996). The decisions in each phase are highly strategic. The make or buy decision (1a) is crucial in terms of where resources are to be allocated and driven, inter alia, by the nature of the internal capability gap and the competitive environment (Makri et al., 2010; Kurokawa, 1997). The remaining decisions, i.e., target selection, acquisition timing, deal value, and extent and speed of integration have all been shown to influence acquisition and acquirer performance either directly or indirectly (Capron and Shen, 2007; Brueller et al., 2015; Carow et al., 2004; Ransbotham and Mitra, 2010; Chaudhuri, 2005; Coff, 1999; Bauer and Matzler, 2014; Stahl and Voigt, 2008; Pablo, 1994; Angwin, 2004; Homburg and Bucerius, 2006). According to the literature key drivers of decisions and their outcomes in the pre- and post-acquisition phase are, inter alia, strategic fit (Larsson and Finkelstein, 1999; Kaul and Wu, 2016), organizational fit (Datta, 1991), cultural fit (Bauer and Matzler, 2014; Stahl and Voigt, 2008), environmental factors (Haleblian et al., 2009) and the acquisition process itself (Jemison and Sitkin, 1986). The key underlying driver of the acquisition decisions is the strategic rationale for the deal (Haspeslagh and Jemison, 1991a; Yu et al., 2016). Therefore, this thesis is centered on acquisitions with either a performance- or functionality-focus as strategic rationales that influence the decision making process and thereby decision outcomes.

2.2.4 Relevance of acquisition timing and deal value decisions The acquisition decisions that have been studied most prominently from a process perspective are make-or-buy, target selection and the extent of integration (Pablo, 1994). This thesis focuses on the decisions of acquisition timing and deal value as these are expected to provide most insights in resolving the heterogeneity regarding technology type in terms of performance- and functionality-focused acquisitions. Recall that in the example of the two acquisitions by EMC (see section 1.1), XtremIO, the target acquired for performance reasons, was rather at the beginning of its life cycle while Syncplicity as the target of a functionality-focused acquisition was further advanced. This is clearly an issue of acquisition timing. I investigate deal value decisions in conjunction with acquisition timing decisions for two reasons. First, both decisions are interlinked and second, a joint study may uncover additional differences in decision making concerning performance- or functionality-focused acquisitions. The study of acquisition timing—acquiring earlier or later (Ransbotham and Mitra, 2010)—is relevant both from a theoretical and practical perspective. According to Shi et al. (2012) “strategy scholars often view time as a hidden and unrecognized dimension of strategy that has the potential to create competitive advantage”.

Strategic decision making in technology-focused acquisitions | 23

Nevertheless, temporal aspects in the context of acquisition management have received only little attention in the literature so that our understanding is still limited (Shi et al., 2012; Ransbotham and Mitra, 2010). In their literature review on the temporal perspective of M&A, Shi et al. (2012) identified only seven empirical studies considering acquisition timing as a key variable14. From a practical perspective the decision of acquisition timing is relevant because acquirer performance and acquisition success are contingent on acquisition timing in terms of target maturity (Brueller et al., 2015; Granstrand and Sjölander, 1990; Mayer and Kenney, 2004). Acquisition timing has been studied mainly with respect to merger waves (Andonova et al., 2013; Doan et al., 2016; Popli and Sinha, 2013; Harford, 2005; Ahern and Harford, 2014; McNamara et al., 2008; Lambrecht, 2004; Carow et al., 2004; Toxvaerd, 2008), or optimal timing from a theoretical perspective using real options (Thijssen, 2008; Dong and Iihara, 2014; Pereira and Rodrigues, 2015; Alvarez and Stenbacka, 2006). Other authors focus on acquisition timing in terms of firm-level triggers for seeking an acquisition target (Iyer and Miller, 2008) or an acquirer (Graebner and Eisenhardt, 2004). Studies dealing with target maturity at the time of acquisition operationalized via target age show that the rate of acquirer new product introduction decreases with target age (Puranam et al., 2006), revenues gained from acquisitions become more immediate and financial performance improves (Chaudhuri et al., 2005), and acquirer abnormal returns decline (Ransbotham and Mitra, 2010). Acquisition timing has also been studied relative to the market cycle (Kusewitt, 1985) and industry standard setting (Warner, 2006). Only few authors take an actual decision making perspective with respect to acquisition timing (see e.g., Alvarez and Stenbacka (2006)). This thesis defines acquisition timing in terms of its relation to the target life cycle at the time of acquisition. This is logical because the observed heterogeneity that motivates this thesis stems from acquisition target life cycle differences. In the context of this definition, the question emerges whether target life cycle is simply another attribute within the target selection decision. I argue that it is not the case for two reasons. First, there is no clear temporal order in acquisition decisions as earlier decisions are frequently revisited (Angwin et al., 2015). Hence, the timing decision might take place before target selection or in close interaction with it. Second, even if target selection included target life cycle as one decision criterion, buyers always have the option of acquiring quickly or waiting (Ransbotham and Mitra, 2010; Toxvaerd, 2008) to directly affect timing. The determination of deal value (pay more or less) is an important outcome of the acquisition decision making process (Haspeslagh and Jemison, 1991a; Straub et al., 2012; Chaudhuri, 2005). From a practical perspective, choosing the proper deal value is difficult due to a high risk of overpaying that is caused, e.g., by the “win-

|| 14 Out of 144 studies dealing with the concept of time in M&A

24 | Theoretical foundations—Technology-focused acquisitions and strategic decisions

ner’s curse” (Varaiya and Ferris, 1987; Kagel and Levin, 1986). A high acquisition premium may have a negative impact on acquisition performance (Datta, 1991; Haunschild, 1994; Hayward and Hambrick, 1997). A deal value that is too high puts pressure on post-merger integration efforts to recapture lost value (Krishnan et al., 2007). The riskiness of deciding for a certain deal value is exacerbated in comparison to internal resource allocation decisions due to limited reversibility of acquisitions (Alvarez and Stenbacka, 2006; Toxvaerd, 2008). In high-technology industries, the study of deal value is especially relevant from both a practical and theoretical point of view. The risk of overbidding is high because of large uncertainties regarding an assessment of the target’s knowledgebased assets (Coff, 1999). In his study of related vs. unrelated acquirers of targets in knowledge intensive industries, Coff (1999) discovered that related acquirers employ compensating mechanisms such as offering lower bid premia, while unrelated acquirers do not. From a theoretical perspective, the profit through acquisitions— that is materially influenced by the deal value—is not well understood in hightechnology environments, because here innovation as opposed to diversification is the objective (Ransbotham and Mitra, 2010). This thesis studies acquisition timing and deal value in unison because both decisions are closely interlinked (Ransbotham and Mitra, 2010; Alvarez and Stenbacka, 2006; Toxvaerd, 2008; Brueller et al., 2015). Timing through its impact on target maturity and the likelihood of success or failure of post-merger integration influence deal value (Brueller et al., 2015). In addition, the trade-off between the technological flexibility and the uncertainty in the valuation of early stage targets (Ransbotham and Mitra, 2010) drives both potential overbidding and acquisition timing. In summary, the decisions of timing and deal value are relevant in the context of technology-focused acquisitions both from a theoretical and practical perspective. Acquisition timing has hitherto received little attention in the literature despite its practical relevance in connection to strategy and acquisition performance and success. Theoretical understanding of the choice of deal value is especially limited in high-technology industries and—from a practical viewpoint—has important implications on acquisition integration and performance. Both decisions are interrelated so that studying them in combination is warranted. I expect that the scrutiny of both decisions will provide important insights into the phenomenon of performance- and functionality-focused acquisitions.

3 Performance- and functionality-focus in product development and acquisitions Technology-focused acquisitions and product development are directly connected through the “make-or-buy”-decision (Cassiman and Veugelers, 2006; Higgins and Rodriguez, 2006), especially so in high technology industries (Ransbotham and Mitra, 2010). Thus, it is not only highly instructive but also logical to study technology-focused acquisitions in the broader context of product development (Chaudhuri, 2005). In the subsequent sections, I will show that some relevant ideas, concepts and phenomena stressed in the product development literature are readily transferrable to technology-focused acquisitions. Concretely, in section 3.1 I will—as a foundation for the subsequent sections—provide a short introduction to some core concepts and findings of the product development literature. Then in section 3.2— coming back to core topic of this thesis—performance and functionality in the context of product development are discussed. In section 3.3 this differentiation is then carried over to technology-focused acquisitions and discussed in detail leading to a natural emergence of the dichotomy of performance- and functionality-focused acquisitions. Section 3.4 derives hypotheses on acquisition timing and deal value in the context of performance- and functionality-focused acquisitions. Finally, in section 3.5 the results of theory development are summarized and discussed.

3.1 Core concepts of product development and the relevance of uncertainty Product development is a key source of competitive advantage and critical for success and renewal of the firm (Brown and Eisenhardt, 1995; Wheelwright and Clark, 1992). It not only enables catering to established markets but also helps open up entirely new ones (Tidd et al., 2003). Conversely, firms that do not invest in product development are likely to fail or at least seriously fall behind in their current markets due to intensifying levels of international competition, fragmented, demanding customers as well as heterogeneous and fast technological change (Wheelwright and Clark, 1992). Overall strategic advantages are numerous (see Tidd et al. (2003) for an extensive list). Product development refers to the development of new technologies or the (re-)combination of existing technologies and their introduction to the commercial market to serve user needs (Utterback and Abernathy, 1975; Fleming, 2001) or quite simply to “changes in things (products/services) which an organization offers” (Tidd et al., 2003). An important distinction within product development is that of assembled vs. non-assembled products (Utterback, 1996). Examples for nonassembled products are window glass panes or paint consisting of only few materi-

DOI 10.1515/9783110562095-003

26 | Performance- and functionality-focus in product development and acquisitions

als (Utterback, 1996). In contrast, assembled products such as automobiles or computers are made up of individual components (Utterback, 1996; Krishnan and Ulrich, 2001). This feature of assembled products is relevant because it enables product development efforts and thereby innovation to take place either on the component level as opposed to the system or architectural level or both (Afuah, 2003; Tidd et al., 2003; Henderson and Clark, 1990; Ulrich and Eppinger, 2016). This thesis focuses on assembled products though some of its conclusions may be applicable to non-assembled products, as well. Conventionally, product development is described to occur in distinct, sequential phases following, e.g., the stage gate model (MacCormack et al., 2001; Cooper, 1990; Wheelwright and Clark, 1992; Ulrich and Eppinger, 2016) or the waterfall model in software development (MacCormack, 2001). A simplified version of the stage gate model distinguishes the concept development, product planning, product/process engineering and pilot production/ramp-up stages (Wheelwright and Clark, 1992). Even preceding these phases are the steps of customer need identification and target specification establishment (Ulrich and Eppinger, 2016). Proceeding from one stage to another, development projects have to pass a “gate” that encompasses a careful evaluation of quality criteria (Cooper, 1990). Several authors (MacCormack et al., 2001; Bhattacharya et al., 1998a; Iansiti, 1995; Bhattacharya et al., 1998b) have criticized this model as unsuitable for turbulent and uncertain environments such as the internet-software industry. They propose a flexible approach to product development consisting of frequent iterative cycles of customer/user feedback, product adaptation and testing (Eisenhardt and Tabrizi, 1995). Product development is impossible when it is not accompanied by some form of process development. Process development refers to the creation or enhancement of a production process, i.e., the “system of process equipment, work force, task specifications, material inputs, work and information flows, etc. that are employed to produce a product or service” (Utterback and Abernathy, 1975). Examining the temporal interplay between product and process innovation, Utterback and Abernathy (1975) developed a model of the dynamics of innovation which hypothesizes that the rates of major product and process innovation are contingent on the current life cycle phase (fluid, transitional and specific) of an industry. In the beginning, the rate of product innovation surpasses that of process innovations. The emergence of a dominant design, i.e., a product class that “the market settles on”, marks the transition from competition based on product innovation to process innovation (Utterback, 1996; Afuah, 2003; Teece, 1986; Tushman and Anderson, 1986). The literature on product development emphasizes the importance of uncertainty both in the development process and from an industry dynamics perspective. While a number of types of uncertainty have been distinguished (Fleming, 2001; Rosenberg, 1996; Wheelwright and Clark, 1992; Jalonen, 2012), technology and market uncertainty stand out as the two most prominent ones (Chaudhuri et al., 2005; Chaudhuri, 2005; Krishnan and Ulrich, 2001; Iansiti, 1995; MacCormack and

Product performance and functionality in product development | 27

Verganti, 2003; MacMillan and McGrath, 2002; Tidd et al., 2003; Afuah, 2003). Technology uncertainty stems from questions related to technological barriers (Will it work? Will it scale? Will aspired product performance be achieved?), availability of development equipment or platform uncertainty, i.e., the necessity of design changes (MacCormack and Verganti, 2003; Huchzermeier and Loch, 2001). Key sources of market uncertainty are, for example, market acceptance/customer requirements, demand, or competitive reaction (MacMillan and McGrath, 2002; McGrath and MacMillan, 2000; MacCormack and Verganti, 2003). From the perspective of this thesis market and technology uncertainty are crucial concepts because of their direct impact on decision making in product development, e.g., in relation to the decision of which one of several unproven technologies should be employed in a product (Krishnan and Ulrich, 2001). Product development forms the foundation of technology-focused acquisitions. It is highly relevant in the context of performanceand functionality-focused acquisitions because both types of acquisitions cater to specific product development and ultimately customer needs as I will show in the following sections. In addition, the uncertainties present in product development are expected to “carry over” to technologies gained from technology-focused acquisitions. In conclusion, product development is a key driver of competitive advantage with considerable strategic benefits. It is based on the development of new technologies or the (re-)combination of existing ones. This thesis focuses on assembled products, i.e., those that consist of individual components or modules. Product development often occurs in stages though flexible models are equally relevant. It is usually accompanied by process development. From an industry perspective, the emergence of the dominant design represents an important milestone because it freezes basic properties of products. Product development is characterized by high uncertainty of which market and technology uncertainty are most prominent. Product development is directly connected to the distinction between performance- and functionality-focused acquisitions.

3.2 Product performance and functionality in product development 3.2.1 Definition of product performance- and product functionality-focused innovations Success of a product on the market is to a large extent determined by its attributes (Brown and Eisenhardt, 1995; Cooper and Kleinschmidt, 1987; Thölke et al., 2001). According to Brown and Eisenhardt (1995), “successful products [provide] superior customer value through enhanced technical performance, low cost, reliability, quality or uniqueness”. Some industries purely compete on the basis of product perfor-

28 | Performance- and functionality-focus in product development and acquisitions

mance as a product attribute (Friar, 1995; von Hippel, 1976; Foster, 1986). Looking at product success from a customer requirements perspective, products must satisfy a minimum quality threshold in its attributes (independent of price) to stimulate a customer’s consideration of purchase (Adner and Levinthal, 2001). Hence, ascertaining the proper product attributes is of such crucial importance for product development that techniques such as quality function deployment or the “house of quality” (Griffin and Hauser, 1993; Ramaswamy and Ulrich, 1993) have been devised that match customer needs with product attributes or—on a more detailed level— engineering characteristics. Due to their importance, setting targets for product attributes is clearly a conscious, strategic decision (Griffin and Hauser, 1993) within product development. Indeed, the degree to which aspiration levels of product attributes are realized is a measure of success of the product development process by itself (Nellore and Balachandra, 2001). The notions of product performance and product functionality are both strongly linked to product attributes via product quality (Garvin, 1987; Garvin, 1984; Kitchenham and Lawrence Pfleeger, 1996; Reeves and Bednar, 1994; Sebastianelli and Tamimi, 2002; Tamimi and Sebastianelli, 1996). Hence, both product performance and product functionality are clearly the result of strategic product development decisions. The existence of specific product attribute roadmaps on a corporate level (Albright and Kappel, 2003) or even on an industry level as it is the case for the semiconductor industry (ITRS, 2015) epitomizes this view. In his discussion of product quality, Garvin (1984) distinguishes five approaches to defining quality15. Across these approaches, he defines eight dimensions of product quality of which the two primary ones are “performance” and “functionality”16. Performance as a product quality dimension “refers to the primary operating characteristics of a product [...] for an automobile these would be traits like acceleration, handling, cruising speed [...] for a television set [...] sound and picture clarity [...]. Measurable product attributes are involved [...].” (Garvin, 1984). Functionality as a product quality dimension is characterized as “the ‘bells and whistles’ of products, those secondary characteristics that supplement the product’s basic functioning. Examples include [...] cotton cycles on a washing machine, and automatic tuners on a color television set” (Garvin, 1984). Please note, however, that this definition is oversimplified in the sense that it is hardly fitting in situations where there is no

|| 15 These are transcendent approach, product-based approach, user-based approach, manufacturing-based approach, and value-based approach (Garvin, 1984). 16 Garvin (1984) speaks of “features” instead of “functionality” in his article. The literature sometimes uses the term “features” in reference to performance dimensions as in “We use ‘features’ for the product dimensions on which innovation may occur. For example, a notebook computer’s features might include its weight, hard drive storage, screen brightness, etc.” (Paulson Gjerde et al., 2002). Therefore, this thesis refrains from using the term “feature” to avoid confusion and employs the term “functionality” in a consistent manner instead.

Product performance and functionality in product development | 29

clear distinction between secondary and primary characteristics. An example would be a modern smart phone where—depending on the user—the built-in camera would not classify as a secondary characteristic even though clearly being a functionality. Tab. 1: Example performance dimensions—primarily in relation to products in the ICT industry (not exhaustive)

Performance dimension Example Accuracy/precision

Particle simulation: # of significant digits in particle position

Bandwidth

Networking: Maximum data rate supported by interface

Density

Microprocessors: # of transistors per area

Effectiveness

Compression algorithm: Image file compression ratio

Efficiency

Data center: Energy consumption per server

Error rate

Visual recognition: Share of images identified incorrectly

Form factor

Hard drives: Physical size of disk

Jitter

Packet voice networks: Varying delay between packets

Latency

Messaging: Delay between sending and receiving a message

Relevance

Search algorithm: Relevance of search results

Reliability/availability

Datacenter: Downtime per year

Scalability

Cloud computing: Growth of resource need with # of users

Speed

Search algorithm: # of websites Speed scanned per second

Storage volume

Flash disks: Storage capacity

Throughput

Networking: Actual data rate supported by interface

Based on these definitions of product performance and product functionality from the perspective of product quality it is possible to establish the concepts of product performance-focused innovations and product functionality-focused innovations. Their definition is straightforward. A product performance-focused innovation refers to an innovation with the principal objective of improving one or several product performance dimensions of a product. A product functionality-focused innovation refers to an innovation whose primary goal is to add one or several novel functionalities to a product or product family. The term “focused” has been chosen deliberately to reflect the conscious, strategic nature of both types of innovations. To my knowledge this definition has not been made in such explicit terms in extant literature though it is easily derived from it. The names for both types of innovations are chosen such that they parallel the corresponding types of acquisitions. Paulson Gjerde et al. (2002) provide an example for both types of innovations. Related to a product performance-focused innovation of a scanner firm they write “[...] the company’s strategy [...] is to compete principally on the basis of perfor-

30 | Performance- and functionality-focus in product development and acquisitions

mance and quality of its products and services. In particular, R&D projects were aimed at improving the size, weight, reliability, quality, and readability of scanners at increased distances, faster speeds, and higher density codes.” A statement in the same source concerning Hewlett-Packard clearly describes a product functionalityfocused innovation: “Hewlett-Packard originally designed the HP85 to function solely as a personal computer, but discovered that its functionality could be expanded such that the HP85 could also be used as an equipment controller.” (Paulson Gjerde et al., 2002). Especially the first example underlines the strategic nature of performance-focused innovations by the use of the word “aim”17. Though the above definitions of product performance- and product functionality-focused innovations make sense intuitively, further elaboration is necessary for a more comprehensive understanding. Especially the term “product performance dimension” and the distinction between product performance and product functionality need more attention. I shall start with a discussion about product performance dimensions. In Garvin’s (1984) description of product performance as an element of quality the notion of measurability of product performance plays a central role. Measurability implies that a numerical value can be attached to a product performance dimension as in “a 100-watt light bulb provides greater candlepower (performance) than a 60-watt bulb” (Garvin, 1984). The term performance dimension corresponds to a “performance indicator” whose evolution follows an S-curve (Afuah, 2003; Utterback, 1996; Jones et al., 2001). Table 1 provides an incomplete list of product performance dimensions that can be the objective of a product performance-focused innovation18. Product performance dimensions bear resemblance to the engineering characteristics in the House of Quality (Ramaswamy and Ulrich, 1993). There are important differences, however. This thesis stresses that the improvement of a product performance dimension as the target of a product performance-focused innovation incorporates a user or customer perspective (Garvin, 1984). The enhancement needs to provide a change of product performance dimensions that the innovator expects to be noticeable from a user or customer perspective so that a certain user or customer can clearly distinguish one as “better” than the other potentially triggering a purchase decision19. From the viewpoint of engi-

|| 17 The second example reads more like a case of a serendipitous discovery than a strategic move. Note, however, that even such a chance discovery can become strategic. 18 Garvin (1984) lists reliability and durability as elements of quality that are separate from performance. As both reliability and durability are measurable, this thesis includes them in the set of product performance dimensions. 19 This statement implicitly contains an application perspective. The correct phrase would therefore be: “The enhancement needs to provide a change of product performance dimensions that the innovator expects to be noticeable from a user or customer perspective in a specific application [...].” An illustrative example would be that of a microscope that works sufficiently well for analyzing cellular structure of type A but that, due to a limited resolution, works not so well for finer struc-

Product performance and functionality in product development | 31

neering characteristics a per mill reduction of product weight could be seen as an improvement while an innovator would clearly not expect users/customers to notice it. Also the improvement of one engineering characteristic can simply be the result of mere design trade-offs (Ramaswamy and Ulrich, 1993) and therefore lacks the strategic quality of a product performance-focused innovation. In the following, I shall explore the distinction between product performance and product functionality more deeply. The marketing literature as well as the product development literature provide useful definitions of both product performance and product functionality. Studying early and late product market entrance Zhang and Markman (1998) introduce the distinction between alignable differences that are “comparable along the same dimension” and non-alignable differences that are unique for a certain product. Zhou and Nakamoto (2007) distinguish the introduction of enhanced features and unique features corresponding to product performance changes and product functionality additions, respectively. They state that enhanced features are characterized by “improved performance along existing attributes” and unique features “offer something that other brands lack” in their products. According to Ziamou and Ratneshwar (2003) product functionality “refer[s] to the opportunity for action that is afforded by a product” and product functionality-focused innovations “make a novel set of benefits available to the consumer”. Thölke et al. (2001) define product functionality as “each identifiable aspect of the total offering that a critical reference group perceives and evaluates as an ‘extra’ to a known standard among comparable products”. Garvin (1984) speaks of functionalities as “supplement[s] [to a] product’s basic functioning” implying that the most basic version of a product does not possess them. Product (family) roadmaps differentiate versions of products in terms of “full-featured” or “de-featured” (Albright and Kappel, 2003)20. The previous five descriptions of product functionality evoke the notion of a gap, i.e., one product having a functionality that the other does not have21. Hence, the definition of a product functionality-focused innovation speaks of adding new functionality as in filling a (functionality) gap. Consistent

|| tures of type B causing them to appear somewhat blurry. An improvement in the microscope's resolution as a product performance dimension would be barely perceivable and likely irrelevant for a scientist studying only cellular structure A but clearly noticeable for a scientist dealing solely with cellular structure B. Obviously, this difference is application dependent and could very well be the stimulus of a purchase decision. The phrase “[...] the innovator expects [...]” has been included to reflect the fact that there are firms such as medical diagnostic ultrasound manufacturers which continuously improve product performance even though customers do not notice the change (Friar, 1995). 20 The terms “full-featured” and “de-featured” potentially refer to both product functionality and product performance differences but do not dismiss a pure functionality-based interpretation. 21 Note that even though it is common to speak of a performance gap between products it is impossible that one product is completely devoid of performance while the other one is not.

32 | Performance- and functionality-focus in product development and acquisitions

with prior literature this thesis defines a functionality as “a way of doing something” that can potentially be a new or unique “thing”. This definition takes the perspective of a user/customer who, in her mind, compares a certain product with an imaginary version of itself that does not have the particular functionality. As mentioned above, this thesis emphasizes that a functionality difference needs to be noticeable from a user/customer perspective (at least expectedly so) to qualify as such. Depending on the industry and product context, another helpful perspective for distinguishing a product performance-focused innovation from a product functionality-focused innovation is that of inputs and outputs. In simplified terms, a product performance-focused innovation leads to a product that takes (roughly) the same inputs and produces (conceptionally) the same outputs at a better performance. In contrast, a product functionality-focused innovation takes different inputs to produce conceptionally the same or different outputs22. An easy heuristic for distinguishing two products along the concepts of performance and functionality is based on asking two questions. A product performance difference should give an answer to the question “How much better?” while a functionality difference should give an answer to “How many things can be done with one product but not the other?” The core idea here is that product performance deltas can be measured while product functionality differences need to be counted.

3.2.2 Challenges in delineating product performance- and product functionalityfocused innovations Garvin (1984) aptly notes that “the distinction between [performance and functionality] is primarily one of centrality or degree”. In many practical applications, the concepts of product performance- and product functionality-focused innovations are somewhat fuzzy and hard to demarcate properly. This is primarily based on four challenges. The first challenge is connected to the product development process and technology. A product performance-focused innovation may enable new functionalities and thereby be combined with a product functionality focused innovations. A faster facial recognition algorithm may enable real-time facial recognition in Google glass as a new functionality. The opposite case may be true as well. The development of a knob for tuning the colors of a TV screen as a new functionality might || 22 As an illustration for a product performance-focused innovation, take the case of the Google search engine. A performance improvement could be in terms of search speed or relevance of outputs. In the case of speed inputs and outputs are exactly the same. In the case of relevance, inputs are the same but outputs are only conceptionally the same. A functionality addition could be the introduction of the "I'm Feeling Lucky" button. The input changes as the user now clicks the “I'm Feeling Lucky” button and the outputs remain conceptionally the same, i.e., they are still a list of search results.

Product performance and functionality in product development | 33

result in an accompanying innovation leading to better TV screen color contrast and saturation regulation. Despite these examples, it can be the outright rationale of a firm to pursue both performance- and functionality-focused innovations for the same product simultaneously as exemplified by the advent of digital image processing in the film industry (Afuah, 2003). The second challenge is tied to semantics. If the objective of an innovation is to “improve functionality” one of three meanings is possible, i.e., “doing the same thing just at a better performance”, “doing a new thing” or “doing a new thing while also improving product performance”. Applying the concepts of this thesis, the first case would be a product performancefocused innovation, the second case a product functionality-focused innovation and the third case a mixture. The third challenge is based on the fact that new functionalities have some kind of performance by themselves. For example, the introduction of footstraps to a surfboard is a functionality-focused innovation. However, the footstraps are linked to performance dimensions via the durability of their mounting. The fourth challenge relates to the definition of a product performance-focused innovation that heavily rests on the concept of performance dimensions. In some cases this definition loses its usefulness. As an illustration, consider usability as a product performance dimension. Unfortunately, usability may be driven by two sources, namely product performance and product functionality. Specifically, a product can be evaluated as more usable on the grounds of improved speed as a performance dimensions or based on new functionality that adds convenience such as the tool-tips that appear when hovering the mouse pointer over certain GUI elements in a software program. There is no remedy to the first challenge. Hence, hybrid cases do exist and may at best be differentiated by the degree of importance of the underlying product performance- and functionality-focused innovations from a perspective of firm strategy. The second and third challenge may be resolved by in depth scrutiny concerning what is the actual motivation for an innovation acknowledging, however, that mixed cases do occur. The last challenge may be dealt with by not choosing equivocal product performance dimensions such as usability in an effort to identify a product performance-focused innovation. Instead, an adherence to more technology-oriented product performance dimensions is recommended. Due to these challenges of defining product performance and product functionality as objectives of innovation, it is not surprising that the literature often uses both terms or related terms in ways that can lead to confusion or appear to disagree with the definitions given above. Hence, as a reader, please be aware of this phenomenon and examine the literature with care23.

|| 23 For example Jones et al. (2001) list “product performance” and “market performance” under the category “firm performance measures”. In their study, product performance consists of seven elements, namely “overall product performance”, “number of unique product features”, “product durability/product life”, “how well the product functions”, “ease of product use”, “ease of product

34 | Performance- and functionality-focus in product development and acquisitions

3.2.3 Technology-level foundations of product performance- and product functionality-focused innovations After discussing the definitions of product performance- and product functionalityfocused innovations at length, it is instructive to examine the foundations and implications of both types of innovations on a technology level. I will show that both product performance- and product functionality-focused innovations can result in product innovation at a component and architectural level, and in process innovation. This knowledge is especially relevant when attempting to identify product performance- and product functionality-focused innovations. Recall from section 3.1 that assembled products are comprised of individual components or modules (Utterback, 1996; Krishnan and Ulrich, 2001). However, a product is not merely the sum of its components but is also defined through its architecture, i.e., “the ways [components] are integrated into the system” (Henderson and Clark, 1990; Afuah, 2003; Wheelwright and Clark, 1992). Product architecture assigns “functional elements […] to the physical building blocks of a product” (Ulrich and Eppinger, 2016). In the latter view, a product’s hierarchy can be expressed in terms of chunks that consist of a series of components and interfaces (Ulrich and Eppinger, 2016). Product performance is then defined as “how well a product implements its intended [functionalities]” (Ulrich and Eppinger, 2016). Two extremes can be distinguished with respect to a product’s architecture, i.e., a highly modular one and a highly integrated one. In the former case, one chunk implements exactly one functionality while in the latter case, functionalities are distributed across several chunks or one chunk incorporates multiple functionalities (Ulrich and Eppinger, 2016)24. The previous description of chunks enables an intuitive—yet simplified—way of relating product performance- and product functionality-focused innovations to a product’s technological and physical level. Assuming that functionalities have a one-to-one mapping to individual product chunks as in the highly modular architecture, a product functionality-focused innovation corresponds to adding a new chunk to the existing set. A product performance-focused innovation is equivalent to replacing an existing functionality that resides in a specific chunk with virtually the same functionality at a better performance. Note that instead of a replacement,

|| serviceability”, “product reliability (mean time to first failure)”. There are multiple meanings to be distinguished here. They use product performance as an abbreviation of “firm product performance” measuring how good products that the focal firm develops are concerning their intrinsic properties as opposed to their performance on the market. “Number of unique product features” refers to the number of product functionalities and “product reliability” is a product performance dimension in the language of this thesis. However, these terms are lumped together with “overall product performance” making a clear distinction difficult. 24 For a different definition of modularity see e.g., Baldwin and Clark (2000)

Product performance and functionality in product development | 35

an enhancement or augmentation of a given functionality within a chunk may be sufficient to improve product performance. In this simplified view, the only two possible operations (short of outright removal of a functionality or component) are chunk replacement or augmentation as opposed to the addition of a chunk. These operations are covered by product performance- and product functionality-focused innovations, respectively25. This simple logic is illustrated for in figure 2 (see section 3.3)26. This simplified view of product performance-focused innovations on a technical level disregards two important considerations. First, by construction, i.e., due to the focus on individual chunks, it ignores the overall relevance of product architecture and second, the scientific and technological drivers of product performance are disregarded. A product’s architecture by itself influences product performance. Highly integrated architectures are usually designed as to optimize product performance at the cost of flexibility (Ulrich and Eppinger, 2016). From a scientific and technological perspective, actual product performance can be decomposed into two quantities, technological potential and technological yield (Iansiti, 1997; Iansiti, 1998; Foster, 1986). Iansiti (1998, 1997) defines technological potential (TP) as “the lowest known upper bound model” of a measurable performance dimension for which an upper bound model—often rooted in natural sciences—in terms of an algebraic expression exists. This upper bound model can for example be a physical limit such as the speed of light (Afuah, 2003). Technological yield (TY), on the other hand, “measures how close a product actually comes to its maximum theoretical performance” (Iansiti, 1998) and is thus more closely related to engineering than to science. The following formula applies to a product with a particular performance dimension P (Iansiti, 1997; Iansiti, 1998): P = TP × TY, where 0 < TY < 1 In product development, technological potential is determined by the chosen technology paradigm such as employing supercomputers with a single processor. Often radical innovation then causes a shift to a new technological paradigm such as that of multiprocessor computers with an expectedly higher technological potential (see e.g., Afuah, 2003). This shift is often visualized by moving to a new technological S-

|| 25 Note that in the context of product changes, the product development literature distinguish “upgrades” and “add-ons” (Ulrich and Eppinger, 2016). Following the arguments presented thus far, functionality-focused innovations would classify as an upgrade or an add-on while performance-focused innovations would likely only be associated with upgrades so that this distinction is not useful here. 26 The illustration focuses on performance- and functionality-focused acquisitions instead of innovations. The general logic described here, however, applies to both innovations and acquisitions in the present context.

36 | Performance- and functionality-focus in product development and acquisitions

curve (see e.g., Afuah, 2003). Product performance-focused innovation that aims at improving technological potential can result in developments at the architectural level as shown in the previous example, at the component level as exemplified by the move from vacuum tubes to transistors in electronics (see e.g., Afuah, 2003) or in principle entail process innovations. Similarly, technological yield improvement as an objective may effect product architecture changes (MacCormack et al., 2001), e.g., through rationalizing inter-component communication bottlenecks (see e.g., Afuah, 2003), component-level changes, e.g., optimizing a search engine algorithm or process innovation (Iansiti, 1998) such as the move from mercury lamps to excimer lasers in producing integrated circuits via photolithography (Kapoor and Lim, 2007)27. Product functionality-focused innovations can entail changes on the level of individual or several components, individual or several chunks, product architecture, process changes or a mixture of two or more of these changes. One or several chunks or components and/or the product architecture are affected depending on the breadth of the product functionality-focused innovation. It is unlikely that a product functionality-focused innovation results in pure architecture level changes or pure production process changes. An example for the first could be a product functionality-focused innovation aiming at enabling the quick and easy integration of add-ons to a software program, i.e., the move from integral to a modular architecture. However, component changes are likely inevitable to facilitate such an architectural transformation.

3.2.4 Relation of product performance- and product functionality-focused innovations to other innovation typologies Literature on innovation management has proposed several typologies of innovations. While on the first glance it might seem that the distinction between product performance- and product functionality-focused innovations can be subsumed under one of the existing typologies, I will show in the following that this is not true. The subsequent sections dealing with acquisitions will also shed light onto the value add of the distinction introduced in the present thesis. Well known typologies of innovations include (1) incremental vs. radical, (2) regular vs. revolutionary vs. niche vs. architectural (Abernathy-Clark model), (3) incremental vs. architectural vs. modular vs. radical (Henderson-Clark model), (4)

|| 27 The switch from mercury lamps to excimer lasers can be viewed as changing the technological potential or the technological yield depending on whether one considers the physical limitations of the production technology (Kapoor and Lim, 2007) or the size of the atoms (Ito and Okazaki, 2000) as the upper bound model.

Product performance and functionality in product development | 37

sustaining vs. disruptive (disruptive innovations), and (5) core vs. peripheral (core/peripheral) (Afuah, 2003; Henderson and Clark, 1990; Abernathy and Clark, 1985; Christensen, 1997; Tushman and Anderson, 1986; Tushman and Murmann, 1998; Gatignon et al., 2002). According to Gatignon et al. (2002) these models differ in their characteristics (1,3), type (2,3,4), and with respect to their locus in a product’s hierarchy (5). The incremental vs. radical dichotomy (1) distinguishes between the organizational and the economic (competitiveness) view (Afuah, 2003). In the organizational view an innovation is considered radical if previously non-existent knowledge and capabilities are necessary for its realization so that it is competence destroying (Tushman and Anderson, 1986; Afuah, 2003). The economic view holds that innovations are deemed radical if the corresponding products possess attributes so superior that prior products are rendered non-competitive (Afuah, 2003). In short, the organizational view connects innovations with organizational knowledge and capabilities while the economic view connects innovations with surpassing a competitiveness threshold. The distinction of product performance- and product functionality-focused innovations does neither—it is irrelevant for the definition if competence is destroyed or existing products lose their competitiveness, though both types of changes may accompany either type of innovation. The Abernathy-Clark (2) model assigns innovations to one of four categories depending on whether market capabilities and technical capabilities are preserved or destroyed (Afuah, 2003; Abernathy and Clark, 1985). Each category is linked to a different competitive environment that necessitates specific organizational and managerial skills (Abernathy and Clark, 1985). The model stresses that destruction of market or technical capabilities can be compensated by capabilities in the respective other domain (Afuah, 2003; Abernathy and Clark, 1985). The definition of product performance- and product functionality-focused innovations is not contingent on the preservation or destruction of market or technical capabilities—though both might be a side-effect. The model of product performance- and product functionality-focused innovations makes most sense in a specific competitive environment, i.e., one where competitors differentiate both on product performance and product functionality, but does not require it. Hence, specific competitive environments play a role in both models but is much less prominent for product performance- and product functionality-focused innovations. The Henderson-Clark model (3) differentiates component knowledge from architectural knowledge and proposes that each can either be enhanced or destroyed (Henderson and Clark, 1990; Afuah, 2003). The corresponding framework consists of four categories with the labels given above. Based on the above discussion of product performance- and product functionality-focused innovations on a technology level it is clear that both types of innovation may cause the enhancement or destruction of component or architectural knowledge.

38 | Performance- and functionality-focus in product development and acquisitions

The dichotomy of sustaining vs. disruptive innovations (4) combines the perspectives of product performance and application (Christensen, 1997; Afuah, 2003). A disruptive innovation is characterized by initially worse performance in an application that is irrelevant for mainstream customers of incumbent firms. Product development by niche firms for non-mainstream applications eventually leads to a better product performance than that of mainstream products. Eventually, the markets for mainstream and non-mainstream applications converge so that niche firms replace incumbents in their leadership role (Christensen, 1997; Afuah, 2003). This mechanism is rather complicated and involves several firms and initially separate performance trajectories in different applications. Neither is necessarily the case for product performance- and product functionality-focused innovations. Even a single firm perspective with a singular performance trajectory in one application is—in principle—a sufficient setting for a product performance-focused innovation. In addition, the distinction between sustaining and disruptive innovations largely ignores the potential impact of product functionality-focused innovations. The core/peripheral dichotomy (5) holds that products consist of “nested hierarchy of subsystems and linking mechanisms” (Gatignon et al., 2002). Core subsystems are defined through their tight linkage to other subsystems while peripheral subsystems exhibit a weak coupling (Gatignon et al., 2002). Core subsystems may be connected with “strategic performance parameters” (Gatignon et al., 2002). The core/peripheral dichotomy may appear to be similar to that of performance- and functionality-focused innovations because of an intuitive association between a performance improvement and changes to core subsystems as well as a functionality addition and the introduction of a new peripheral subsystem. However, this is not necessarily true, as performance improvements can originate from architectural changes as explained in section 3.2.3 and functionality additions can affect core subsystems, as well. The comparison between the concept of product performance- and product functionality-focused innovations and the models (1) through (5) reveals that they are—at the very least—distinct from each other. In addition, two overarching differences that distinguish the categorization in product performance- and product functionality-focused innovations from models described above exist, as well. First, this thesis establishes product performance- and product functionality-focused innovations clearly as strategic rationales for product development efforts. This strategic perspective is largely missing in models (1)-(4) where the organizational view of (1) is considered. A strategic ex-ante perspective is in contrast to the ex-post nature of the typologies (1)-(4) where this statement again applies to the organizational view of (1). The economic view of the incremental vs. radical dichotomy (1) and model (5) are potential exceptions to the previous claim. Ascribing labels such as “radical” or “disruptive” to innovations is usually done in retrospective (Danneels, 2004; Dahlin and Behrens, 2005). Those terms are likely not associated with the motive of an invention—at least not in their scientific meaning. It has become a hype especially

Product performance and product functionality in technology-focused acquisitions | 39

among startup founders to describe their technologies as “disruptive” as evidenced in startup conference names such as “TechCrunch DISRUPT”28 and therefore use the term beyond its original meaning (Danneels, 2004). Rigorous scrutiny usually reveals that these labels at the time of their expression are premature at best. The second fundamental difference relates to the fact that the product performance and product functionality dichotomy of innovations takes a strong customer perspective by asking the questions: Is a product performance improvement expected to be noticeable by a customer? Is the functionality new to a customer—though not necessarily new to the world (Tidd et al., 2003)? With the slight exception of model (4) this perspective is largely missing or only implicit in the other typologies. This customer perspective might even add a new element to the structural approach to innovation proposed by Gatignon et al. (2002) that differentiates the models of innovation by their locus in a product’s hierarchy, characteristics and type as described above.

3.3 Product performance and product functionality in technology-focused acquisitions Already in chapter 1 I posited that the acquisition of XtremIO is product performance-focused and that of Syncplicity is product functionality-focused. The dichotomy of product performance-focused acquisitions and product functionalityfocused acquisitions can now be properly defined and characterized based on the definition of product performance- and product functionality-focused innovations that heavily drew from insights of the product development, product quality and—to a lesser extent—marketing literature. In short, a product performance-focused acquisition refers to a technologyfocused acquisition of a firm whose main aim is to provide the technology and capabilities for realizing a product performance-focused innovation. In the same vein, a product functionality-focused acquisition refers to a technology-focused acquisition of a firm whose main aim is to provide the technology and capabilities for realizing a product functionality-focused innovation. This logic is captured in figure 2 that shows in a highly simplified, yet intuitive two-dimensional framework how acquired technologies interact with an acquirer’s existing technologies in terms of performance- and functionality-focus.29 The framework distinguishes between purely performance-focused (1) and purely functionality-focused (4) acquisitions. It also allows for mixed cases with a relatively higher performance- (2) or functionalityfocus (3). Note that the distinction between performance- and functionality-focused

|| 28 https://techcrunch.com/event-info/disrupt-sf-2016/ (accessed 02.12.2016) 29 Note that a description of the logic underlying figure 2 is given in section 3.2.3.

40 | Performance- and functionality-focus in product development and acquisitions

acquisitions is useful beyond capturing some of the heterogeneity in the acquisitions of XtremIO and Syncplicity by EMC. Due to its close connection to product development it is one answer to Angwin’s (2007) call for a “richer picture of motivations for M&A” bringing scientific research closer to “actual M&A practice ‘on the ground’” (Angwin, 2007). For convenience and readability, I will drop the word “product” from the labels of both types of acquisitions and speak of performancefocused acquisitions and functionality-focused acquisitions as well as performancefocused innovations and functionality-focused innovations instead of their longer counterparts. The objective of the following paragraphs is to explain the most crucial similarities that the concepts of performance- or functionality-focused innovations compared with performance- or functionality-focused acquisitions share and what is clearly unique about the latter.

Mode of technology integration: Newly acquired technology… Replace technology for given functionality … 1

… in every application

Yes …replaces or enhances acquirer’s existing technologies to improve performance

2

… depending on specific application1

Primarily replace/ enhance existing technology while also adding new functionality

3 Primarily add new functionality while also replacing/enhancing existing technology

4 No technology sourcing

ILLUSTRATIVE SIMPLIFIED

Add new functionality to existing product

No Add new product with new functionalities to existing portfolio No

Yes

…expands acquirer’s existing technologies Product

Individual functionalities within product

Acquirer’s original technology

Acquired technology—primary focus

Acquired technology— secondary focus

1 Replacement depending on specific application—e.g. replacement for one type of input but not for another

Fig. 2: Simplified framework distinguishing performance- and functionality-focused acquisitions on a technology level

Product performance and product functionality in technology-focused acquisitions | 41

The key similarity between performance- and functionality-focused innovations and performance- and functionality-focused acquisitions is already given in their definition. The strategic rationale of improving product performance or adding product functionality with its ex-ante character is the same in both concepts. Performance- and functionality-focused innovations and performance- and functionality-focused acquisitions are linked to each other via the make-or-buy decision in the product development process. By virtue of the make-or-buy decision the strategic rationale of the innovation project carries over to the technology-focused acquisition. As an analogy consider the manufacturer supplier relationship that is likely the most common outcome of a “buy” instead of “make” decision. In this context, companies such as Hewlett-Packard or Toyota have a supplier produce and possibly design products and components according to their—the manufacturer’s—needs and specifications (Ulrich and Ellison, 2005; Fine and Whitney, 1996; Strauss, 1962). The implication is that these manufacturers have a very clear, strategic objective for their externally acquired goods in mind and translate this objective into precise specifications. It is only natural to assume that the a similar logic can be applied to technology-focused acquisitions, with the obvious restriction that it does not make sense to expect that technologies and capabilities packaged in firms (Granstrand and Sjölander, 1990) can precisely satisfy engineering specifications. This description brings the notion of maximizing strategic fit that is very prominent in mergers and acquisitions (Bauer and Matzler, 2014) to a very concrete level. Another key similarity is that uncertainty related to market and technology is equally present and relevant in performance- and functionality-focused innovations and performance- and functionality-focused acquisitions. This is evident by comparing the discussions in sections 2.2 and 3.1. Uncertainty in relation to performanceand functionality-focused acquisitions will play an important role in the development of hypotheses. The core differences between the concepts of performance-focused innovations and functionality-focused innovations and performance- and functionality-focused acquisitions lie in the decision making process (Haspeslagh and Jemison, 1991b) and in the uniqueness of the decisions in the context of acquisitions. Haspeslagh and Jemison (1991a) in their comparison of internal resource allocation and acquisition decision making processes note seven unique characteristics of the latter: (1) sporadic nature, (2) dissimilarity from manager’s regular experiences, (3) opportunistic nature, (4) speed of decision making, (5) limited access to and processing of information, (6) indivisibility of the opportunity, and (7) inherent riskiness (Haspeslagh and Jemison, 1991b). In the context of this thesis, especially (5) and (7) are important so that I elaborate on them shortly. “Limited access to and processing of information” (5) refers to the information asymmetry in acquisitions. This issue is exacerbated in the focal technology-focused acquisitions (such as performance- and functionality-focused acquisitions) because of the tacitness and socialembeddedness of knowledge. Characteristics (1)-(6) contribute to the overall riski-

42 | Performance- and functionality-focus in product development and acquisitions

ness of acquisitions as opposed to internal resource allocation decisions (Haspeslagh and Jemison, 1991b) such as those related to product development. Hence, risk and uncertainty may play an even stronger role in technology-focused acquisitions than in product development. Preparing and conducting a technology-focused acquisition with a performance- or functionality-focus brings with it a whole set of decisions. Section 2.2.3 provides an overview of the key decisions in technology-focused acquisitions that apply to performance- and functionality-focused acquisitions, as well. These decisions have analogues in product development but are unique due to the idiosyncrasies of tech-focused acquisitions as a setting. I will shortly comment on the decisions of acquisition timing and deal value as these are the focus of this thesis. From the perspectives of product development, marketing, and technology diffusion, timing is an important issue (Hoppe, 2002; Chatterjee and Eliashberg, 1990; Kauffman and Li, 2005; Bhattacharya et al., 1998a; Götz and Astebro, 2006; Wheelwright and Clark, 1992; Bhattacharya et al., 1998b). Timing decisions have been analyzed mainly with respect to time-to-market (Cohen et al., 1996), introduction timing of different product versions (Bhattacharya et al., 1998a; Padmanabhan et al., 1997) or product attributes (Klingebiel and Joseph, 2016), and technology adoption and diffusion (Hoppe, 2002; Jensen, 1982; McCardle, 1985; Rogers, 1983; Doraszelski, 2004; Chatterjee and Eliashberg, 1990; Farzin et al., 1998). The technology adoption and diffusion literature differentiates individual (Chatterjee and Eliashberg, 1990) and industry wide adoption (Jensen, 1982; Rogers, 1983) and firm level adoption (Jensen, 1982; Farzin et al., 1998; Doraszelski, 2004; Götz and Astebro, 2006) as different levels of analysis (see Hoppe (2002) and Geroski (2000) for a comprehensive review). Especially the latter level that studies individual firm decision making on adoption bears closest resemblance to the timing decision in technology-focused acquisition decision making. However, the timing decision in technology-focused acquisitions studied in this thesis is unique in relation to the rather generalized adoption and diffusion models because it deals specifically with technology-focused acquisitions that are subject to the idiosyncrasies mentioned above and also in this thesis are imbued with a very concrete strategic purpose (i.e., of improving product performance or adding new functionality). Capital budgeting decisions are the closest product development analogue to the decision on deal value in acquisitions. Capital budgeting decisions in product development occur on different time scales, i.e., calendar time for the annual budget decision and “in real time” when a project passes a gate within the stage-gate model (Cooper, 1994). Decisions on acquisition deal value are unique due to their comparatively spontaneous nature (Haspeslagh and Jemison, 1991b). According to a CFO survey by Graham and Campbell (2002) capital budgeting decisions are predominantly made based on financial techniques such as IRR, NPV/DCF or hurdle rate. While these or similar techniques may be used for acquisitions (DeAngelo, 1990; Koeplin et al., 2000), their application is complicated due to the increased

Product performance and product functionality in technology-focused acquisitions | 43

uncertainty and information asymmetry of acquisitions (Haspeslagh and Jemison, 1991b). Especially in technology-focused acquisitions of startups with or without products, some acquirers divert from traditional approaches favoring per-engineer or per-user based valuation techniques or endeavor to calculate how much it would cost to build and market the target’s product by themselves30. Besides these “technical” differences, acquisition deal value decisions are influenced by unique drivers such as bidding competitions and the winner’s curse (Kagel and Levin, 1986; Varaiya and Ferris, 1987), founders’ and investors’ minimum expectations30, and more generally their negotiation position and power. The dichotomy of performance- and functionality-focused acquisitions has been derived from product performance- and functionality-focused innovations. In essence, this dichotomy relates to the type of technology transferred via the acquisition of a firm. There are at least two other somewhat related concepts that I shortly comment on. In relation to the acquisition integration decision, Puranam et al. (2009) distinguish technology-focused acquisitions with the intended use of the acquired technology as either a component or a standalone product. This distinction is similar to that of performance- and functionality-focused acquisitions as both share an ex-ante, strategic nature. They are different, however, because the component- vs. standalone-product distinction is primarily concerned with target product architecture and less with future customer benefit. Léger and Quach (2009) investigate the effect of technology compatibility and complementarity in technologyfocused acquisitions on acquisition performance. Both compatibility and complementarity have an ex-ante, strategic character at the acquisition level as do performance- and functionality-focused acquisitions. The strategic character of the latter, however, originates already within product development of the acquirer. This is a clear difference between the concepts studied by Léger and Quach (2009) and the framework introduced in this thesis. As an additional difference, compatibility takes place on the technology level and financial level31 and complementarity is relevant from a customer perspective while product performance and product functionality are relevant on both the technology and customer levels. As a final note on performance- and functionality-focused acquisitions, it needs to be pointed out that the process leading to an acquisition’s categorization as one or the other is to some extent endogenous. Depending on their own product landscape, product development efforts, and technology gaps the planned use of a target’s technology may be that of improving product performance for one potential acquirer but that of adding new functionalities for another. This strict endogeneity

|| 30 http://bits.blogs.nytimes.com/2013/04/07/how-deal-makers-put-a-value-on-start-upsdisruptions/ (accessed 05.12.2016). 31 According to Léger and Quach (2009), compatibility “reduces the investments the new [combined] entity needs to make to market a unified product portfolio”.

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is somewhat softened up when considering “archetypal” performance- or functionality-driven startups that will be described in section 3.4. As a short illustration of this idea consider a startup with technology that has achieved world-class product performance in a well-established performance dimension meaning that no potential acquirer is likely to have a technology with a better performance measured on that specific dimension. The likelihood is presumably high that such a startup will be acquired for improving product performance.

3.4 Derivation of hypotheses The purpose of this section is to derive hypotheses on acquisition timing and acquisition deal value that are conditioned on the differences between performance- and functionality-focused acquisitions as defined in section 3.3. In this effort, I build upon the previous sections and heavily draw from literature on technology-focused acquisitions, product development, technology adoption and diffusion, and marketing. I posit that the key argument for establishing these hypotheses rests on uncertainty. As explained in section 2.2.2, the two major types of uncertainties from the perspective of a buyer seeking a technology-focused acquisition are technology and market uncertainty32. Section 3.1 shows that these two types of uncertainty are also key in product development and therefore “carry over” to technology-focused acquisitions. Recall that technology uncertainty stems from lack of information on whether a specific product will “work” based on its components, architecture, or production methods. Market uncertainty is due to missing information on whether a product will sell successfully on the market driven, e.g., by consumer preferences and expectations. To generate hypotheses, I will first derive uncertainty profiles for performance- and functionality-focused acquisitions that describe the temporal evolution of uncertainty. Section 3.4.1 presents the results of this effort. Then I will apply these uncertainty profiles to acquisition timing decisions and deal value decisions to develop hypotheses in sections 3.4.2 and 3.4.3, respectively.

|| 32 Depending on the author, other types of uncertainty are also deemed relevant such as competitive uncertainties (Souder and Moenaert, 1992), business uncertainties (Afuah, 2003), uncertainty related to target management and operations (Ransbotham and Mitra, 2010; DePamphilis, 2012) or financial outcomes (DePamphilis, 2012). However, they are not discussed here further, as there is no consensus in the literature on their relative importance and they should not systematically differ between performance- and functionality-focused acquisitions.

Derivation of hypotheses | 45

3.4.1 Uncertainty profiles of performance- and functionality-focused acquisitions The derivation of the uncertainty profiles follows a two-stage approach that I summarize here shortly. The first stage builds technology and market uncertainty profiles from the perspective of “archetypal” startups that will be acquired in a performance- or functionality-focused acquisition (sub-section 3.4.1.1). These profiles describe the evolution of uncertainty over time independent of an acquirer. In the second stage, the perspective of an acquirer is taken and the uncertainty profiles are modified due to the effects of information asymmetry and an acquirer’s superior knowledge of their own market and customers (sub-section 3.4.1.2). Note that uncertainty profiles are to be understood as representing uncertainty averaged over an entire population of acquisition targets. The use of “archetypal” acquisition targets in the derivation of uncertainty profiles and the implications of “non-archetypal” targets for the derivation are discussed in sub-section 3.4.1.3. 3.4.1.1 First stage: Uncertainty profiles from a startup’s perspective To establish the baseline uncertainty profiles from a startup perspective for both performance- and functionality-focused acquisitions I will first examine general drivers of uncertainty in product development. Then I will consider these drivers separately for market and technology uncertainty taking the perspective of “archetypal” startups that are “performance-driven” or “functionality-driven” and will likely be the future subject of a performance- and functionality-focused acquisition, respectively. The constructs of “archetypal” performance- and functionality-driven startups simplify the derivation. For a discussion of “non-archetypal” startups, please refer to section 3.4.1.3. According to Souder and Moenaert (1992), technological innovations “can be viewed as processes of uncertainty reduction, or alternatively, as processes of information collecting and processing.” Hence, during innovation projects uncertainty decreases over time (Moenaert and Souder, 1990). This reduction of uncertainty is facilitated by the activities in the product development process itself. Afuah (2003) highlights the information gained via “experimentation, trial, error and correction such as prototyping, beta site testing, and test marketing”. In their studies of software development, MacCormack et al. (2001) and MacCormack and Verganti (2003) mention presentation of the first prototype to customers, integration of component modules and availability of a beta release for customers as the three key milestones where uncertainty is reduced. The enabler of uncertainty reduction is thus the

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availability of feedback mechanisms which Souder and Moenaert (1992) link to the concept of task analyzability33. The effects of product development on technology and market uncertainty apply equivalently to “archetypal” performance-driven startups and functionalitydriven startups. A performance-driven startup refers to a young firm that will likely be the subject of a performance-focused acquisition and views maximizing product performance, i.e., performance-focused innovations, as a major product development objective. This is because its product requires a non-trivial advancement over the performance of existing products in order to be competitive. A functionalitydriven startup is defined as a young firm that competes mainly based on the novelty of its functionalities so that a major performance advancement is not or considerably less important. Technology and market uncertainty profiles for performancedriven and functionality-driven startups should have the following shape based on the discussion above. Technology and market uncertainty start at high level at the infancy stages of product development and decrease over time for both performance- and functionality-driven startups. Prior to market introduction of a product, discontinuous uncertainty drops occur whenever feedback on the functioning of the technology or its desirability by test customers arrives. The sharpest decline of market uncertainty takes place—depending on the rate of adoption—shortly after market entry because feedback from a large group of customers with likely increased heterogeneity in their characteristics pours in. Technology uncertainty exhibits the largest decrease prior to a product’s market launch. The decline of technology uncertainty after the product’s market launch occurs because the scalability of the technology will be known best in an environment with a large number of diverse customers. Overall, market uncertainty may be somewhat lower for a performance-driven technology due to the knowledge that a similar product with inferior performance is successfully marketed by another firm, i.e., the potential future acquirer. As a certain level of product performance needs to be reached in the case of a product from a performance-driven startup, one might expect that technology uncertainty is significantly higher at all times until product development is finished. I argue that this is not true for two reasons (see the discussion of product performance in section 3.2). First, the technology potential as a theoretical upper limit for product performance is well known in many cases (Iansiti, 1997; Iansiti, 1998). Second, the actually achieved level of product performance (Iansiti, 1997; Iansiti, 1998) can usually be measured. Both factors considerably reduce uncertainty. The above arguments nicely fit with Afuah’s (2003) discussion on using technological and market regularities for uncertainty reduction from an industry perspective. An important technological

|| 33 Souder and Moenaert (1992) define task analyzability as “the degree to which there are procedures to identify uncertainty and reduce it.”

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regularity is that product performance moves along S-Curves (see e.g., Afuah, 2003). An important market regularity is that customers prefer improvements in “space, time, and mass” (see e.g., Afuah, 2003) that are representative of product performance improvements. Both arguments imply a reduced technology and market uncertainty for performance-driven startups as compared to a situation where the technological and market regularities described above are unknown. In summary, baseline uncertainty profiles for performance- and functionalitydriven startups look as follows. At the beginning of product development, technology and market uncertainties tend to start high, drop discontinuously when feedback comes in and have another decline shortly after market entry. Market uncertainty for products of performance-driven startups is overall somewhat lower given that there is a comparable existing product at worse product performance and due to market regularities. Technology uncertainty for products of performance-driven startups tend to be only somewhat higher as a consequence of the theoretical underpinnings of technology potential and measurability of product performance34. 3.4.1.2 Second stage: Perceived uncertainty profiles from an acquirer’s perspective The second stage builds upon the first but takes the perspective of an acquirer instead of a startup. Therefore, uncertainty as perceived by the acquirer is analyzed. This stage draws from literature on decision making, technology adoption, and marketing. In assessing technology and market uncertainty of a potential acquisition target, an acquirer is severely limited by information asymmetry caused by confidentiality and secrecy in the acquisition process (Haspeslagh and Jemison, 1991b; Graebner et al., 2010; Warner, 2006). However, she possesses three sources of information, i.e., public information about the target such as information in market reports or on product launches (James et al., 1998), internal knowledge about markets and technologies and knowledge about technological as well as market regularities (Afuah, 2003). The first and third source of information have already been incorporated in the baseline uncertainty profile and shall therefore be disregarded here. I argue that evaluating a target’s uncertainty profile based on information asymmetry and acquirer private information is analogous to examining an object through a speckled magnifying glass. The acquirer will see some aspects of the target sharper than the target itself (superior internal knowledge accessible) while other aspects appear clouded due to information asymmetry (only outside-in perspective available). I operationalize the notion of the speckled magnifying glass by || 34 Note that product performance needs to be measurable with reasonable effort for the arguments to make sense. However, if a startup builds its competitive advantage upon product performance, an infrastructure for measuring performance should be in place.

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modifying the baseline uncertainty profiles derived above such that uncertainty is increased (clouded vision) or reduced (sharper vision). The image of the speckled magnifying glass has parallels to theory via the concepts of perceived uncertainty and perceived risk (Pablo, 1996; Chatterjee and Eliashberg, 1990) as a basis for strategic decision making (see section 2.2.1). The modification of uncertainty profiles to account for an acquirer’s perspective of target uncertainty profiles is realized by adding or subtracting a linear function for clouded or sharper vision, respectively. The linear function has a negative slope, i.e., it decreases over time eventually reaching and staying at zero, as the effects of clouded and sharper vision are likely more pronounced for targets at a low maturity level than those with a high maturity level post market launch when there is considerable public information available. In the following, adding this linear function corresponds to raising uncertainty profiles and subtracting the function to lowering them. A modification of uncertainty profiles not using a more complicated transformation is justified because the general shape of the uncertainty profiles remains roughly the same. Ransbotham and Mitra (2010) write in this context “[The target’s] technology matures with age and there is a better understanding among potential buyers of its feasibility and benefits. Likewise, as the target matures, buyers have better knowledge of its markets and the revenue potential of its technology. Thus, [...] uncertainty decrease[s] with target age [...].” For a performance-focused acquisition, perceived technology uncertainty increases only slightly because the effect of information asymmetry is mitigated by an acquirer’s prior experience and perceived information reliability (Chatterjee and Eliashberg, 1990), i.e., the belief that performance measurements are reliable, and observability (Rogers, 1983). In a performance-focused acquisition, technology is acquired that has roughly the same functionality of that of an acquirer at a better performance. Hence, an acquirer may have conducted R&D in a similar direction as that of the acquisition target but failed (Henkel et al., 2015) leading to prior experience. Perceived reliability of information and observability decrease uncertainty in technology adoption decisions (Chatterjee and Eliashberg, 1990; Rogers, 1983). The performance advancement of the technology in a performance-focused acquisition should be independently measurable and demonstrable in an experiment leading to a high reliability of information and also observability. In the case of functionalityfocused acquisitions information asymmetry raises technology uncertainty but prior experience, perceived information reliability and observability play a weaker role in attenuating this increase. Prior experience is less likely as new functionality fills a gap for the acquirer, perceived information reliability and observability is less pro-

Derivation of hypotheses | 49

nounced, as there is no product performance improvement to measure or demonstrate35. In a performance-focused acquisition, perceived market uncertainty is lowered relative to the baseline uncertainty profile because the acquired technology addresses the same or a similar market as the acquirer. One can expect that the acquirer has superior knowledge about market demands and developments through long standing relations and continuous customer interactions. The acquirer’s market knowledge is likely better than that of the target. To derive the reduction of perceived market uncertainty with respect to a functionality-focused acquisition one needs to distinguish two cases. If the technology transferred in a functionalityfocused acquisition targets a different market than the current market of the acquirer, no change in perceived market uncertainty can be expected. Conversely, if it targeted the same market as the acquirer a reduction in perceived market uncertainty would follow from the same logic as above. However, as explained in the following paragraph, the preferences of an acquirer’s customers need to be taken into account. The result of this line of reasoning is that no change of market uncertainty for functionality-focused acquisitions occurs. Note that an acquirer’s customers are generally different from—though may have some overlap—with an acquisition target’s customers. Depending on a target’s life cycle at the time of acquisition, it may not even have any customers. The previous paragraph assessed perceived market uncertainty with respect to performance- and functionality-focused acquisitions based on whether an acquirer has an informational advantage over a startup before its acquisition. However, an acquirer can be expected to also consider how her customers will view improved product performance or additional functionality that are the expected outcomes of the acquisition once they are implemented in the acquirer’s future products. The marketing literature offers some insights into how perceived market uncertainty should be modified given customer preferences. Zhang and Markman (1998) and Zhou and Nakamoto (2007) differentiate between product performance improvements and product functionality additions from a customer preference perspective. Zhang and Markman (1998) argue that customers prefer performance improvements from one product version to the next over functionality additions due to a better recall of product performance differences. This indicates an effective decrease of perceived market uncertainty for performance-focused acquisitions and no alteration for functionality-focused acquisitions. Zhou and Nakamoto (2007) in a more || 35 Note that Rogers (1983) also discusses relative advantage, complexity, compatibility, and trialability as drivers of adoption. Relative advantage is picked up in the present thesis in relation to market uncertainty. Compatibility and trialability are disregarded because on average they should be the same for both performance- and functionality-focused acquisitions. Complexity is not discussed here because in an acquisition not only technologies but also people who are experts in their own technology's complexities are transferred.

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complicated argument come to the conclusion that generally familiar or expert customers prefer added product functionality while unfamiliar or novice customers have a preference for improved product performance. Their logic is driven by perceived congruency and perceived utility uncertainty. Product functionality additions have a higher utility uncertainty than product performance improvements (Zhou and Nakamoto, 2007). Familiar or expert customers, however, are virtually unaffected by this uncertainty due to their experience and thus prefer product functionality additions that they view as moderately incongruent over product performance improvements that they view as too congruent (Zhou and Nakamoto, 2007). Unfamiliar or novice customers prefer product performance improvements due to their lower utility uncertainty and because they view functionality additions as too incongruent (Zhou and Nakamoto, 2007). Based on this explanation of the mechanism for the relative preferences of novice and expert customers it appears that product performance improvements represent the “safer bet”. Given the overall high uncertainty associated with technology-focused acquisitions acquirers likely give more weight to the “utility uncertainty” effect than the “congruency effect”. Hence, I argue with respect to Zhou’s and Nakamoto’s (2007) findings that acquirers will perceive market uncertainty of product performance improvements as lower than for product functionality additions. As before, the conclusions is that performancefocused acquisitions experience a reduction in perceived market uncertainty relative to functionality-focused acquisitions. In summary, taking an acquirer’s perspective to evaluate perceived technology and perceived market uncertainty, the perceived uncertainty profiles of performance-focused and functionality-focused acquisitions look as follows. On average, both types of uncertainty start at a rather high level and decline continuously over the evolution of the future acquisition target’s life cycle over time. Product market entry marks the beginning of another decline for both types of uncertainty but especially market uncertainty. Perceived technology uncertainty of performance-focused acquisitions is on average continuously higher but not by much due to the effects described above. Market uncertainty of performance-focused acquisitions is on average much lower than that of functionality-focused acquisitions. A combined graph, that represents the sum of technology and market uncertainty, shows that overall uncertainty is on average lower for a performance-focused acquisition than for a functionality-focused acquisition throughout the potential acquisition target’s life cycle. Only after market entry, both curves converge. Figure 3 depicts three graphs that show perceived technology uncertainty, perceived market uncertainty and perceived combined uncertainty for performance- and functionality-focused acquisitions, respectively. The three graphs properly reflect the overall temporal evolution of perceived uncertainty and the relative differences between performance- and functionality-focused acquisitions. Obviously, the graphs are merely illustrative regarding the specifics of their shapes. The discontinuous nature of uncertainty changes is disregarded in the graphs in favor of a cleaner appearance.

Derivation of hypotheses | 51

Note that the above descriptions represent an aggregate view that generalizes over many potential acquisition targets. For a specific acquirer and acquisition target it is of course necessary to view dyadic information (Yu et al., 2016; Wang and Zajac, 2007). In this context specific signals (Hoppe, 2002) that are relevant for uncertainty evaluation such as patents (Warner, 2006) need to be considered. Also, note that the overall level of uncertainty may be highly influenced by an industry’s life cycle stage. Uncertainty is expected to be higher prior to the establishment of a dominant design than afterwards (Jones et al., 2001).

ILLUSTRATIVE

Technology uncertainty1 market entry

Performance-focused innovation2 Functionality-focused innovation2

Combined uncertainty2 market entry Time Market uncertainty1 market entry

acquisition uncertainty threshold Time

Time 1 Perceived uncertainty from acquirer’s perspective 2 Performance or functionality focus assessed from acquirer’s perspective

Fig. 3: Temporal evolution of technology, market, and combined uncertainty

3.4.1.3 A note on the derivation of uncertainty profiles The above derivation of baseline uncertainty profiles involves the concepts of “archetypal” performance- and functionality-driven startups. “Archetypal” performance-driven startups aim at maximizing product performance while “archetypal” functionality-driven startups have a novel functionality where performance issues do not exist or are easily resolved given current technology. This triggers the ques-

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tion if the above derivation of uncertainty profiles also holds for “non-archetypal” startups. The answer is yes, but with some modifications. “Non-archetypal” cases would be (1) the acquisition of a performance-driven startup for adding new functionality and (2) the acquisition of a functionality-driven startup for improving performance36. Case (1) is a special case of leapfrogging (Götz and Astebro, 2006; Lee and Lim, 2001) where an acquirer adds novel functionality that in addition to being new leaves behind competitors due to superior product performance. Hence, the strategic rationale for the acquisition is not only product functionality but also product performance. Therefore case (1) is a hybrid case as explained in section 3.2.2 for innovations that apply similarly to acquisitions. In case (2) an acquirer’s existing functionality likely has abysmal performance that the acquirer cannot fix (in time) by herself. In this scenario the acquisition of a startup for which product performance optimization was not a priority but whose technology would represent a significant product performance improvement for an acquirer could make sense. I argue that the above derivation of uncertainty profiles applies to case (1). However, as case (1) is a blend between performance- and functionality-focused acquisitions, the arguments have to be weighed depending on whether the performance- or functionality-aspect bears greater relevance for an acquirer. Case (2) appears to be somewhat artificial and thus may likely be disregarded.

3.4.2 Acquisition timing with respect to performance- and functionality-focused acquisitions In this section, a hypothesis on acquisition timing for performance- and functionality-focused acquisitions is generated (subsection 3.4.2.1). Hypotheses on potential interactions between an acquisition’s performance or functionality focus and target characteristics follow (subsection 3.4.2.2). This section builds upon the uncertainty profiles that are derived in section 3.4.1 and displayed in figure 3. 3.4.2.1 Hypothesis on acquisition timing In a 2002 BusinessWeek cover story John Chambers, CEO of serial acquirer Cisco Systems is quoted saying “We’re making the decisions to acquire a company based

|| 36 A potential third case would be a startup with a hybrid focus, i.e., it is performance-driven with respect to some competitors and functionality-driven with respect to others. Such a case is likely a hybrid in terms of an acquisition’s performance- or functionality-focus, as well. Its treatment with regards to uncertainty profiles should be similar to that of case (1).

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on a later point in time, which dramatically lowers the risk”37 (Byrne and Elgin, 2002). Indeed, the decision of acquisition timing is based on the fact that an acquirer is confronted with the options of acquiring a target firm now or waiting until a later point in time to do so (Brueller et al., 2015; Chaudhuri et al., 2005; Graebner et al., 2010; Ransbotham and Mitra, 2010; Toxvaerd, 2008; Warner, 2006; see also section 2.2.4 for the relevance of the timing decision). Since acquisition endeavors, especially those with a technology focus, suffer from high uncertainty and the downside risks associated with these uncertainties (Chaudhuri et al., 2005; Graebner et al., 2010; see also section 2.2.2), waiting is a legitimate strategy for obtaining additional information and thereby reducing uncertainty (Chaudhuri et al., 2005; Graebner et al., 2010; Warner, 2006; McCardle, 1985; Toxvaerd, 2008). Indeed, in very general terms the literature on strategic decision making observes decision deferral (i.e., delaying the acquisition to acquire) as a measure of reducing uncertainty (Lipshitz and Strauss, 1997; Hirst and Schweitzer, 1990)38. Given these arguments, I conclude that acquisition timing decisions are made based on an evaluation of overall perceived uncertainty. I also argue that the decision of timing with respect to performance- or functionality-focused acquisition shares similarities with the decision to adopt a specific technology. In the case discussed here, the technology is not “handed to [the firm], as it were, on a silver platter” which is assumed for technology adoption models (McCardle, 1985) but obtained via acquisition of another firm. Nevertheless, building the analogy between timing decisions in technology-focused acquisitions and timing decisions within technology adoption is fitting for three reasons. First, in technology-focused acquisitions firms must decide to adopt a technology that is more or less new to them. Second, one stream of literature on technology adoption also uses uncertainty as a key argument (Hoppe, 2002). Third, the literature on technology adoption and diffusion provides insights into how decisions on timing are made in which waiting is also a viable option.

|| 37 The statement “based on a later point in time” refers to Cisco waiting with an acquisition “until a target company has a proven product, customers, and management team” (Byrne and Elgin, 2002). 38 An argument on decision timing purely based on uncertainty would entail waiting until uncertainty is fully resolved. Obviously, there are natural limits to the time an acquirer can reasonably delay an acquisition. An acquirer may have an urgent need for a specific technology causing an earlier acquisition. Also, Graebner et al. (2010) state in this context that “waiting poses the risk that a desirable target will be acquired by a competitor, will grow too large to acquire, or will develop its technology in a direction that is incompatible with the platforms and product road maps of the buyer, diminishing the potential synergy from the deal.” These factors, however, are expected to affect both performance- and functionality-focused acquisition in a similar manner and are thus not discussed further. In addition, acquisition timing may be influenced by an acquirer’s routines (Brueller et al., 2015) and obviously hinges on target shareholders’ willingness to sell at a given point in time (Graebner and Eisenhardt, 2004).

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Jensen (1982) formally derives an optimum stopping rule stating that “the firm should adopt when its current belief the innovation is good attains a minimum reservation level” or threshold. Overall perceived uncertainty can be viewed as one measure for how certain an acquirer is that the acquisition target will bring the benefits that the target was initially selected for. Combining these two thoughts and applying them to the timing decision in acquisitions, I propose that overall perceived uncertainty needs to fall below a threshold uncertainty level corresponding to a specific point in time at which the acquisition decision is made. Indeed the notion of a threshold is pervasive in the literature on technology adoption decisions (Jensen, 1982; Doraszelski, 2004; Hoppe, 2002; Alvarez and Stenbacka, 2006; McCardle, 1985) and—on a more general level—strategic decision making (see section 2.2.1). Figure 3 illustrates the uncertainty threshold within the uncertainty profiles as a horizontal line. Timing decisions are usually made with respect to a specific reference point (acquiring before or after a certain point in time). This thesis considers the timing decision in relation to the life cycle stage of the acquisition target at the time of acquisition. For example, Graebner et al. (2010) suggest the completion of a target’s first product or a target’s IPO as potential operationalization of a target’s life cyclebased reference point. Note that a more continuous measures of target maturity, for example in terms of its age (Brueller et al., 2015), are also viable because acquirers might built their timing decisions upon reference points that are highly heterogeneous from one decision to the other and therefore cannot be easily captured by a single, specific reference point. I now apply the notion of a minimum uncertainty threshold for a positive acquisition decision to the profiles of perceived uncertainty in relation to an acquisition target’s maturity in terms of its life cycle at the time of acquisition. Doing so, the above arguments result in the following hypothesis for acquisition timing with respect to performance- and functionality-focused acquisitions: Hypothesis 1a: Ceteris paribus, performance-focused acquisitions take place earlier in target’s life cycle than functionality-focused acquisitions. 3.4.2.2 Moderating effects of target characteristics on acquisition timing decisions In subsection 3.4.2.1, hypothesis 1a proposes a relationship between acquisition timing and acquisition technology type in terms of a performance- or functionalityfocus. Deal and target characteristics may moderate this relationship39. Thus, it is the objective of this sub-section to generate hypotheses for any effects that combine || 39 In principle, also acquirer characteristics could be part of a moderating effect. However, careful analysis showed that no such effects are to be expected. Hence, acquirer characteristics are not considered here.

Derivation of hypotheses | 55

the framework of performance- and functionality-focused acquisitions with deal and target characteristics. The precondition for any moderating effect is that it acts differently on acquisition timing for performance- than for functionality-focused acquisitions. Therefore, deal and target characteristics need to address any aspect of performance- or functionality-focused acquisitions that distinguishes one from the other. I propose that the distinction between domestic (acquirer and target are located in the some country) and non-domestic acquisitions is a likely moderator. If an acquisition is domestic, the information asymmetry between target and acquirer is reduced (Chakrabarti and Mitchell, 2013) which, in turn, lowers perceived market uncertainty (see section 3.4.1). An acquirer that is located in the same country as the target can more easily and with higher confidence assess information about a target’s existing (test-)customers. As market uncertainty is higher for functionalityfocused acquisitions than for performance-focused acquisitions (see section 3.4.1) I expect this effect to influence functionality-focused acquisitions more strongly than performance-focused ones. I expect that there is likely no or only little impact of an acquirer’s and target’s co-location in the same country or not on technology uncertainty because technology is virtually universal across countries and an assessment is also possible from the distance. As established in subsection 3.4.2.1 perceived uncertainty drives acquisition timing. Following this line of reasoning, I hypothesize: Hypothesis 1b: Ceteris paribus, the difference in acquisition timing between performance-focused and functionality-focused acquisitions is smaller for domestic than for international acquisitions. From the perspective of target characteristics, I expect that the distinction whether a target’s founders have been involved in scientific research and therefore are at the forefront of technology development should play a moderating role in acquisition timing. There are likely two mechanisms at play. The first mechanism holds that founders’ involvement in scientific research may reduce technology uncertainty. This effect, however, is likely the same for performance- and functionality-focused acquisitions so that no moderation can be inferred. According to the second mechanism, founders’ activity in scientific research assigns the acquisition a stronger acqui-hire component. An acqui-hire is an acquisition that focuses more strongly on buying a team of highly trained and skilled engineers than on finished products (Coyle and Polsky, 2013; Chatterji and Patro, 2014). As products are of little relevance, acqui-hires typically involve early stage firms (Coyle and Polsky, 2013). I argue that given the acquisition target’s founders’ involvement in scientific research, the resulting acqui-hire component of an acquisition is increased considerably more for functionality-focused acquisitions than for performance-focused ones. This is because performance-focused acquisitions have an inherent acqui-hire component as technology and team that are transferred in the acquisition are more likely to be used for improving an acquirer’s core products. Especially in the case of

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functionality-focused acquisitions where uncertainty is higher, acquirers may view the acquisition of skillful, scientifically trained engineers as a fallback option for creating value if the newly gained functionality fails to achieve customer acceptance. Based on these arguments, I predict: Hypothesis 1c: Ceteris paribus, the difference in acquisition timing between performance-focused and functionality-focused acquisitions is smaller if the target’s founders were involved in scientific research.

3.4.3 Deal value with respect to performance- and functionality-focused acquisitions As explained in section 2.2.3, an acquirer’s decision on the purchase price or deal value is important because of its impact on value capture. In very general terms, it is driven by a multiple factors such as information asymmetry (Coff, 1999), signs of quality such as patents (Grimpe and Hussinger, 2007), level of competition, interorganizational linkages with e.g., interlock partners40 (Haunschild, 1994), toeholds (Toxvaerd, 2008; Bulow et al., 1999), agency problems caused e.g., by seeking “white knights” (Toxvaerd, 2008; Bulow et al., 1999), bargaining power of the acquirer (Alvarez and Stenbacka, 2006), merger booms (Shelton, 2000), managerial hubris (Roll, 1986), a buyer’s related or unrelated expertise (Coff, 1999) and simply the number of bidders (Adkisson and Fraser, 1990). These drivers may affect any acquisition independent of its motivation. This thesis, however, proposes that based on the dichotomy of performanceand functionality-focused acquisitions acquisition deal values differ systematically. The main reason for this lies in the distinct uncertainty profiles of both types of acquisitions (see section 3.4.1). A wide body of literature reports that uncertainty drives bidding behavior in general (De Silva et al., 2008; Goeree and Offerman, 2002; Goeree and Offerman, 2003; Seres, 2014; Milgrom and Weber, 1982; Fatima et al., 2005) and in particular with respect to acquisition bids (Alvarez and Stenbacka, 2006; Ransbotham and Mitra, 2010). The objective of this section is to derive hypotheses on deal value contingent on the dichotomy of performance- and functionality-focused acquisitions. This is done in four steps. First, I will argue that an auction model is a suitable setting for an analysis of deal value differences. Second, I will introduce the three core elements of auctions, common value, private value and uncertainty. Third, I will explain for each of the three elements how they differ with respect to performance- and functionality-focused acquisitions. Fourth, I will derive hypotheses for deal value. Steps

|| 40 Two firms are interlock partners in terms of a director interlock if the manager of one firm sits on the board of another firm (Haunschild, 1994).

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one and two are described in subsection 3.4.3.1 while steps three and four follow in subsection 3.4.3.2. The third step is done considering the pre-market and postmarket entry stages of an acquisition target separately because timing influences deal value (Ransbotham and Mitra, 2010). Given the discussion in section 3.4.2, market entry as reference point is suitable because uncertainty drops significantly post market entry. While using an acquisition target’s market entry as reference point creates a simpler argument for the purpose of hypothesis derivation, it is not obligatory, as the uncertainty profiles of performance- and functionality-focused acquisitions are derived for a continuous period of time. Hence, I will later on relax the strict conditioning on pre- and post-market entry stages. Please note that the objective is not to determine the deal value of an individual performance-focused or functionality-focused acquisition in absolute terms. Instead the focus is on systematic deal value differences. These are expected to occur across an entire population of deals, where performance- and functionality-focused acquisitions represent the only two sub-populations. 3.4.3.1 An auctions framework for evaluating deal value An auction framework (Milgrom, 1989) is particularly well suited for studying deal value in technology-focused acquisition41. Toxvaerd (2008) states that following Delaware law, the management of an acquisition target is required to behave “as an auctioneer charged with getting the best price for the stock-holders at a sale of the company.” Ransbotham and Mitra (2010) present four arguments for employing an auction framework specifically for technology-focused acquisitions in a high uncertainty environment. First, a large number of startups in such an environment view trade-sales as attractive and attempt to ascertain a suitable selling price. Second, the environment “simulates” that of an auction due to the presence of many acquirers seeking technology-focused acquisitions. Third, the auction model incorporates relevant characteristics of acquisitions such as uncertainty and reserve price. Fourth, the research efforts on auctions have produced a rich set of analytic tools. The literature on auctions commonly distinguishes private and common value auctions (Goeree and Offerman, 2002; Milgrom, 1989). A typical literature example of a private value auction is the sale of a painting (Goeree and Offerman, 2002) where each bidder is assumed to be driven purely by own taste. In this setting, a particular bidder knows her private value with certainty but is uncertain about other bidders’ valuations. Kagel and Levin (1986) define a common value auction as a situation where “the value of the auctioned good is the same [i.e., common] to all bidders” but unknown at the time of bidding. An example are oil lease auctions

|| 41 Note that real options represent another method of analyzing deal value decisions in acquisitions (Alvarez and Stenbacka, 2006).

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(Kagel and Levin, 1986; Kagel and Levin, 2014) where the volume of available oil constitutes the common value (Boatwright and Kadane, 2010). However, real world settings are characterized by auctions with a mix of common and private values (Goeree and Offerman, 2002; Goeree and Offerman, 2003; Boatwright and Kadane, 2010). An example is bidding for a painting where personal pleasure is the private value element and investment and potential resale the common value element (Kagel and Levin, 1986). Laffont and Vuong (1996) even argue that it is impossible to distinguish private and common value auctions via economic theory. Acquisitions contain elements of both common and private value auctions (Dittmar et al., 2012; Bulow et al., 1999; Ransbotham and Mitra, 2010). The common value in acquisitions can be crudely approximated by what a financial investor would pay (Bulow et al., 1999) who lets the target grow standalone and sell its product independently. Ransbotham and Mitra (2010) argue that common value includes a target’s growth options that are “within the normal business operations of the target and that can be effectively exploited by the target in due course”. Common value increases over time as the target increases in size and scale and realizes some of its growth potential (Ransbotham and Mitra, 2010). The private value of an acquisition auction captures the concept of synergistic fit via unexplored growth options that can only be realized based on the union between target and acquirer (Ransbotham and Mitra, 2010). In a first approximation, a bidder’s valuation is simply the sum of common and private value (Goeree and Offerman, 2002). Missing in this first approximation, however, are two effects, uncertainty (De Silva et al., 2008; Goeree and Offerman, 2002; Goeree and Offerman, 2003; Seres, 2014; Ransbotham and Mitra, 2010) and a target’s flexibility in its growth options (Ransbotham and Mitra, 2010). Uncertainty about the common value in a bid reduces valuation (Goeree and Offerman, 2003; Seres, 2014; Milgrom and Weber, 1982; Fatima et al., 2005). As established in section 3.4.1, uncertainty about a target decreases over time. Flexibility reflects an acquirer’s option “to develop the target in unique ways that exploit its private capabilities” (Ransbotham and Mitra, 2010). Ransbotham and Mitra (2010) argue that flexibility just like uncertainty, decreases over time due to maturation of technology, more entrenched competition and entrenched target processes and practices. This thesis proposes that the effect of flexibility can effectively be treated like an uncertainty that an acquirer has about the private value of the target. This makes sense, if one associates flexibility with integration uncertainty. Hence, both the uncertainty about the common value and the uncertainty about the private value can combined into an uncertainty discount. This combined term reduces an acquirer’s bid (Ransbotham and Mitra, 2010). Indeed, risk and uncertainty have been found to lead to a discount in valuation (Koeplin et al., 2000; Capron and Shen, 2007; Akerlof, 1970).

Derivation of hypotheses | 59

3.4.3.2 Hypotheses on deal value For the present thesis, it is relevant to understand if there are any systematic deal value differences between performance- and functionality-focused acquisitions and if these change over the target’s life cycle. As noted before, common value, private value and uncertainty discount are to be understood as calculated across an entire population of deals. Hence, in mathematical terms common value and private value refer to the expected value of common value and private value, each. The uncertainty discount is related to the variance across both. The expected value of common and private value is unknown but assumed to be the same for performance- and functionality-focused acquisitions at the same level of target maturity. There is no reason to believe that either common or private value of both types of acquisitions differ systematically. The acquirer has by definition a clear strategic rationale for both performance- and functionality-focused acquisitions making them likely equally valuable on average at a specific level of maturity. In the pre-market stage, i.e., before an acquisition target’s first introduction of a product to the market, common value of both performance- and functionalityfocused acquisitions is low, particularly if a product’s prototype has not been created, yet. Therefore, private value and uncertainty discount are the dominant factors driving deal value differences. Given that no systematic differences in private value are expected, any systematic differences in deal value are left to the uncertainty component. I argue that the perceived uncertainty profiles (section 3.4.1) are relevant for both common and private value. Recall that market uncertainty generally addresses the question whether customers will like a specific product. Technology uncertainty is roughly based on the question if a specific technology will work. According to this rather broad framing, uncertainty clearly affects common value. However, relevant questions are also if the acquired technology will work well enough for a specific acquirer and whether that acquirer’s customers will like the resulting product. This aspect of uncertainty clearly relates to synergy and therefore private value. The derivation of the perceived uncertainty profiles above relate to both the common and private value aspects of uncertainty. Overall uncertainty is lower for performance-focused acquisitions than for functionality-focused ones in the pre-market stage resulting in a lower bid discount for performance-focused acquisitions. Once products are introduced to the market, the common value rises sharply for both performance- and functionality-focused acquisition targets (Granstrand and Sjölander, 1990). As stated before, there is no reason to believe that private value is systematically different for both performance- and functionalityfocused acquisitions due to their strategic nature. Looking at the perceived uncertainty profiles in an acquisition target’s post market entry phase, perceived uncertainty is lower for performance-focused acquisitions than for functionality-focused acquisitions—yet less so the more time has passed since market entry. In conclusion, the effect of uncertainty leads to a bid discount for functionality-focused acquisitions in both pre-market and post-market entry stages. Private and common

60 | Performance- and functionality-focus in product development and acquisitions

value are expected not to have a systematic difference for each type of acquisition in either stage. Based on these arguments, I predict: Hypothesis 2a: At comparable levels of target maturity (e.g., pre-market entry or post-market entry), the deal value of performance-focused acquisitions is, ceteris paribus, higher than that of functionality-focused acquisitions. Now I relax the condition of comparing performance- and functionality-focused acquisitions either in the pre-market or in the post-market entry stages. Consequently, acquisition timing needs to be taken into account. As I expect—on average—that performance-focused acquisitions take place earlier in a target’s life cycle (hypothesis 1a), a comparison across pre-market and post-market stages is warranted. According to hypotheses 2a, deal value of performance-focused acquisitions is expected to be on average higher in both phases. However, I argue that in absolute terms the common value component is much higher for functionality-focused acquisitions in the post-market entry stage than for performance-focused acquisitions in the pre-market stages. This effect should more than compensate for the systematic differences in the uncertainty discount since uncertainty that is related to variance is likely a second order effect as compared to common value or more precisely the expected value of common value. Hence, I hypothesize: Hypothesis 2b: Overall, the deal value of functionality-focused acquisitions is, ceteris paribus, higher than that of performance-focused acquisitions.

3.5 Discussion of theoretical findings and conclusion The purpose of the chapter was to provide a theoretical framing for studying the heterogeneity originally observed in the acquisitions of XtremIO and Syncplicity by EMC in 2012. This was achieved in two steps. First, a framework of performance- and functionality-focused innovations was established and characterized using mainly insights from the literature on product development, product quality and marketing. Both performance- and functionality-focused innovations were defined as inherently driven by product performance improvement and product functionality addition as strategic rationales. Especially the literature streams on product development, quality, and marketing distinguish product performance and product functionality from which the respective types of innovations are derived. In this context, the marketing literature studies the impact of product performance improvements and product functionality additions on consumer preferences. Second, the framework of performance- and functionality-focused innovations was transferred to the realm of technology-focused acquisitions via the make-or-buy decision. The result is a novel and, hence, previously unstudied framework of performance- and functionalityfocused acquisitions. According to this framework, EMC’s acquisition of XtremIO falls into the category of a performance-focused acquisition and that of Syncplicity into that of a functionality-focused one. Given how EMC’s two focal acquisitions fall

Discussion of theoretical findings and conclusion | 61

into these distinct categories and were treated distinctly in decision making, it stands to reason that this framework is suitable for studying differences in acquisitions such as target maturity at the time of acquisition and deal value. The literature on acquisitions provides relevant insights characterizing technology-focused acquisitions in general as one type of acquisitions within a larger typology. It focuses largely on the questions of acquisition performance. From a decision making perspective, especially decisions regarding the integration of acquisition targets have been studied most prominently. Much attention in product development literature has been given to product development effectiveness and performance. In relation to product development strategy, there are many typologies distinguishing types of innovations from which implications can be drawn regarding the best way of dealing with them. Nevertheless, extant literature suffers from a number of shortcomings, particularly in relation to integrating product development and technology-focused acquisitions, studying acquisitions form the perspective of actual motivations. Established typologies in product development and innovation management are often expost descriptions of innovation and product development outcomes. A strategic, exante perspective is largely missing. While the make-or-buy-decision has received significant attention in the literature only few authors (see e.g., Chaudhuri (2005)) have taken an integrative view where technology-focused acquisitions are considered as truly embedded in the product development process. Thus, most studies fail to incorporate the fact that the characteristics of a specific product development effort such as uncertainty considerations or the strategic rationale carry over to technology-focused acquisitions. Regarding the strategic rationale, notable exceptions are the studies on component vs. standalone products (Puranam et al., 2009) and, to a lesser extent, complementarity vs. compatibility (Léger and Quach, 2009). This is despite the call to look deeper into individual acquisition motives in order to resolve the acquisition performance paradox42 (Angwin, 2007). Acquisition decision making with respect to decisions that take place before the announcement of an acquisition are understudied (Yu et al., 2016). Acquisition timing together with deal value has, to my knowledge, only been the focus of few studies (Alvarez and Stenbacka, 2006; Ransbotham and Mitra, 2010). The mechanisms that drive timing decisions have been explored mainly theoretically (Alvarez and Stenbacka, 2006; Ransbotham and Mitra, 2010) or quantitatively (Brueller et al., 2015; Warner, 2006) so that a qualitative foundation is lacking. In addition, to my knowledge, only Warner (2006) explicitly studies acquisition timing as the dependent variable of a quantitative analysis. Thus, the setting of technology-focused ac-

|| 42 Recall that the acquisition performance paradox highlights the apparently illogical phenomenon that the number of acquisitions grows while failure rates as determined by numerous authors remain to be high (Angwin, 2007).

62 | Performance- and functionality-focus in product development and acquisitions

quisitions that have specific strategic rationales combined with the study of acquisition timing and deal value as inter-related decisions is truly unique. I expect it to shed light on the complex and partially hidden decision making processes in acquisitions. The present thesis attempts to help address the above-mentioned gaps and shortcomings. The framework of performance- and functionality-focused acquisitions is utilized to gain a deeper understanding of acquisition timing and deal value decisions. For this purpose, five hypotheses—three on acquisition timing and two on deal value—have been derived. In addition, the derivation of the framework of performance- and functionality-focused acquisitions and of the hypotheses brings together numerous streams of literature with hitherto only limited connections. In conclusion, there are unanswered questions regarding acquisition timing and deal value in connection to product performance improvement and functionality addition as acquisition motives. What are the mechanism driving acquisition timing and deal value decisions? How do a performance- and functionality-focus of acquisitions influence these mechanisms? What other factors are relevant? What are key characteristics of performance- and functionality-focused acquisitions? Is this distinction already meaningful prior to an acquisition? The literature provides preliminary answers to these questions. Conducting both a qualitative and quantitative study will be the appropriate course of action for further exploring these and other questions to create a well-rounded and deep understanding.

4 Qualitative Study—Acquisitions in the ICT Industry The objective of this chapter is to address the research questions posed in section 1.2 from a qualitative perspective with a focus on the ICT industry. Concretely, this means—first—providing evidence for and characterizing the distinction of performance- and functionality-focused acquisitions. Second, potential differences in acquisition timing and deal value in regards to this distinction and the underlying mechanisms causing them are to be understood. In other words, the focus is on the how and why of timing and deal value decisions. Close attention is paid to the phenomenon of risk and uncertainty as theory predicts its importance in relation to performance- and functionality-focused acquisitions and decision making. This chapter’s research benefits from the theoretical considerations developed in the previous two chapters (2 and 3) and extends them. Section 4.1 describes the methodology applied to this thesis’ qualitative research with a focus on research design (section 4.1.1), sampling approach (section 4.1.2), data description, collection and analysis (section 4.1.3) and finally overall robustness of the chosen design (section 4.1.4). Section 4.2 presents the qualitative results. The concept of performance- and functionality-focused acquisitions is analyzed in section 4.2.1. Uncertainty and risk of technology-focused acquisitions in ICT is the focus of section 4.2.2. The results on the decisions of acquisition timing and deal value are described in sections 4.2.3 and 4.2.4, respectively. Finally, section 4.3 discusses the qualitative results in the light of theory.

4.1 Methodology 4.1.1 Research design The qualitative research presented here aims at connecting the theoretical arguments developed in chapter 3 to the actual behavior of practitioners. Thereby it challenges, qualifies and augments theoretical findings. Prior literature dealing with timing and deal value decisions in technology-focused acquisitions is scarce and there is no literature on these decisions incorporating the novel distinction of performance- and functionality-focused acquisitions. Hence, the generation of theory via qualitative research is warranted (Edmondson and McManus, 2007; Eisenhardt, 1989; Merriam, 2009) and the best methodological fit (Bansal and Corley, 2011). The design of this qualitative research builds chiefly upon grounded theory (Glaser and Strauss, 1967) and borrows elements from case study research (Yin, 2003)—despite not conducting a formal case analysis. Grounded theory is a research

DOI 10.1515/9783110562095-004

64 | Qualitative Study—Acquisitions in the ICT Industry

strategy specifically contrived to generate theory that is grounded in the data (Glaser and Strauss, 1967; Merriam, 2009; Punch, 1998). A rich description of data is an important element of grounded theory. The result of grounded theory analysis is usually a substantive theory (Glaser and Strauss, 1967; Merriam, 2009) that is a special case of so-called “middle-range” theory as opposed to a grand theory (Glaser and Strauss, 1967). Substantive theory is characterized by its embeddedness in an empirical context—such as technology-focused acquisitions. It is specific and brings a high utility to practice (Merriam, 2009), which is one of the goals associated with studying the distinction of performance- and functionality-focused acquisitions. Process questions are aptly studied following a grounded theory approach. Case study research is especially fitting to study how and why questions (Eisenhardt and Graebner, 2007; Pratt, 2009; Yin, 2003; Eisenhardt, 1989). Indeed, a focal element of case study research is to explore decision making and its outcomes (Yin, 2003). Thus, both grounded theory and case study research are well suited tool sets that this thesis borrows from. Case study research differentiates itself from other types of inquiry by examining the dynamics of a contemporary phenomenon (Yin, 1981; Yin, 2003; Eisenhardt, 1989) without abstracting from the real-life context which it is part of (Gibbert et al., 2008; Yin, 1981; Yin, 2003). Grounded theory and case study research both incorporate a multitude of data sources such as interviews, observations, and documents (Merriam, 2009; Yin, 2003). Thus, interviews enriched with secondary data are well suited for the study of acquisitions and acquisition decision making. The research design presented here involves multiple investigators, which has the advantages of providing novel or complementary insights, added creativity, and confidence (Glaser and Strauss, 1967; Eisenhardt, 1989). Overall, qualitative research is regarded highly for providing deep insights at different levels of analysis and the proximity to the phenomenon (Bansal and Corley, 2011). A methodological orientation on grounded theory is fitting because it is explicitly dedicated to building theory from qualitative data (Döring and Bortz, 2016) and has been characterized as the “most widely employed interpretive strategy in the social sciences today” (Denzin and Lincoln, 2005). Building theory borrowing elements from case study research is appropriate because it is considered interesting and influential (Eisenhardt and Graebner, 2007; Gibbert et al., 2008). Thus, the proposed research design is clearly well suited for examining decision making in the context of performance- and functionality-focused acquisitions and serves as a perfect antecedent to the quantitative study below (see chapter 5).

4.1.2 Sampling Proper sampling is highly relevant for research focused on building theories (Eisenhardt, 1989). The interview partners selected for this study need to be “excellent participants [in a phenomenon of study] to obtain excellent data” [italics added]

Methodology | 65

(Bryant and Charmaz, 2007). This thesis follows a purposeful sampling approach (Döring and Bortz, 2016; Gentles et al., 2015; Patton, 1990; Pratt, 2009) to identify interview partners with rich information and high relevance (Yin, 2011) in relation to decision making within the framework of performance- and functionality-focused acquisitions. It invokes elements from theoretical sampling (Charmaz, 2006; Glaser and Strauss, 1967) to benefit from its advantage of being tailored to maximizing the theoretical contribution (Döring and Bortz, 2016). Theoretical sampling is a highly iterative sampling approach commonly employed in grounded theory research. Data analysis guides researchers to new elements for their sample with the aim of fulfilling a specific theoretical purpose (Strauss, 1987). Similar to the process of moving from initial sampling to theoretical sampling (Charmaz, 2006), the initial purposefully collected sample of this study was augmented by new elements that were expected to shine light on rather obscure but likely relevant aspects of theory. There are three steps to the sampling approach of this thesis. First, out of a broad spectrum of potentially relevant industries one industry was selected. Second, within the focal industry individual firms were chosen and suitable interview partners were identified. Third, based on an initial analysis of qualitative data, additional firms along with interview partners were selected to pursue specific categories within the emerging theory. In the first step, using data from the M&A databases Crunchbase and ThomsonOne a set of six industries (ICT, biotech & pharma, semiconductors, telecommunication, medical devices and aerospace & defense) was identified based on two criteria, technology intensity and acquisition frequency. The industry was required to have a high technology intensity (Hatzichronoglou, 1997; Hecker, 2005; OECD, 2011) to ensure that a considerable share of acquisitions are done for the purpose of external technology sourcing. Acquisition frequency measured through the number of acquisitions within previous years needed to be sufficiently high to maximize the chance of identifying and gaining access to information rich acquisition situations (Patton, 1990). All of the above industries have previously been the subject of inquiry in literature on technology-focused acquisitions (Brueller et al., 2015; Cloodt et al., 2006; Valentini, 2005; Wagner, 2008; Warner, 2006; Yu et al., 2016). The set of six industries was narrowed down to ICT with a focus on software as the focal industry due to a particularly high frequency of acquisitions and the good availability of acquisition announcements, accounts and analyses in popular media43 or analyst reports. More importantly, the ICT industry should provide rich evidence of performance- and functionality-focused acquisitions with higher clarity than other industries such as biotech and pharma or aerospace and defense for two reasons. First, the ICT industry features system products that are modular (see e.g., MacCormack, 2001) and hence allow the replacement or addition of individual components—

|| 43 Examples: TechCrunch (https://techcrunch.com/) or Gigaom (https://gigaom.com/)

66 | Qualitative Study—Acquisitions in the ICT Industry

parallels to the definition of performance- and functionality-focused acquisitions are apparent. The phenomenon of modularity is certainly more difficult to observe within the drugs of the biotech and pharma industry. Second, the ICT industry is much more transparent regarding past and recent developments and changes as evidenced by the open source movement, which is in stark contrast to other industries such as the notoriously secretive aerospace and defense industry. For the selection of individual firms and interview partners in the second step, a number of strategies exist (Pettigrew, 1990). A prominent one is focusing on extreme situations (Eisenhardt and Graebner, 2007; Pettigrew, 1990) ideally combined with a high level of experience (Pettigrew, 1990). Extreme situations are those where a phenomenon is transparently observable (Pettigrew, 1990). In the present thesis, serial acquirers within the ICT industry, specifically the software industry, were selected because high levels of experience are evident and since there is a high likelihood of extreme situations due to the frequency of acquisitions. Consistent with extant literature (Brueller et al., 2015), I define serial acquirers as those companies with 20 or more acquisitions within the previous 10 years. Serial acquirers are particularly well suited study objects for two more reasons. First, due to their standardized routines for their M&A activities (Brueller et al., 2015) a common logic should govern all of their acquisitions making their M&A decisions somewhat comparable with each other. Second, due to their breadth of acquisitions, the phenomenon of performance- and functionality-focused acquisitions should be readily observable. In the third step, the purposefully created sample consisting of serial acquirers of the first two steps was enhanced following an initial analysis of the data. Especially the perspective of acquired firms and non-acquired firms was added to the sample to enrich theoretical categories. In this context, constellations in which a suspected performance focus coincided with a particularly late acquisition provided valuable insights for theory building. These situations bear some resemblance to “polar types” (Pettigrew, 1990), i.e., constellations that seem to disconfirm preliminarily established patterns (Eisenhardt and Graebner, 2007; Pettigrew, 1990; Eisenhardt, 1989). The present qualitative research encompasses a set of 21 interviews in total that correspond to the study of 20 particular firms. Each firm has either conducted multiple acquisitions (serial acquirers) or experienced one acquisition (acquired firm). Two firms had not been acquired at the time of the interview. At least one of these firms had already received an informal acquisition offer. Table 2 provides an overview of the interviewees, the associated firms, and additional information. Note that interviews I 02 and I 03 were conducted within the same firm but with managers that had different roles. The interview partners presently have or recently had leading roles at the focal firms. For serial acquirers these roles are in all but one case VPlevel or SVP-level with a responsibility in the field of M&A and for acquired and nonacquired startups these roles are at the highest management level (C-level). All of the firms operate within the ICT industry offering software products and services

Methodology | 67

while few also offer hardware products. Most of them sell their products to other businesses (B2B). Serial acquirers (interviews I 01–I 11) were ~4,000–80,000 employees in size and had conducted ~20–70 acquisitions in the timeframe 2005–2014. Hence, the observed timeframe covers multiple software product generations where product functionality additions and product performance improvements become especially relevant. Most serial acquirers conducted both, performance- and functionality-focused acquisitions with the majority typically being functionalityfocused. Acquired and non-acquired startups had ~5–100 employees and were 0–13 years old at the time of acquisition. Two of them were acquired for the purpose of adding new functionality, four to improve product performance, one was a mixture and another one was a pure acqui-hire (Coyle and Polsky, 2013) aimed at basic research. Interviews and secondary data suggest that an acquisition of the two nonacquired firms would have most likely been for adding new functionality. Exemplary product performance improvements enabled through acquisitions cover dimensions such as communication latency, connection stability, input/output speed, and output relevance. Functionality additions include electronic signatures, big data analytics, and data visualization. Some of the interviews dealt with acquisitions in the realm of artificial intelligence technology where both product performance parameters (primarily better accuracy and speed) and added product functionality (e.g., image recognition capabilities) play an important role. In addition, at least one, possibly two, merger waves fall into the timeframe of inquiry (Ahern and Harford, 2014). Thus, the background against which firms were selected and the firms themselves provide a rich setting for the study of performance- and functionality-focused acquisitions and of timing and deal value decisions with respect to these two types of acquisitions. There is no straightforward answer regarding the proper number of firms or interviews for qualitative research (Merriam, 2009). Mason (2010) states that purely qualitative doctoral theses typically include 20 to 30 interviewed participants in their sample. Hypothetically viewing each firm as one case, the literature on case study research provides some additional benchmarks. Eisenhardt (1989) recommends four to ten cases for reasons of complexity, which is similar to Yin (2003) who speaks of six to ten cases. Even the extremes of less than four or more than 15 cases are not uncommon (Gentles et al., 2015). Theoretical sampling as used in grounded theory is not concerned with a specific size of the sample but with theoretical saturation. Adding data to the sample stops when theoretical categories saturate (Charmaz, 2006; Strauss and Corbin, 1998), or—in other words—when the marginal utility of one more unit of data approaches zero. Purely looking at these benchmarks, the number of 21 interviews within 20 firms of this thesis appears to be appropriate. Theoretical saturation was chosen as a stopping criterion in this thesis.

68 | Qualitative Study—Acquisitions in the ICT Industry

Tab. 2: Overview of interviews, interview partners, and firms

Interview

(Former) interviewee role

Firm role Industry

Profile

I 01 HHead of M&A

Serial Enterprise acquirer software– general

~80,000 employees Europe (location) € ~20b revenue

~60

–

P,F

I 02

Head of M&A integration

Serial Enterprise acquirer software– design

~8,000 employees US (location) $ ~3b revenue

~70

–

F only

I 03

Head of M&A

Serial Enterprise acquirer software– design

~8,000 employees US (location) $ ~3b revenue

~70

–

F only

I 04

Head of M&A

Serial Enterprise acquirer software– business process management

~4,000 employees Europe (location) € ~1b revenue

~20

–

P,F

I 05

Head of M&A

Serial Enterprise acquirer software/hardware–data management

~60,000 employees US (location) $ ~20b revenue

~60

–

P,F

I 06

Head of M&A

Serial Enterprise acquirer software– design

~14,000 employees US (location) $ ~5b revenue

~30

–

F only

I 07

Head of M&A

Serial Enterprise acquirer software– virtualization/cloud

~10,000 employees US (location) $ ~3b revenue

~30

–

P,F

I 08

Head of M&A

Serial Enterprise acquirer software– design

~14,000 employees Europe (location) € ~3b revenue

~30

–

F only

I 09

Head of M&A

Serial Enterprise/conacquirer sumer software

~14,000 employees US (location) $ ~2b revenue

~50

–

P,F

I 10

Head of M&A

Serial Software– acquirer e-commerce

~34,000 employees US (location) $ ~9b revenue

~40

–

P,F

I 11

Head of M&A

Serial Software–social ~16,000 employees acquirer networking US (location) $ ~18b revenue

~60

–

P,F

I 12

CTO

Acquired Enterprise

–

2013

F

~70 employees

# of Year Type of acquisi- acquired acquisitions1 (age in tions2 years)

Methodology | 69

Interview

(Former) interviewee role

Firm role Industry

startup

Profile

# of Year Type of acquisi- acquired acquisitions1 (age in tions2 years)

software– US (location) business intelli- Founded in 2003 gence

(10)

I 13

Founder, Acquired Enterprise COO startup software– data exchange

(# emp. unknown) Europe (location) Founded in 1999

–

2012 (13)

P

I 14

Founder, Acquired Enterprise CEO startup software–AI

~30 employees Europe (location) Founded in 2005

–

2013 (8)

F

I 15

Founder, Acquired Software–AI CEO startup

~5 employees Europe (location) Founded in 2014

–

2014 (0)

(Acquihire)

I 16

Founder, Acquired Enterprise CEO startup software– AI

~30 employees Europe (location) Founded in 2010

–

2014 (4)

P,F (mix)

I 17

Founder, Acquired Enterprise CEO startup software– AI

~20 employees US (location) Founded in 2006

–

2013 (7)

P

I 18

Founder, Acquired Enterprise CEO startup software– search

~5 employees Europe (location) Founded in 2006

–

2011 (5)

P

I 19

Founder, Acquired Enterprise CEO startup software– AI

~10 employees Europe (location) Founded in 2006

–

2014 (8)

P

I 20

Founder, NonEnterprise ~100 employees CEO acquired software– US (location) startup business intelli- Founded in 2009 gence

–

–

–

I 21

Founder, NonEnterprise ~30 employees CEO acquired software– US (location) startup business intelli- Founded in 2008 gence

–

–

–

1 Number of acquisitions conducted in years 2005—2014 (Source: ThomsonOne) 2 P–performance-focused; F–functionality-focused

4.1.3 Data, data collection, and data analysis For the present study, two types of data were collected—interviews and secondary sources—that are typical for qualitative research (Eisenhardt and Graebner, 2007;

70 | Qualitative Study—Acquisitions in the ICT Industry

Strauss, 1987; Yin, 2003; Eisenhardt, 1989). The data collection approach adhered to the quality criteria proposed by Yin (2003) which are use of multiple sources of evidence to enable triangulation, creation of a study database and maintaining a chain of evidence. Interviews were semi-structured and open-ended to obtain facts, opinions, and follow up on emerging themes (Merriam, 2009; Yin, 2003). Main questions were prepared in advance and combined with follow-up questions and probes during the interview (Rubin and Rubin, 2005). Secondary data sources comprise a diverse list of acquisition-related press releases, popular accounts, analyst reports, archived data, especially acquisition targets’ former websites, and data from various M&A databases. The two types of interview partners—M&A managers at serial acquirers and Clevel executives at startups who are in most cases also their founders—were selected because of their expert knowledge on acquisitions. Due to M&A managers’ deep involvement in the entire acquisition process (see section 2.1.2) and especially in the decision making they were expected to be highly knowledgeable (Eisenhardt and Graebner, 2007) and provide insights not only into individual acquisitions but also into patterns that emerge across multiple acquisition events. The choice of startups’ C-level executives is motivated by the interviewees’ deep involvement not only in the acquisition but also in the developments prior to and after the acquisition event. They are expected to be a source of rich information on the technologies of both acquisition target and acquirer, which obviously plays a key role in the study of performance- and functionality-focused acquisitions. Executive interviews from non-acquired firms were included to gain a contemporary and un-biased perspective on technology, technology-based competition and reasons for and against trade-sales. Interviews lasted 19 to 89 minutes44. Tandem researchers conducted 15 interviews, a single researcher did five interviews and one interview consisted of exchanging questions and answers via e-mail. Interviews with serial acquirer executives covered eight acquisitions on average at varying levels of detail. All interviews were recorded and transcribed filling roughly 450 pages of double spaced text. Transcription and coding were performed using F4 and NVivo, respectively. Follow up questions were discussed via e-mail. Interviews took place within the five-month timeframe from June to October 2015 and the three-month timeframe from October to December 2016. Interview questions were framed around the key research questions, i.e., exploring the dichotomy of performance- and functionality-focused acquisitions and understanding the mechanisms underlying timing and deal value decisions. Interviews with acquisition target executives contained additional questions (Eisenhardt, 1989) examining the target’s technology and its development before and after the acquisition.

|| 44 Median interview duration was 48.5 minutes and mean duration was 50 minutes.

Methodology | 71

There are different approaches for qualitative data analysis such as those proposed by Gioia et al. (2012), Eisenhardt (1989) or Glaser and Strauss (1967)45. At the heart of these approaches is typically a highly iterative process involving coding and categorization of data on multiple levels of abstraction, comparisons to identify within group similarities and across-group differences and multiple steps of pattern matching that eventually lead to higher level insights (Eisenhardt and Graebner, 2007; Gioia et al., 2012; Yin, 2003; Eisenhardt, 1989). Interviews were coded with category formation following an inductive approach (Punch, 1998). Themes derived from theoretical considerations provided an initial but fluid ordering. Coding iterations led to re-shaping of individual categories (Mayring, 2014). Pattern matching was facilitated by examining the data from different perspectives (Eisenhardt, 1989).

Acquisition context and motivations Gain understanding of acquisition context in terms of acquirer and target products and technologies, business and market situation Analyze acquisition motives and perform initial categorization (PvF1) Form initial ideas about drivers and mechanisms

PvF1 characteristics and decision-making

High-level themes and mechanisms

Derive PvF1 characteristics and decision making drivers via initial coding of data into emerging categories

Clarify driver categories and their interconnections by iterative recoding of data

Conduct basic abstraction from characteristics and drivers to identify emerging core themes and mechanisms

Refine and elaborate core themes and mechanisms by in-depth pattern matching

1 PvF—performance-focused vs. functionality-focused acquisitions

Fig. 4: Data analysis process of qualitative study

The data analysis process followed three steps (see figure 4). The first step establishes an understanding of the focal acquisitions’ context and underlying motives and the last two create a dynamic understanding of processes and patterns. The steps are (1) acquisition context and motivations, (2) performance- vs. functionalityfocused acquisitions’ characteristics and decision making, and (3) high-level themes and mechanisms. The objective of step (1) is to gain a deep understanding of individual acquisitions in their respective context and thereby establish first ideas of drivers and mechanisms with respect to decision making in performance- and functionality-focused acquisitions. Acquirer and target products and technologies, their

|| 45 For additional approaches, see note (2) in Gioia et al. (2012).

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business situation in terms of, for example, growth, and their market situation with respect to customers and competitors, form the context of acquisitions. For the preparation and review of interviews with serial acquirers all acquisitions within the last 10 years adhering to certain criteria such as technology-focus were analyzed using information from M&A databases46 and other secondary sources such as press releases, news articles or analyst reports. A range of 19 to 114 acquisitions47 was analyzed for each interview. Hence, a well-rounded picture of serial acquirers’ acquisition behavior was created. With respect to interviews with executives of (non)acquired firms, similar data with a greater focus on the individual firms’ context was analyzed. This enabled an initial categorization of acquisitions in terms of performance- or functionality-focus and contributed to first ideas about decisionmaking drivers and mechanisms. Step (2) focuses on delineating the characteristics of performance- and functionality-focused acquisitions and on deriving decision making drivers. Similar to the open coding approach in grounded theory (Strauss, 1987), initial categories were formed from the data in an effort of “fracturing or breaking open the data” (Punch, 1998). Then, categories were sorted into a rough, fluid structure to facilitate distillation of drivers. This structure was still rather complex and obscure featuring seven levels of hierarchy. The method applied is somewhat related to the axial coding process in grounded theory analysis (Strauss, 1987), whereby interconnections between categories are formed. Within this process, further abstraction lead to the emergence of rough core themes and mechanisms. The purpose of step (3) is to create a final set of high-level themes and mechanisms surrounding the concept of performance- and functionality-focused acquisitions. These themes and mechanisms bear some resemblance to the core category within grounded theory analysis that “accounts for most of the variation in a pattern of behavior” (Strauss, 1987). To generate this final set of core themes and methods, categories were iteratively re-named, re-coded and re-organized into a new structure that is built upon the initial, rough themes and mechanisms of step (2). The result of the process employed in step (3) was threefold. First, a clear structure with only four levels emerged that facilitated pattern matching. Second, interconnections between categories were clarified leading to the final set of drivers. Third, in-depth pattern matching within the new structure lead to refined core themes and categories. To test the validity of the final category system and promote further abstraction, short, written syntheses of the data grouped within each category at lowest level48 were created. These cover the core ideas and raise them to a more general perspective. In total, 380 data points across 121 lowest level categories were synthesized in this manner.

|| 46 Crunchbase (www.crunchbase.com) and ThomsonOne (www.thomsonone.com) 47 46 acquisitions on average 48 Levels three and four within the coding structure

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This research took several steps to mitigate retrospective informant bias that is of concern in interview-based research (Golden, 1992; Huber and Power, 1985). For each firm the apparently most knowledgeable manager was identified and interviewed (Huber and Power, 1985) to maximize information quality and breadth. All but six interviews were conducted by tandem researchers to reduce the chance of inadvertently missing out on relevant information and allow for different angles to explore topics (Huber and Power, 1985). All interviewees were granted anonymity to reduce disincentives for responding and maximize accuracy (Huber and Power, 1985). Very importantly, next to interviews multiple other sources were reviewed to facilitate triangulation and enhance overall validity (Golden, 1992). The data analysis approach described above created a broad picture on the phenomena of performance- and functionality-focused acquisitions and acquisition decision making with respect to timing and deal value. Overall, coherent insights could be derived. The discovery of various nuances regarding the delineation of performance- and functionality-focused acquisitions and the drivers of decision making not only shows the richness of the phenomena but also that a sufficient number of perspectives were combined.

4.1.4 Methodological rigor in the qualitative study Methodological rigor in empirical research is commonly assessed using four measures, internal validity, construct validity, external validity, and reliability (Calder et al., 1982; Gibbert et al., 2008; Yin, 2003). Likely the most important measure of rigor for theory building research is internal validity as it directly relates to the quality of inferred causal relations while external validity is least important (Calder et al., 1982). To maximize methodological rigor concerning all measures, several procedures were applied in this thesis. These are explained in the following. Internal or logical validity (Gibbert et al., 2008; Yin, 2003) is provided if causal relationships in terms of “A leading to B” can be established (Calder et al., 1982; Gibbert et al., 2008). It needs to be ensured during the data analysis phase (Yin, 2003). Threats to internal validity were mitigated in the following ways. First, pattern matching in terms of comparing patterns resulting from data analysis to predicted ones that stem from theoretical considerations was conducted (Denzin and Lincoln, 2005; Eisenhardt, 1989). Second, explanation building was performed. Initial propositions about mechanisms were iteratively refined, revised and condensed by constant comparison to establish final explanations (Yin, 2003). Third, a clear research framework derived from literature was used and fourth triangulation using different sources of data was employed (Gibbert et al., 2008). Construct validity refers to the extent to which the operationalization of a concept is adequate (Calder et al., 1982; Gibbert et al., 2008; Yin, 2003) or in other words whether a study “investigates what it claims to investigate” (Gibbert et al., 2008). It

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is chiefly the data collection process that needs to be planned and conducted such that construct validity can be established (Gibbert et al., 2008; Yin, 2003). In this research, construct validity was ensured by establishing a chain of evidence (Yin, 2003), i.e., steps in the research process can be stepped forward and backward. This was achieved by establishing a close and traceable connection between the qualitative research report and the individual pieces of evidence. To raise construct validity further, the relevant study objects under scrutiny need to be carefully selected and a justification why the selected measures for these study objects are meaningful is necessary. In the present thesis, this was achieved by presenting a clear reasoning as to why acquisition deal value and timing decisions are studied relative to the framework of performance- and functionality-focused acquisitions and—with respect to timing decisions—relative to a target’s life cycle at the time of acquisition. If the same research can be replicated by another researcher, reliability is given (Gibbert et al., 2008; Yin, 2003). Hence, transparency especially in the data collection process is important for achieving reliability (Gibbert et al., 2008; Yin, 2003). This thesis achieved reliability in two ways. First, a database with all relevant information on data collection was established. Second, research procedures specifically concerning selection of interview candidates, interview questions and the data analysis process were carefully documented (Gibbert et al., 2008). A seamless connection between high level categories and individual data points that lead to their emergence exists. External validity is equivalent to the generalizability of a study across different settings (Calder et al., 1982; Gibbert et al., 2008). This was ensured by providing a clear reasoning for why specific interview partners were selected and by elaborating on the context in which the focal acquisitions took place (Gibbert et al., 2008). In addition, the number focal firms (20) and interviews (21) should be sufficient for analytical generalization—a concept borrowed from case study research—where the study of four to ten situations is typical (Eisenhardt, 1989). From the perspective of grounded theory research, there were clear indications for theoretical saturation (Charmaz, 2006).

4.2 Results 4.2.1 Performance- and functionality-focused acquisitions in the ICT industry The aim of this section is to deepen the understanding of performance- and functionality-focused acquisitions. To this end, it is first of all necessary to find evidence supporting that the distinction can be made (subsection 4.2.1.1). Once this is achieved, a characterization from different perspectives is warranted (subsection 4.2.1.2): How do acquirers and founders view the distinction? How is it related to acquisition strategy and product development? Beyond product development, does

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the marketing perspective differ? A whole typology of acquisitions has been preconceived (see section 2.1.1). In this context, one needs to understand whether the distinction between performance- and functionality-focused acquisitions differs from existing types (subsection 4.2.1.3). Lastly, a connection to how product performance and functionality play a role in the competition among startups prior to their acquisition is made (subsection 4.2.1.4) to create a well-rounded image of performanceand functionality-focused acquisitions. Key results are summarized in subsection 4.2.1.5. 4.2.1.1 Evidence for performance- and functionality-focused acquisitions In the ICT industry, technology-focused acquisitions follow a pattern of performance- and functionality-focus. The addition of new functionality through acquisition such as the capability of determining the optimal position of a new elevator in a software solution for building planning is a straightforward case. Even though performance dimensions are more intuitively associated with hardware, they appear also in software especially in the context of algorithms. Example performance dimensions are accuracy, latency, precision, recall, speed and throughput. Hence, performance-focused acquisitions in the software industry usually involve the transfer of a better performing algorithm. While functionality-focused acquisitions are common across all sub-industries within the software industry, the occurrence of performance-focused acquisitions depends heavily on the focal sub-industry. In some sub-industries such as multimedia and creativity, performance-focused acquisitions play virtually no role in technology-focused acquisitions while in other sub-industries such as e-commerce performance-focused acquisitions account for a considerable share of total technologyfocused acquisitions. I would say that generally speaking acquisitions happen much more in the area of a new feature than it does in the area of performance. [...] I can’t even think of an example [...] that I would put categorically into the performance dimension. [I 06] [...] I would venture to say right now that there is probably equal or maybe slightly more on the performance side, than on the functionality side. [I 10]

Despite large differences in the prevalence of performance- and functionalityfocused acquisitions, acquirers generally find the distinction is reasonable. However, it is new in the sense that acquirers do not consciously differentiate performanceand functionality-focused acquisitions or, instead, focus more broadly on feature changes because performance improvement or functionality addition on a first glance appear to be similar concepts. Startup founders agree that the distinction is meaningful but highlight that other existing acquisition typologies such as acqui-

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hiring vs. acquiring technology with market traction or acquiring purely for technology reasons vs. acquiring a new marketable product are equally or even more sensible. There are very clear-cut cases of performance- and functionality-focused acquisitions. In these cases, a clear performance dimension for performance-focused acquisition can be stated while the concept of performance seems irrelevant in unequivocally functionality-focused acquisitions. Other cases are mixed in which, for example, a performance-focused acquisition may include some new functionalities as a second order priority for the acquirer. In our case it was definitely performance. [...] With our cloud-based recognition approach, we increased the performance by multiple orders of magnitude. From hundred to tens of millions of images. [I 19] [With the acquired technology] you introduce an element of workflow. [...] that’s like a natural extension of functionality for [product name] [I 07] I think it was more around performance than it was the additional functionality. But the additional functionality was very nice to have. [I 13]

4.2.1.2 Characterization of performance- and functionality-focused acquisitions One possible delineation of performance- and functionality-focused acquisitions is defining a performance-focused acquisition as a “swap out” or a “better mouse trap” and a functionality-focused acquisition as “something new” that is filling a gap. [Acquirer name] has historically done more of [...] the performance improvement, […] well this is the term you use a lot over here is ‘it is a better mouse trap’, right? So they built a better mouse trap. So, we have a mouse trap, they have a mouse trap, but theirs is better. [I 09] [...] it was driven by the fact that we recognized that we had a gap in our portfolio [...] a functionality gap and for that we need a certain technology. [I 04]

While these definitions have some merit due to their intuitive nature, they face difficulties when confronted with situations that are more complex or when certain ways of rationalizing a specific categorization are applied. Concretely, these difficulties arise for three reasons, i.e., because the phrase “improving a product” has an ambiguous meaning, product performance improvements can enable new functionalities and lastly because the effects of some technologies are difficult to categorize within a performance and functionality framework. First, if a technology-focused

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acquisition results in a better product, this improvement can have its roots in a performance improvement or a new functionality. Second, if due to improvements, product performance surpasses a certain threshold, new functionalities are enabled that previously seemed impossible. Hence, it is difficult in such a case to distinguish if an acquisition is performance-focused with the enablement of new functionalities serving the purpose of an add-on or whether the enablement and, thereby, addition of new functionalities is the central aspect of an acquisition. Third, categorization is virtually impossible, when framing end-user experience as a performance dimension since an improvement of end-user experience can be caused by a purely functionality-focused acquisition as well as a performance-focused one (see section 3.2.2). This issue can be circumvented by focusing on technology-oriented performance dimensions from an end-user perspective. It is difficult, however, to categorize some technology-based improvements such as better security, flexibility or inter-operability whose benefit may affect end-users only indirectly. Additional reasons for confusion are that the concept of a functionality-focused acquisition does not distinguish between extending an existing product or adding a new product to an acquirer’s portfolio and that the technology behind both performance- and functionality-focused acquisitions can be integrated into one or several core products of an acquirer. While these issues make classification difficult, remedies do exist as discussed in section 3.2.2. Complex situations are not the rule as described earlier in relation to clear-cut cases. Performance- and functionality-focused acquisitions are usually the results of strategic decision making within the product development process. The make-orbuy decision establishes the link between product development and both types of acquisitions. Target selection criteria are adapted to meet the specific strategic gap and performance or functionality are communicated as acquisition rationales. [...] always we try to tie acquisition to strategy and strategic gaps and so we do a formal strategy review session every year and there’s this part where you talk about where you stand today and then where the business needs to be in three to five years and then they do an assessment of the gaps they have in order to meet that three to five year plan. And it’s typically during that process or closest to that process that we come up with a bunch of acquisition ideas and themes. And then the characteristics we are looking for typically will match the strategy [...] [I 03] [...] everything starts with the strategy of the business unit. So each of those business units that I described has its own strategy and we work very closely with them to develop these strategies. And then we try to help them identify, build, buy, and partner opportunities to satisfy those strategic objectives. And in many cases it ends up being a buy opportunity, an acquisition opportunity. [I 05] Yes the argument [...] was, that [acquirer name] said, ‘we need to—for various reasons— improve our search drastically.’ And the question was, as usually, make or buy. And the makeoption was discarded rather quickly because they did not have either the capacity or the com-

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petency [...] to raise that to a whole new level. And therefore they acquired a new core technology [for their search engine]. [I 18] Interviewer: Did [acquirer name] communicate ‘performance improvement’ as the strategic rationale for the acquisition to you as founders? If not, what was communicated instead? Interviewee: I don’t remember the exact wording, but I think it was something like that. [I 19]

The categorization of an acquisition as performance- or functionality-focused is endogenous to the matching of acquirer and acquisition target. The technology of a particular acquisition target can be viewed as either improving product performance or adding new functionalities by two different acquirers, depending on which firm acquires the target. This view is contingent on an acquirer’s specific strategic rationale for an acquisition and therefore ultimately on an acquirer’s product and technology landscape, capabilities, and strategic plans. [...] so for [acquirer name], like I said, our first task was performance [...], for the other company [second potential acquirer], […] they view the technology as something that would give them functionalities that other competitors didn’t have [...] [I 17]

From a marketing and sales perspective, performance- and functionality-focused acquisitions have unique characteristics. The marketing of the improvements resulting from performance-focused acquisitions is somewhat easier due to existing salesforce and customer relationships if the improvement is large enough and the performance dimension is relevant. New functionality gained from functionalityfocused acquisitions may have higher overall returns but a larger return variance. Generally, the technology gained from both performance- and functionality-focused acquisitions can be a key selling point or simply a basic prerequisite for a successful sale depending on the product and the competitive landscape. Performance it is a little easier to do, on the upside functionality might be bigger, because if you go new it is really competitive you are trying to differentiate the market, so more risk more reward, on the functionality side maybe. [I 17] I think as a first approximation new stuff is better than improvements to old stock. But I don’t think that’s a hard false and true, I think if you have a new feature that isn’t very useful and you can have a 100x improvement in something absolutely mission critical. So, clearly in that case the improvement is better than the new thing. [I 14]

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4.2.1.3 Performance- and functionality-focused acquisitions within the acquisition typology landscape Acquisitions in the ICT industry can roughly be clustered into seven different types: (1) acqui-hire, (2) technology & team, (3) revenue and product addition, (4) market expansion, (5) geographic expansion, (6) opportunistic acquisition, and (7) strategic acquisition. A natural way of ordering these types is based on the distance of a target’s business to that of the acquiring firm that increases if products and product lines, customers and finally markets are new. See figure 5 for an illustration. Known types

New types

Low – core business

ILLUSTRATIVE Distance of acquisition target’s business and technology to that of acquirer Medium high – High – new customers new market

Medium low – new product

1 Acqui-hire 2 Technology & Team 3 Revenue/product addition 4 Market expansion 5 Geographic expansion 6 Opportunistic acquisition 7 Strategic acquisition Performance-focused Functionality-focused

▪ Distinction novel, i.e., not covered by existing ones ▪ Existing types differentiate product improvement ▪

and product addition instead of improving performance and adding new functionality Slightly stronger correlation between acqui-hires and performance-focused acquisitions and between product additions and functionalityfocused acquisitions than with counterparts

Fig. 5: Acquisition typology

Acqui-hires (1) refer to deals whose core purpose is gaining access to talent, usually engineers but also managers. A target’s existing technology is of secondary importance or even abandoned entirely. New talent may be deployed within the core business but may also be assigned to conducting basic research for yet undefined products and markets. Hence, acqui-hires are located close to the core business but may extend into new markets. Technology & team deals or tuck-ins (2) refer to acquisitions mainly done for obtaining new technologies and experts in this technology. Technology & team (2) deals often enhance an acquirer’s core business or introduce new products. Howev-

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er, even in market expansions technology can play a role. There are two distinctions within technology-focused acquisitions, i.e., improving an existing product vs. adding a new product (red space vs. white space), and regarding the strategic use of acquired technology combined with the integration mode49. Deals for revenue and product addition (3) focus less on the technology of the acquired target but more on the products and accompanying revenue streams that the acquisition targets brings in. A market expansion (4) is usually a full business of its own with little or no connection to an acquirer’s existing business. Diversification is also grouped into the same category. Thus, revenue/product additions (3) are somewhat removed from an acquirer’s core business and market expansions even more so. Similar to both revenue/product additions (3) and market expansions (4) is a geographic expansion (5) because it can either refer to extending an acquirer’s core business to another geography or entering a new market in a new geography simultaneously. Opportunistic acquisitions (6) may be triggered by an unexpected customer need or by companies seeking a trade-sale, which can both lead to an acquisition. Strategic acquisitions (7) have a strategic reasoning that is only indirectly related to revenue generation. Examples are acquisitions aimed at blocking a competitor from gaining access to a specific technology or achieving independence from a technology supplier. The distinction between performance- and functionality-focused acquisitions is novel and not covered by the previously mentioned types (1)–(7). Both types of acquisitions are largely included in technology & team deals (2). Within technology & team deals (2) a differentiation between improving existing products and adding new products has been made (red space vs. white space). This is not the same as improving performance and adding new functionality, which can both be a reason to update existing products or launch new products. And I think for some products, performance will be a key selling point and so you could increase performance and, on the back of that, drive a go-to-market and everything else, right, which is going to change how you think about the acquisition and change the willingness to invest in it post acquisition. The same could be true for functionality. [I 13]

Performance-focused acquisitions appear to have a stronger correlation with acquihires (1) than functionality-focused acquisitions because performance-focused acquisitions seem closer to an acquirer’s core business and acqui-hires are less often done for functionality. Similarly, functionality-focused acquisitions have a stronger correlation with product additions (3).

|| 49 Within this framework, the acquired technology can be used standalone as a new product addition, integrated with existing products to improve them or fully replace existing products.

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I view these things as a spectrum. You know it is more analog than digital, that this is absolutely this. [...] You could think of these things in a graph, from talent to performance to functional to traction [I 11] Usually we were facing with some core business issues and also when we were pursuing taking talent deals, I would say that they were pretty close to our core and then ended up accelerating our performance, more than completing our functionality. [I 10]

4.2.1.4 Performance- and functionality from a pre-acquisition perspective To characterize performance- and functionality-focused acquisitions it is instructive to understand the role that product performance and functionality played for startups prior to their acquisition. A relevant question is whether these startups differentiated themselves from their competitors based on performance or functionality and how this influenced the acquisition. Indeed, there are straightforward cases where startups competed on a basis of better performance and were acquired for a product performance improvement. Sometimes startups even build additional functionality directly on top of an acquirer’s existing products and are subsequently acquired for it. Interviewer: How did competition work—was it more based on better performance or new functionalities? Interviewee: It was performance. Our motivation was to say that using our technology, search will be better, faster, and more accurate than anywhere else [...]. We did not have functionality that others didn’t have. [I 18] A lot of these companies, who were new to working with our products, recognized that there were things missing from our products and so they see a need and they just go to that need and get a bit of traction and even more traction [...] if the customer starts to really like it, then we say ‘Hey! Why don’t we just buy it?’ [I 02]

An example of a mixed case is competition based on achieving best in class performance followed by an acquisition aimed at injecting new functionality. This is a classic example of leapfrogging. I think in our case the acquisition of [startup name] this was technology that they didn’t have. They [acquirer] were experimenting [...] And also they couldn’t get it to work. [...] we [acquired startup] were always being focused on getting the best accuracy and then I think the broadening functionality [...] helped the company survive, it brought us money and focusing on the accuracy was in our DNA. [I 16]

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It is even difficult to make a general statement regarding the relevance of performance and functionality prior to an acquisition. Some startups focus on solving a particular pain point or frustration regardless of whether this means developing new functionality or improving performance. Others may only have a vague vision instead of a clear focus that can be translated into the product performance or functionality framework. Some startups even change their focus over time. When a startup competing on performance lags behind its competitors it may choose a market niche where the achieved level of performance is sufficient and differentiate itself based on functionalities tailored to that market niche. 4.2.1.5 Key results The distinction between performance- and functionality-focused acquisitions can clearly be made in technology-focused acquisitions within the ICT industry. However, both types of acquisitions are not equally distributed among all sub-industries within ICT. A simplified definition of performance- and functionality-focused acquisitions based on swapping functionality out and filling a gap can be derived that helps classifying most technology-focused acquisitions. Nevertheless, there are complex cases in which simple classification rules fail. The distinction of performance- and functionality-focused acquisitions as well as each type of acquisition individually has unique characteristics. The classification into performance- or functionality-focused acquisitions follows a particular acquirer’s strategic rationale and is therefore endogenous. Performance- and functionality-focused acquisitions differ from a perspective of product marketing. They can be a key selling point or raise a product just barely above the bar of becoming competitive. Performance improvements may be easier to market if the performance delta is sufficient. Performance- and functionality-focused acquisitions form a unique classification scheme within technology-focused acquisitions that cannot be covered by other categorizations such as acqui-hires or product additions, though performancefocused acquisitions appear to correlate more with the former and functionalityfocused acquisitions more with the latter. It is instructive to understand if a startup’s basis of competition prior to an acquisition translates into an equivalent acquisition rationale. Straightforward cases in which product performance-based competition is followed by a performance-focused acquisition or the equivalent for functionality do occur. However, mixed cases exist as well.

4.2.2 Uncertainty and risk of technology-focused acquisitions in ICT Uncertainty and risk play a key role in making strategic decisions such as those related to acquiring a company (see section 2.2). In technology-focused acquisitions, acquirers distinguish four types of risk that are relevant to their decision making:

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financial risk (1), market risk (2), technology risk (3) and integration risk (4). Figure 6 provides an overview of these types of risks50.

Risk in technology-focused acquisitions Components Financial risk

Market risk

Technology risk

Integration risk

▪ ▪ ▪ ▪ ▪

User acceptance Customer fit Market acceptance Revenue scalability Competitive reaction

▪ Basic research risk − Performance risk ▪ Development risk − Compatibility − Cybersecurity − Scalability − Talent risk − Time-to-market ▪ Standard setting risk

Fig. 6: Risk components and drivers of risk in technology-focused acquisitions

Financial risk (1), or the risk of losing invested capital, is based on the money invested to gain benefits such as synergies from an acquisition. It increases with deal value, as more synergies—some of which may not be realized—are required to justify a higher price. Financial risk is essentially the product of market risk, technology risk and integration risk and therefore not as fundamental as these three. Market risk (2) is generally based upon the question of whether the new technology performs on the market. Technology risk (3) refers to whether technology in development will perform as desired. These two types of risk are relevant for comparing performance- and functionality-focused acquisitions. Their in-depth analysis follows in subsections 4.2.2.1 and 4.2.2.2. While they are treated separately, both types of risk are related because technology is usually built to solve the paint points of customers in a specific market.

|| 50 Acquirers and startup executives speak of risk instead of uncertainty with risk having the negative connotation of downside risk (see section 2.2.1). To retain a close connection to the richness of the interviews the term “risk” instead of “uncertainty” is used throughout this and the following sections within in this chapter. This is done even in phrases where “uncertainty” would clearly be the more appropriate term such as “risk is lower because there is more knowledge about […]”.

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Integration risk is the result of uncertainty about cultural fit, key employee attrition and the not-invented-here (NIH) syndrome (Afuah, 2003; Cohen and Levinthal, 1990). It is fundamental because unsuccessful integration may disrupt an acquisition target’s existing business and destroy its revenue streams or even cause the full loss of capital invested in the acquisition. It is especially a concern for the integration decision (see section 2.2.3) but also, to a lesser extent, for decisions on acquisition timing and deal value. These risks are perceived to be lower the more information is available about acquisition targets. Prior relationships, e.g., through partnering over an extended period of time or toeholds51 greatly reduce perceived risk. The same is true if quality signals such as a target’s traction or a sufficient company size are evident. Waiting for such quality signals to emerge is a viable strategy of perceived risk reduction for acquirers. 4.2.2.1 Market risk Market risk is based on five components, user acceptance, customer fit, market acceptance, revenue scalability and competitive reaction. User acceptance may be compromised if a product’s design is too complicated or lacks usability for certain groups of users. Closely related but more general are issues regarding customer fit. It is at stake if an acquired technology only to some extent addresses a specific customer need. [Name of technology from acquired startup] is for example very complex in its deployment and functions only well for large business customers. And it does not address 80% of what customers of [acquirer name] need. [I 21]

It is rather difficult for acquirers to identify targets that exactly fill a certain technology need. It is just difficult to find that fit, there are a lot of holes out there for which there is no company to fit in nicely, you know, it was not uncommon for a product lead to say ‘Oh, why don’t we look at company X because they fill this gap?’ [I 09]

From an acquirer’s perspective, the contribution of customer fit issues to market risk is low if the acquirer’s customers specifically ask for the technology of a certain startup. One level broader than customer fit are potential concerns regarding a product’s market acceptance. In the worst case, it is unclear if a market for a certain

|| 51 An acquirer has a toehold in a target if the acquirer invested in the target prior to the acquisition (Hutzschenreuter et al., 2014; Bulow et al., 1999).

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product exists at all. Even if it does, future changes of a market’s direction may jeopardize a product’s success. Market acceptance may also be at stake if a product’s use case is too narrowly tailored to very few customers and hence may not be accepted by the market as a whole. Related to market acceptance is the issue of revenue scaling, i.e., growing the revenue of an acquired startup to a certain size. Revenue scaling risk is especially a concern in cases where an acquired firm operates standalone, possibly in an adjacent market. Following an acquisition there is the risk of a detrimental competitive reaction. It is likely higher the more an acquirer attempts to differentiate in the market. Especially for acquirers, customer fit is a relevant driver of market risk because it is difficult to find a suitable target while a failure regarding customer fit of the acquirer may have triggered searching for a target in the first place. The latter point may apply additional pressure onto mitigating potential customer fit issues in acquisitions. Market acceptance of acquired products and technologies is an issue for acquirers especially in the case of quickly changing market conditions. You can’t know for sure, that every acquisition will work out for sure. You are trying to build good functionalities, whether or not you know the customer base well or the future direction of the market well. Sometimes it is really a type of hit or miss. [I 02]

4.2.2.2 Technology risk Technology risk is comprised of three components, basic research risk, development risk and standard setting risk. Basic research risk refers to the chance of a technology project not coming to fruition due to fundamental barriers that cannot be overcome if a certain approach is chosen. [...] the initial idea, that is a certain risk. I am building a quantum computer that nobody has and I have a certain theory that quantum computers will become very strong [...]. Then that is the first bet. [I 04] [...] of course, there are certain things that are simply not do-able for physical or mathematical— truly scientific—reasons. [I 04]

Acquirers distinguish between technology that is difficult to develop and virtually impossible to replicate. Especially cases in the latter category serve as a trigger for an acquisition of a target having solved fundamental issues. Conversely, acquirers try to avoid having to bear basic research risk of an acquisition target because it tremendously increases the chance of acquisition failure. [...] if you have to take on the R&D risk, [...] that introduces a high degree of risk. [I 09]

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Startups minimize basic research risk by ensuring that the major technological decisions and innovations have taken place prior to their establishment. Founders often develop their technology at the university and establish their startup as a spin-off with university backing. Alternatively, they experiment with and research new technologies “on the job” working for example as consultants. The risk of not achieving an aspiration level of product performance is a special but relevant case of basic research risk. From a startup perspective, it is again considered low if for example prior research at the university level has proven a technology’s feasibility. Acquirers can minimize it by putting performance to the test during due diligence if they have the proper skillset. [...] in the acquisition process we went through technical due diligence, and the technical due diligence was ‘hey, we are going to give you something to do, and we want to measure the performance, but technically, and we want to compare it against like industry benchmarks’, so, like in our case, they were saying, ‘[...] we are going to compare it [performance] to like your competitors and compare it to the market’ and so, the companies try to mitigate the technical risk as much as possible. [I 17]

Development risk is an umbrella term that combines the technology risks that is incurred when turning a technology into a market-ready product. It encompasses compatibility, cybersecurity, scalability, talent- and time-to-market risk. Compatibility risk refers to the chance of acquired and existing technologies not properly working together and can range from the architectural, over the code level to the user interface level. It depends greatly on the depth of technology integration required. Cybersecurity risk becomes an issue when the protection of IT products against undesired external access is questionable. Scalability risk is relevant when a technology that worked properly with few users is required to scale to user numbers that are several orders of magnitude higher than previously. Talent risk is related to the chance that a target’s engineers are not sufficiently skilled to finalize product development. Time-to-market risk is the result of uncertainty about how long it takes until a product is ready for a full-fledged market launch. Time-to-market risk is likely an outcome of the previously mentioned components of development risk as issues related to compatibility, cybersecurity, scalability and talent drive time to market. From an acquirer’s perspective, compatibility, scalability risks and talent risks are most important among the elements of development risk so that they are focal areas of technical due diligence. The first two are high in technology-focused acquisitions because they are difficult to test from an acquirer perspective. The last one can be somewhat mitigated via interviews and engineer background checks.

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The big problem in such an acquisition is the integration of code that they have developed stand alone in my core technology platform. And that is not trivial and usually leads to the situation that the core platform has to be redone. [I 21] When we do diligence on these companies one of the big things is: Will it scale? Will it operate at large scale without slowing down or crashing, right? So reliability at scale, performance at scale. [I 07] So all the sudden you go from ‘this has to work across twenty or thirty servers’ to ‘it has to work across two or three thousand servers and maybe does not scale.’ [...] What is very, very difficult to understand is the scalability. The scalability in a real working environment. [I 09]

The last component of technology risk is that of standard setting risk. It occurs when two or more technologies compete while a standard has not been established (Warner, 2006). With an acquisition, acquirers may bet on the wrong standard so that there is a chance of the acquired technology becoming obsolete or not receiving sufficient market traction. Hence, standard setting risk is somewhat at the interface of technology and market risk. In conclusion, basic research risk, development risk and standard setting risk are the three components of technology risk. Basic research risk is can be a serious issue in acquisitions as well as compatibility and scalability risks as parts of development risk because they drive time to market. In spite of this evidence, there is the perspective of technology risk being low overall in software development and technology-focused acquisitions in the software industry. There are two reasons. First, hardly any software-related issue simply cannot be solved. Second, software development and the technical due diligence heavily involve testing of a software solution. Very rarely have we looked at something and said ‘There is no way that we could build that.’ I mean, the good thing about technology is, it’s just code. [I 03] [...] but mainly in the areas of acquisitions and the future of startups, technology risk is simply not the critical issue. [I 20]

4.2.2.3 Differences in risk between performance- and functionality-focused acquisitions Generally speaking, risk is lower for performance-focused acquisitions than for functionality-focused acquisitions because “it is a little easier to do” and there are “more knowns”.

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[...] when a big company like [acquirer name] acquires a small start-up, they need a good story, they need you to make their mind up saying they’re buying this company, this technology is going to be used here to improve this product, here, just like a say rationalization, is very important on the acquisition and … kind of the functionality side it is more or less, well I think it’s easier on the kind of performance side in a justification meeting on the acquisition, because there is more knowns say in a sense, less risk. [I 17]

This general view is corroborated by a comparison of performance- and functionality-focused acquisitions at the level of technology and market risk. Market risk is much higher for functionality-focused acquisitions while technology risk is higher for performance-focused acquisitions—however, only slightly so. See figure 7 for a summary.

Key risk component Risk differential

Reasoning

▪ In the case of performance-focused

Market risk Performancefocused

Functionalityfocused

▪

acquisitions, customers, competitors, growth of own product, market and marketing approach are well known Functionality-focused acquisitions bring something “new” that is less similar to existing products and therefore less well known

▪ Performance risk as basic research risk is

Technology risk Performancefocused

Functionalityfocused

▪

relevant for performance-focused acquisitions but is rather low because performance is observable and testable Development risk is similar for performance and functionality-focused acquisitions because risks regarding compatibility, cybersecurity, scalability, talent and time-tomarket apply equivalently

Fig. 7: Market and technology risk in performance- and functionality-focused acquisitions

Market risk of performance-focused acquisitions is low because customers, the marketing approach, the market environment, revenue scalability, and the competition are well known. The key reason for this is that the technology of performancefocused acquisitions yields a direct and observable benefit for an acquirer’s core products that sell through proven marketing channels to existing customers. Hence, customer fit is ensured instantly. Given an acquirer’s prior experience with their products and customers there is the expectation that revenue scalability is easier to achieve and involves less risk.

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So I do not think it is something where it [customer’s needs] is so unknown on the technology expansion side, I think it is more on the performance improvement side things are so well known. It is just really an advantage. [I 09] I mean, when you buy a performance, you should have a higher impact faster. So if you bring it to your own business you can just scale things. [I 10] [...] like [acquirer’s product name] for example [...] they know themselves, they know the customers, they know what features the customers want, they know what features the customers want improved, so in a search case, search was terrible [...] so the pain point was known like very well. [I 17]

Conversely, market risk of functionality-focused acquisitions is higher than that of performance-focused acquisitions because customer fit is not immediately ensured. If novel functionality is to be sold as a new product, it cannot benefit as much from an acquirer’s deep knowledge of their core business. In this case, acquirers view startups that are bought for new functionality as internal startup businesses corresponding to high risk. Acquisition targets are required to have market validation implying an a priori uncertainty about customer fit. [...] like on the new functionality side, like that’s a start-up business themselves, and start-up businesses by definition are risky, because you don’t know what if the new functionality is needed, you know you can guess, but a lot of times you know it fails [...] [I 17] [...] the danger is, that those functionalities, while we use them and we try them out, they happen to have way difficult time, going from the so called incubation stage to really full-fledged massive release, whereby the full assets of our business is put behind it to help them scale. Yeah, we buy them and, yes, they can grow a little bit, but they don’t get the full benefit of the core [...] businesses in some cases and this a challenge. [I 10]

Technology risk is relatively low in the software industry as explained in subsection 4.2.2.2. Performance- and functionality-focused acquisitions differ predominantly with respect to basic research risk as development risk is similar for both types of acquisitions. The risk of standard setting applies only in distinct cases. Performance-focused acquisitions have a slightly higher technology risk than functionality-focused acquisitions because basic research risk plays a more important role with respect to achieving a specific level of performance. This risk is somewhat mitigated by the fact that performance can be demonstrated. Development risk as one component of technology is similar for performance- and functionality-focused acquisitions. Scalability can be an issue for functionality-focused acquisitions.

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If for example we were completely integrated into [name of potential acquirer] tomorrow and suddenly had a million or more users, then I certainly would have to check three times, if our software scales to that level seamlessly given how it is built right now [I 21]

In performance-focused acquisitions, scalability may also be a concern. Scalability and performance may be interlinked so that performance at scale is the proper measure. Risks on tech side included the scalability of the image recognition. We started with 100 objects in the database and a recognition speed of ten seconds [...], but for a good service, we needed hundreds of millions of objects in the database to be recognized in a few milliseconds. The risk was that we will technologically not get there due to hardware limits, etc. [I 19]

In summary, functionality-focused acquisitions have a higher market risk than performance-focused acquisitions because they share fewer similarities with an acquirer’s existing products. Technology risk is slightly higher for performance-focused acquisitions due to product performance strongly being tied to basic research risk. This does not offset the greater market risk differential so that overall risk is higher for functionality-focused acquisitions than performance-focused ones. 4.2.2.4 Key results Among the four types of risk in technology-focused acquisitions technology and market risk are most relevant for studying risk differences of performance- and functionality-focused acquisitions in the ICT industry. Market risk is driven by user acceptance, customer fit, market acceptance, revenue scalability and potential competitive reactions. Technology risk is driven by basic research risk, development risk and standard setting risk. Comparing performance- and functionality-focused acquisitions, especially customer fit and revenue scalability play an important role in terms of market risk. Technology risk of both types of acquisitions is generally low as software development usually does not face hard scientific barriers. If they do exist, they are tied to basic research risk, often in terms of product performance or product performance at scale. Overall, risk is higher for functionality-focused acquisitions than performance-focused ones. The main driver is market risk that is— especially in relation to customer fit and revenue scaling—much higher for functionality-focused acquisitions. The risk of lacking customer fit is high because it is unclear if customers like a particular new technology. Revenue scaling is an issue for functionality-focused acquisitions if the new functionality is not deeply integrated with an acquirer’s core business. Overall, functionality-focused acquisitions share fewer similarities with an acquirer’s core business. Technology risk is slightly higher for performance-focused acquisitions due to the basic research risk associated with achieving a certain level of product performance. The observability of prod-

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uct performance during due diligence reduces the risk associated with product performance. Thus, higher technology risk of performance-focused acquisitions is not sufficient to offset the reverse differential in market risk.

4.2.3 Acquisition timing in technology-focused acquisitions in ICT Acquisition timing refers to the life cycle stage of an acquisition target in which the acquisition is to take place (see section 2.2.4). Acquisition timing is determined by two factors. The first factor is contingent on the occurrence of windows of opportunity for an acquisition in which one firm is triggered to search for an acquisition target and another firm is open to or actively seeking a trade-sale. Windows of opportunity in technology-focused acquisitions are described in subsection 4.2.3.1. The second factor is based upon the decision making mechanisms that cause an acquirer to wait with an acquisition or select a target that fulfills an acquirer’s requirements regarding its life cycle. These decision making mechanisms are the topic of subsection 4.2.3.2. The differences of these mechanisms regarding performance- and functionality-focused acquisitions are then explored in subsection 4.2.3.3. Subsection 4.2.3.4 discusses the interaction of acquisition windows of opportunity and timing mechanisms based on two examples. A summary of the key results in subsection 4.2.3.5 concludes the analysis of acquisition timing in technology-focused acquisitions in the ICT industry. 4.2.3.1 Windows of opportunity in technology-focused acquisitions Two necessary but not sufficient pre-conditions for an acquisition to take place are that a firm (acquirer) searches for an acquisition target and that there is at least one other firm (acquisition target) that is open to or actively seeking a trade-sale and has the relevant technology. These two pre-conditions usually do not persist indefinitely and their temporal overlap constitutes windows of opportunity in which an acquisition can take place. Figure 8 illustrates these windows of opportunity using an example of an acquirer and a single acquisition target52. I think that the point in time of the acquisition was perfect for both sides. On one side, an acquisition from [acquirer name] would have failed at an earlier stage due to lack of important features [...]. On the other side having these features improved the price accordingly for the acquisition which again was important for [name of acquisition target]. [I 19]

|| 52 At a given point in time there may be other potential acquisition targets for which the landscape of windows of opportunities looks entirely different.

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EXEMPLARY

ILLUSTRATIVE

Acquirer perspective Begin of product development Acquisition Technoprelogy gap condition1

Trade-sale precondition1

Internal developent too slow or expensive

Risky conditions: funding risk3

Begin of gap-filling technology development

Market launch of product Product quality or market traction lacking

Time Acquisition windows of opportunity ▪ Acquisitions feasible only within windows of opportunity ▪ Drivers such as uncertainty and risk influence acquisition timing within windows of opportunity

Acquisition offer(s) above reservation price4 Risky conditions: unprofitable product3 Market launch of product

Time

Single target perspective2 1 Please note that the pre-conditions necessary for acquisition/trade-sale may last much shorter/longer than indicated and can be voided temporarily e.g. by partnering 2 Often there are several potential acquisition targets with different time frames in which the pre-conditions for a trade-sale are fulfilled 3 Example pre-condition for a trade-sale 4 Can be influenced by acquirer

Fig. 8: Windows of opportunity for conducting an acquisition

An acquirer starts searching for an acquisition target if one of the following preconditions is fulfilled. There is a technology gap, internal product development is too slow or too expensive, product quality from an internal, customer, or competitive perspective is lacking or market traction of the final product is unsatisfactory. A technology gap may be identified during an acquirer’s formal strategy review sessions or when considering a product update. It can also emerge ad hoc following a competitor’s announcement of new product development plans. Each case triggers a make-or-buy analysis in which first cost and time-to-market are compared and second acquisition ideas and a set of desirable target characteristics are generated. A make-or-buy decision is made or revisited if internal development turns out not to be in budget or on schedule. The same is true if product quality proves to be insuffi-

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cient or traction is falling short of expectations. One potential reason for such a negative development is a misalignment of product strategy. Quite obviously, a target is only considered for acquisition if its technology provides the acquirer with suitable functionality or it has reached a sufficient level of performance that is attractive for an acquirer. I think the acquisition has to be justified, and it’s justified by [...] what the acquirer is trying to do [...]. If there’s a start-up that’s got technology that’s say that looks like it might give a 100x improvement in the performance of neural nets obviously that’s attractive to an acquirer that’s doing a lot of stuff with neural nets. [I 14]

A startup considers a trade sale if one of two pre-conditions (or a combination of both) is fulfilled. Either the acquisition offer is sufficiently attractive or the startup’s situation is unacceptably risky so that a trade-sale is the most viable strategy. An acquisition offer is, for example, sufficiently attractive, if it is above a seller’s reservation price. A startup’s situation may be unacceptably risky if it expects to run out of external funding in the near future or it operates in a market niche with few large incumbents. So one factor is related to financing or the commercial viability of startups. Especially if they are burning cash quickly and at some point it is foreseeable that another round of external funding may not come, then there is pressure to sell. [I 01]

4.2.3.2 Acquisition timing mechanisms Four mechanisms drive the timing of an acquisition within the windows of opportunity. These are—roughly in order of importance53—risk/uncertainty threshold (T1), acquirer value focus (T2), best technology focus (T3), and internal resistance delay (T4). In the following each mechanism is described individually. The risk/uncertainty threshold mechanism (T1) holds that acquirers make acquisition timing decisions based on the risk that a certain acquisition target represents for them. If this risk falls below a threshold, an acquisition is justified. Risk and acquisition timing in terms of the acquisition target’s life cycle at the time of acquisition are closely interrelated. When a target grows and matures, there is the general expectation that traction as an indicator of product quality is higher, the stability of the product and its underlying technology increases, basic technological research has been finished and a target should be more robust against the disruption caused by a post-merger integration. These factors speak for lower market,

|| 53 The order of importance is roughly based on the prominence of the mechanisms in the interviews.

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technology and integration risk of a later acquisition and conversely a higher risk of an earlier acquisition. [...] in an acquisition we seek a certain gravity, age, revenue size, number of employees. We do not always get that. [...] those that have a certain maturity and size. There are essentially two reasons for that. The first one is that we do not want to provide our business customers with shaky functionality. Hence, a certain product maturity is necessary. And then a certain organizational elasticity of the organization and the capacity to survive a post-merger integration. [I 01]

However, other factors show that acquiring early lowers risk, which introduces a trade-off. A target that is early in its life cycle at the time of acquisition may not have developed assets that are irrelevant for the acquirer, which reduces deal value. Hence, the invested capital at stake is lower reducing financial risk. Younger acquisition targets have a higher technological flexibility and due to their size require a less complex post-merger integration, which lowers technology risk in terms of compatibility risk and integration risk. If [...] these guys [engineers at the target] are a couple of years ahead but with this now we can go to market. If you have that, then yes, you should go even earlier, before it [the acquisition target’s product] is fully done, because again, it is going to be cheaper and you can build it out exactly the way you want it." [I 09]

The risk threshold for acquisition timing depends on the individual target and acquirer characteristics. A target’s technology needs to function properly and address the customers’ need. This can be before or after a target has finished product development. Hence, the risk threshold is set such that the target can show sufficient proof of both. An acquirer adjusts this individual risk threshold based on their internal capabilities. If the acquirer has engineers with a particularly good understanding of a specific target’s technology, a threshold that corresponds to an earlier acquisition may be adequate. You know the real threshold for ‘how early?’ is as early as possible to get something that is viable and meets the customer need. [I 09] But I think the value to the acquirer it comes when they can prove that or when they have something that acquiring them would then result in that benefit going to the acquirer—and that could be before or after they’ve actually created something. [I 14]

An acquirer can lower the risk associated with an acquisition so that it falls below the individual threshold based on three strategies: selecting a target with market

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validation, engaging in pre-acquisition partnering or joint product testing, and, lastly, waiting for the acquisition target to mature. I would say that we do not buy a business without doing testing which can take a while or without knowing, that there is a strong market validation of their technology in the marketplace. [I 10]

In addition, an acquirer’s risk propensity (Pablo, 1996) may influence timing within mechanism (T1). If an acquirer is particularly risk seeking, the risk threshold for acquisitions is likely set to a lower level, leading to overall earlier acquisitions. According to the acquirer value focus mechanism (T2) acquirers attempt to optimize acquisition timing such that they gain exactly what they value without having to pay for irrelevant target assets. If an acquirer only values a target’s technology, but not its market traction then the ideal timing is before the target achieves a significant user base. If we cared about the underlying technology because, again to use your terminology, it was performance based, we want to actually take [their technology] and integrate it into our products then we don’t care about how the market performs. [I 11]

However, if only the technology is valued, then there must still be proof that the technology will bring the desired value to the acquirer. Conversely, if an acquirer values a target’s traction in order to enter an adjacent market then acquisition timing must ensure that sufficient traction is present. What an acquirer values about an acquisition target may be determined by strategy or culture. An example of a strategic reason for valuing market traction is to keep and enhance the option of a go-tomarket of the target’s product independently of the acquirer. A cultural reason maybe an acquirer’s sales driven ideology which means that a target’s traction is valued—and a target selection criterion—by default. In the best technology focus mechanism (T3), acquisition timing is determined by the fact that an acquirer wants to gain the “best”54 technology. For an acquirer, having bought the “best” technology may hold the benefit of increased publicity, introduce an initial marketing push or send a message to competitors. The objective of obtaining the best technology can result in an early acquisition or late acquisition depending on what is deemed as proof that a technology is “best”. If an examination of the technology by itself provides sufficient proof, an early acquisition is viable. Similarly, if external proof is required to show that a

|| 54 The term “best” is used here figuratively as usually there is no single objective standard by which technologies can be compared. A similar, yet less controversial term would be “good enough” for a particular purpose or benefit.

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technology is “best” an acquisition target needs to have sufficient traction, which corresponds to a late acquisition. [...] for functionality it is something new, which has to work and has to have some market validation, usually if you bring the functionality, you want to buy the best in that functionality. [I 10]

The best technology focus (T3) mechanism can be considered a hybrid of the previous mechanisms (T1) and (T2). Acquiring the “best” technology can be interpreted from the viewpoint of minimizing risk (T1) because of requiring proof. Simultaneously, an acquirer value (T2) based perspective would associate the benefits an acquirer gets from buying the “best” technology with acquirer value. The internal resistance delay mechanism (T4) states that internal resistance influences acquisitions timing. If the technology of an acquisition candidate threatens to render some of an acquirer’s internal capabilities obsolete there may be resistance to the acquisition. This resistance may block the acquisition introducing a delay. Within the acquisition there are two groups. One is the group that does the R&D [...] and the other is the one that wants to push the technical skills of the company forward. So the group that wants to push the technological skills forward is in favor of the acquisition, whereas the group that does the R&D [...] they are going to block the acquisition. [I 16]

All of the mechanisms described above can act simultaneously which means that they are not mutually exclusive. In mechanisms (T1) to (T3), waiting or selecting a target with a suitable maturity profile are possible strategies to achieve a desired acquisition timing. The reasoning for applying each strategy is different, though. Mechanism (T4), however, acts on a different level than (T1) to (T3). It deals with an internal delay of the decision to acquire instead of deliberately choosing acquisition timing in terms of a target’s life cycle. 4.2.3.3 Acquisition timing of performance- and functionality-focused acquisitions When considered separately for performance- and functionality-focused acquisitions, acquisition timing mechanisms (T1) through (T4) yield largely similar outcomes for acquisition timing. Mechanisms (T1) through (T3) agree that performancefocused acquisitions take place earlier in a targets life cycle than functionalityfocused ones. Only mechanism (T4) comes to the opposite conclusion. These results along with the underlying reasoning are summarized in figure 9. The risk/uncertainty threshold mechanism (T1) states that performancefocused acquisitions happen earlier because the risk of performance-focused acquisitions is lower. Hence, acquisitions are expected to take place even before the mar-

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ket validation of a technology. Section 3.4.1 and subsection 4.2.2.3 provide an indepth analysis showing that performance-focused acquisitions have lower risk. You want the product to in its current state be sellable, so as soon as possible, as early as possible, to get a product that is sellable or very, very near saleable in its current form. [...] Assuming that it is at that stage and the best time to buy it is the minute or the day that it achieves that level. Then, within that I would say, it is the performance improvement, because [...] you are in a better position to valuate that risk, and to know that it is ready, and you have a much better, stronger feedback loop in terms of understanding what to do to make it better. If you go too early on the technology expansion then it is kind of ‘fall flat on its face’, you are not exactly sure why [...] [I 09] You know I think if it is clearly performance driven, I would say that we sometimes might do it sooner, because you can probably tweak and fix it as well. You would do some testing, unless you know the people. [...] You would probably do some test, but probably your risk-threshold is lower than it would be for completing your businesses or your functionality. [I 10]

✔ Yes ✘ No Finding for performance and functionality-focused Consistent Timing mechanism acquisitions Relevance1 with hyp. 1a2

▪T1

Performance-focused acquisitions happen earlier in a targets life cycle than functionality-focused acquisitions because… Risk/un- ▪ uncertainty/risk of performance-focused certainty acquisitions is lower than that of functionalitythreshold focused ones.

✔

▪T2

Acquirer value focus

▪

acquirer values technology—not traction—in performance-focused acquisitions but traction in functionality-focused acquisitions.

✔

▪T3

Best technology focus

▪

acquirer can ensure publicity effect of acquiring “best” technology only if target has market traction in functionality-focused acquisitions but even before target has traction in case of performance-focused acquisitions.

✔

▪T4

Internal resistance delay

Performance-focused acquisitions happen later than functionality-focused acquisitions because… ▪ acquirer’s internal product development team shows resistance against performance-focused acquisitions but not against functionalityfocused acquisitions.

✘

1 Rough assessment of timing mechanisms’ relevance based on their prominence in interviews 2 Timing mechanism in consistent with hypothesis 1a: Ceteris paribus, performance-focused acquisitions take place earlier in target's life cycle than functionality-focused acquisitions.

Fig. 9: Acquisition timing mechanisms applied to performance- and functionality-focused acquisitions

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The acquirer value focus mechanism (T2) shows, as well, that performancefocused acquisitions take place earlier in a target’s life cycle. The main reason is that in performance-focused acquisitions demonstrating that the technology is valuable in itself is easily achieved based on performance dimensions. This is less so for functionality-focused acquisitions without traction. And if [target of performance-focused acquisition] had been in a different state back then, without or with fewer customers, then they would have acquired [target of performancefocused acquisition] probably as well. I think that this didn’t play a role in the acquisition. Of course, one cannot fully ignore that there were customers because some believe that that has value. But on the other hand it [the technology] was demonstrable even if we hadn’t had any customers but just the technology. [...] I think that customers or no customers did not play a role for [acquirer name]. That was a pure technology-acquisition which was completely independent of having customers [...]. [I 18]

Like the previous two mechanisms, the best technology focus acquisition timing mechanism (T3) is in line with the statement that performance-focused acquisitions happen earlier in a target’s life cycle. The claim of acquiring the “best” technology in case of a performance-focused acquisition can be established by simply by measuring performance. For a functionality-focused acquisition, sufficient market traction shows that a technology is “best”. [...] the motivation of a firm like [name of potential acquirer] to acquire a firm like us [potentially functionality-focused acquisition] depends on much more than a pure functionality-based perspective. [...] If they do an acquisition, then they want to send a message to the market that they have bought the market. And that corresponds to a certain size category in which they want to acquire. They are essentially waiting that 'we' firms are growing into this size category [...] [I 21]

Only the internal resistance delay acquisition timing mechanism (T4) holds that performance-focused acquisitions occur later than functionality-focused ones. The reasoning is that the technology gained in performance-focused acquisitions is more likely to replace an acquirer’s existing technology. This leads to internal resistance and a delay of the acquisition. The internal resistance delay is expected to act weaker than mechanisms (T1) to (T3) because it can be circumvented by bypassing internal groups showing resistance. Interviewer: Do you think [...] the acquisition would have happened earlier or later, in the case that your technology had been performance focused? [...] Interviewee: Later, because [...] the group [within the acquirer] that does the R&D itself, [...] they are going to block the acquisition. [I 16]

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4.2.3.4 Interactions of windows of opportunity and acquisition timing mechanisms—two examples Mechanisms (T1) through (T3) are in line with the statement that performancefocused acquisitions take place earlier in a target’s life cycle than functionalityfocused ones. While this may be true on average, there are situations where rather mature targets are acquired to improve product performance. The interaction of acquisition windows of opportunity described in subsection 4.2.3.1 and the timing mechanisms explained in subsection 4.2.3.2 provide an explanation. In the following, the consequences of this interaction are illustrated based on two examples of performance-focused acquisitions. Both examples have in common that a startup was acquired to improve the accuracy of an acquirer’s search engine. In the first example, the acquirer did not realize the need for a performance improvement until competitive pressure increased significantly. When the acquisition offer came, the startup had already launched their first product and gained traction in the market. We did have customers, we were present in the market - already for some time, not just a few months but a few years. [...] There even had been contact with [acquirer name] for some time— not in relation to an acquisition. [...] But my guess is that they simply, based on the development in the market [...] said they have to do something. And even if [target name] had been in a different state meaning no or much fewer customers then they would have acquired [target name] as well. [I 18]

In the second example, the startup initially launched one product that was entirely unrelated to improving the performance of search. When this product did not gain traction a second, search-based technology was developed and the prototype was presented to potential acquirers. As acquirers could link the second technology to an internal technology gap an acquisition was proposed. Our first product that we spend three years on [...] turned out not so useful [...] it never really took off, so even though we had a lot of customer focus and ‘that’s cool’-comments, it wasn’t profitable and it wasn't high-enough growth [...] and that's when over this time period when we have been developing the whole search [...] and that was the point where we developed the technology and we developed a prototype of the a new product [...] and when we brought that up to show the larger companies to say validate it they were like ‘Yeah, that’s also a pain point that we know of’ [...]. [I 17]

In the first example the performance-focused acquisitions could—in principle—have taken place early, i.e., prior to market launch. In the second example, the acquisition did take place prior to the market introduction of the technology that triggered the acquisition—though another product had been launched before. Hence, both examples are consistent with timing mechanisms (T1) through (T3). However, there was market traction in both cases, even if it was due to the first product as in the

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second example. Hence, technically both examples qualify as acquisitions of mature targets or equivalently as acquisitions with rather “late” timing. This is caused by acquisition windows of opportunity—competitive pressure for the acquirer in the first example and the target’s development of the relevant technology only as a second product in the second example—opening up rather late in each target’s life cycle55. Thus, if an individual case appears to contradict the timing mechanisms described in subsection 4.2.3.2, a full understanding of the focal case that takes windows of opportunity into account is warranted, though likely not consistently feasible from a practical perspective. 4.2.3.5 Key results Acquisition timing is driven by windows of opportunity for an acquisition and timing mechanisms. Acquisition windows of opportunity represent a temporal matching between an acquirer’s need to conduct an acquisition and a potential target’s search for or openness to a trade-sale. Acquirers generally look for a target to conduct a technology-focused acquisition if there is or will be a technological gap, internal development is too slow or expensive or a product’s quality or market traction are insufficient. Potential targets likely accept a trade-sale offer if it is attractive or its business conditions are risky. Within these windows of opportunity—several of which may overlap due to multiple viable targets—timing mechanisms determine acquisition timing in terms of a target’s life cycle. There are four timing mechanisms which are in a rough order of relevance risk/uncertainty threshold (T1), acquirer value focus (T2), best technology focus (T3) and internal resistance (T4). According to (T1), the timing of an acquisition is such that it takes place when the risk associated with an acquisition falls below a certain threshold. Mechanism (T2) holds that acquisition timing is driven by what an acquirer values most about the target. If only technology is valued and there is proof for this value then an acquisition can take place early, possibly even before a target’s product launch to avoid paying for irrelevant target assets. An acquisition happens later if an acquirer values market traction in a target. In mechanism (T3), an acquirer chooses acquisition timing such that obtaining the “best” technology can be ensured to benefit from the marketing effect. An early acquisition may be chosen if the claim of acquiring the “best” technology can be proven based on the technology by itself. (T3) is a hybrid of mechanisms (T1) and (T2). Mechanism (T4) focuses on the acquisition delay that internal resistance can cause as a determinant of acquisition timing. All mechanisms (T1) through (T4) may act simultaneously at varying degrees. In mechanisms (T1) through (T3), a target’s maturity at the time of acquisition may be influenced deliberately by waiting or

|| 55 Equating a target’s life cycle to that of its technology, one could argue that the startup in the second example has started a new life cycle.

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selecting a target with the appropriate maturity profile. In mechanism (T4) waiting is involuntary. It is the weakest mechanism because an acquirer can bypass the resisting internal group in anticipation of delay. Mechanisms (T1) through (T3) concur that performance-focused acquisitions take place earlier in a target’s life cycle than functionality-focused acquisitions. Key reasons are lower risk of performancefocused acquisitions and the fact that performance can be demonstrated even before a product’s availability on the market. According to mechanism (T4), performancefocused acquisitions happen later than functionality-focused ones. The reasoning is based on higher internal resistance against performance-focused acquisitions because they are more likely to replace an acquirer’s internal development efforts. Individual cases may apparently contradict the timing mechanisms described above. A full understanding that includes acquisition windows of opportunity may resolve this contradiction. For example, a performance-focused acquisition of a target with significant market traction may be the result of a window of opportunity opening up rather late in the target’s life cycle.

4.2.4 Acquisition deal value in technology-focused acquisitions in ICT The deal value in a technology-focused acquisition is in an idealized case a match between an acquirer’s deal value proposition, i.e., the acquirer’s offer, and the target shareholder reservation price facilitated via negotiation. Market conditions such as the presence of a buyer’s or seller’s market and the geographic location56 of the target represent an external influence on deal value. The left side of figure 10 depicts this general logic. An acquirer’s deal value proposition is the result of the combination of three mechanisms, an acquirer’s evaluation of synergies (D1) gained from the acquisition, an acquirer’s assessment of the target’s value independent of synergies (D2), and a risk/uncertainty discount (D3). An in-depth description of the three mechanisms (D1) through (D3) jointly with the underlying drivers follows in subsection 4.2.4.1. The drivers of the shareholder reservation price are described in conjunction with mechanism D2 because of their close connection. Subsection 4.2.4.2 deals with the question whether these mechanisms yield different outcomes with respect to performance- and functionality-focused acquisitions. Key results are summarized in subsection 4.2.4.3.

|| 56 Especially the acquisition of US-based targets seems to be more expensive than, e.g., of those based in the European Union.

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Perspective

Deal value drivers

▪ Synergistic

▪ Target ▪ Synergy risk future revenue and − Integrarevenue traction tion risk ▪ Revenue pre- ▪ Technology − Market servation/ and team risk cost saving ▪ Exclusion of buy instead of target irelemake1 vant assets3 ▪ Publicity effect ▪ Acquirer ▪ Target value D3 ▪ Risk/uncerD1 D2 synergies (assessed by tainty disacquirer) count

Acquirer

Deal value

Market conditions

Deal value mechanisms

▪ Buyer’s vs. seller’s market ▪ Target geographic location

Shareholder reservation price Target

▪ Degree of commercial success and traction

▪ External funding ▪ Maturity and timing 1 An example are potential cost savings from not having to hire and train engineers 2 Risk/uncertainty discount in parentheses to illustrate its relatively small prominence among drivers of deal value 3 Example of potentially irrelevant assets: target’s non-engineering team

Fig. 10: Logic governing deal value decisions

4.2.4.1 Deal value mechanisms for technology-focused acquisitions in ICT There are three mechanisms governing an acquirer’s derivation of their deal value proposition for an acquisition target (henceforth simply called deal value), acquirer synergies (D1), target value (D2), and risk/uncertainty discount (D3). The right side of figure 10 shows these mechanisms along with their drivers. The acquirer synergy mechanism (D1) is based on the synergies gained from a particular acquisition. Acquirers are willing to pay a certain price for achieving these synergies. By definition, synergies purely stem from what an acquirer gains due to the combination of two firms in acquisition (Weber et al., 2013) and are therefore, in terms of deal value, unrelated to the target’s value. Key drivers for the acquirer synergies mechanism (D1) in technology-focused acquisitions in ICT are synergistic future revenues, revenues preserved and cost savings due to not having to develop a technology internally or a publicity effect. Synergistic future revenues may stem from incorporating a target’s technology into existing products and sell it via an acquirer’s sales channels or cross-selling an ac-

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quirer’s products to the new customers that the acquisition target brought in. An acquisition target’s technology may be used to accelerate the time-to-market of an acquirer’s product development effort. This gain in speed may preserve revenues streams from customers that would have moved to a competitor if the slower internal development trajectory had continued. A technology-focused acquisition may also lead to cost synergies due to not having to hire and train engineers internally for a specific development project. A publicity effect may come from acquiring a market leader and yield marketing synergies. The target value mechanism (D2) derives the deal value according to the acquirer’s assessment of the target’s assets. This mechanism examines the target essentially stand-alone and excludes or discounts the value of any assets that are irrelevant for an acquirer. Drivers of target value are its revenues and traction, its technology, and its team. Revenues and traction include current and future revenues following from growth rates. The value of technology derives from its scarcity and of the team from its scarcity and its skillset. Assets that may not be valued by an acquirer include a target’s traction if the acquisition is purely done for the technology or any administrative staff. Closely related to an acquirer’s assessment of the target’s value is the target’s shareholder valuation of the target’s assets. Shareholders use their own valuation as a basis for their reservation price, i.e., the minimum deal value they are willing to sell for. It is driven by the degree of a target’s commercial success including traction, its external funding, target maturity and timing of the acquisition (see figure 10). Lacking commercial success and traction lead to a lower reservation price and hence a lower deal value while much VC funding and later timing usually drive up the reservation price and thereby deal value. Once they come to the realization, that a), they can’t get funding anymore because their investors give up on them or b), they don’t get any revenue traction because they got the timing wrong or there just isn’t a standalone market for that capability… And once they’ve realized that, you can often buy these types of companies for a reasonable sum [...] [I 07] Then another important element that has to do with pricing is how much venture capital a firm has taken. When it has taken lots of venture capital, the price is typically high. [I 04] You just see it inevitably as companies grow, they are going to cost a lot more. [I 09]

The risk/uncertainty discount mechanism (D3) is not as prominent in a deal value calculation. Nevertheless, it can greatly affect the evaluation of synergies based on factors that jeopardize the realization of synergies. One such factor is based on the integration risk associated with an acquisition target. If integration fails, synergies are commonly lost.

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For example there was a company that was going to cost us 200 million to acquire, but the culture it was staying in - it worked in the same city as the acquiring business unit and there were great synergies with our products and the teams liked each other and maybe had already worked together. That might be the better option to go with, than the 15 million dollar company that provides the same kind of functionality, but is based on the other side of the world and has a very different culture. So it becomes much about just that monetary calculation and more about, can we drive the value—what is it that we try to get out of each acquisition and is there a better way to get it. [I 02]

Market risk also threatens the realization of synergies. Concretely, this is the case if a synergy calculation is based on the expectation of market traction of a target that does not yet have it at the time of acquisition. [...] there usually is that kind of discussion internally, which is like ‘Let's buy it now before it gets too big and too expensive.’ Other people might say ‘Hey let’s wait to see, if it gets big and if it does that takes a lot of the risk out, so you should pay for removal of that risk.’ So you should be willing to pay more, because now it doesn’t have that risk of [not] getting market traction and you know it does. [I 11]

There is no evidence for technology risk having a high relevance in driving the risk/uncertainty discount. For the threats to synergy realization, i.e., integration and market risk, lower risk correlates with a higher deal value. Conversely, higher risk is associated with a deal value discount. Acquirers pick their deal value calculation logic in such a way that it fits to the strategic rationale of the deal and the calculation mechanism. This may be an NPVbased calculation if traction is valued or a make-or-buy opportunity cost analysis if accelerating time to market is the objective. In principally talent-focused deals, a per-engineer-based valuation is common. Interestingly, acquirers seem to rely on benchmark deal value ranges that depend on the type of acquisition. Example ranges are ~$10–25 million for technology and team deals and ~$1 million per engineer in acqui-hires. Given that these benchmarks are sometimes likened to a flat-rate pricing, the derivation of a deal value appears to incorporate simple heuristics more often than not. So some of this ends up being more arts than science and in some circumstances it could be like really like regress, spreadsheets and modelling and more science. [I 11]

4.2.4.2 Deal value differences of performance- and functionality-focused acquisitions According to the acquirer synergies mechanism (D1) there are no systematic deal value differences between performance- and functionality-focused acquisitions. On

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the contrary, the target value (D2) mechanism holds that functionality-focused acquisitions have a higher deal value than performance-focused acquisitions. There is no evidence for a risk/uncertainty discount (D3) mechanism in relation to performance- and functionality-focused acquisitions so that it appears to be irrelevant in this context or extremely weak as compared to mechanisms (D1) and (D2). The left side of figure 11 summarizes these results. ✔ Yes ✘ No Deal value mechanism

Finding for performance and functionalityfocused acquisitions

Systematic effect on deal value same matu- across maturity levels1 rity levels2

▪ Acquirer D1 synergies

▪ There are no systematic differences

✘

✘

▪ Target value D2

▪ Functionality-focused acquisitions have a

✘

✔

▪ Risk/uncerD3 tainty discount

▪ The risk/uncertainty discount appears not to

✘

✘

regarding the deal value of performance and functionality-focused acquisitions. Synergies can be equally low or high in either case. higher deal value than performance-focused ones as targets in func-tionality-focused deals typically have higher revenues.

be relevant regarding the deal value of performance and functionality-focused acquisitions so that there is no effect.

Hyp. 2a3: not consistent Hyp. 2b4: consistent 1 Same maturity levels—compares targets in performance and functionality-focused acquisitions at same maturity level, i.e. prior to product launch or post product launch 2 Across maturity levels—takes timing differences of targets in performance and functionalityfocused acquisitions into account 3 Hyp. 2a: At comparable levels of target maturity (e.g. pre-market entry or post-market entry), the deal value of performance-focused acquisitions is, ceteris paribus, higher than that of functionality-focused acquisitions. 4 Hyp. 2b: Overall, the deal value of functionality-focused acquisitions is, ceteris paribus higher than that of performance-focused acquisitions.

Fig. 11: Deal value mechanisms in relation to performance- and functionality-focused acquisitions

Acquirer synergies (mechanism D1) may be low or high for performance- and functionality-focused acquisition depending on an acquirer’s specific need and the acquisition target’s offering. Additional revenue may stem from both performanceand functionality-focused acquisitions. Depending on the level of performance improvement from a performance-focused acquisition, the acquirer may either benefit only from a simple time-to-market acceleration that preserves revenues or saves

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cost, or gain a significant competitive advantage. Abstaining from a particular acquisition may even result in a future life-threatening situation for an acquirer. This can be the case for both a performance- and a functionality-focused acquisition. [...] there is no black and white answer to that. It really depends. So the underlying technology, let’s say makes our [service name] 5% more effective and we are serving [number] people a month. You know, how much is that worth? I mean we could model that out - that is worth so much. But if we are talking about [target name of functionality-focused acquisition]’s technology, [...] which drives up time spent and engagement. You know, you could compare that. And our team tries to do that. I push my team to do that. [...] but if you want to come up with a general rule, it just depends. [I 11] So I would make the distinction between is it some core unique technology that really enhances performance significantly or do you make the acquisition for performance where you just buy people and talent so you can accelerate your roadmaps. And I would say in the former you may pay quite a bit more but in the latter I don’t think so. So that is why I don’t want to be black and white between functional and performance businesses. [...] it really depends on a case by case basis. [I 10] In the end, the deal value depends on the type of expected benefit. And I can imagine a large benefit for performance that might even go so far as to say that without the performance advantage I need to close down my company. And I can imagine the same also for functionality. Therefore I believe that one cannot make a generalized statement. [I 18]

One may attempt—albeit fail—to build an argument contradicting the logic described above based on a comparison of market growth rates of performance- and functionality-focused acquisitions. The reasoning is as follows. Acquirers choose a target for a functionality-focused acquisition based on two conditions. First, the target needs to have market traction with a new product. Second, its market growth rate has be to higher than that of an acquirer’s existing product whose functionality is supposed to be expanded using the acquisition target’s technology. The second condition assumes that an acquirer would not even consider the acquisition in the opposite case because revenue growth is the ultimate goal of an acquisition. The technology of a performance-focused acquisition would likely be plugged into an acquirer’s existing product that—based on the argument given above—has a lower growth rate. Hence, synergy expectations should be higher for a functionalityfocused acquisition than for performance-focused ones. This argument fails, however, because it implies that the growth rate of an acquirer’s existing product remains stable after a performance upgrade following a performance-focused acquisition. A stringent logic requires that the assumption of revenue growth as an acquisition’s ultimate goal applies to performance-focused acquisitions in the same manner as functionality-focused ones.

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Viewing the deal value of performance- and functionality-focused acquisitions in the light of mechanism (D2) yields that functionality-focused acquisitions have a higher target value. The reason is that functionality-focused acquisitions are more likely to have market traction than performance-focused acquisitions. This traction drives the deal value. Talent is only in a sub-10-million-dollar range depending on the size of the team give or take and then maybe a little bit more, more in the 10, the 50, the 60 million dollar range for a performance- based technology play and then if you are actually valuing the functionality, you can get into the hundreds of millions or even billions, if there is a significant amount of traction. [I 11]

Interestingly, the calculation of synergies in some situations seems to follow the assessment of target value in terms of market traction. […] for functionality it is something new, which has to work and has to have some market validation, usually if you bring the functionality, you want to buy the best in that functionality. So as a result you tend to pay more for it, I would say, and you also have to make a more difficult case, to justify that valuation you are going to pay by exposing more synergies and so forth. [I 10]

4.2.4.3 Key results An acquisition deal value is the outcome of a negotiation between target and acquirer. A necessary pre-condition for a deal is that a certain deal value proposal put forward by the acquirer is above target shareholders’ reservation price. The reservation price is determined by the degree of a target’s commercial success, the amount of external funding it has received and the target’s maturity based on the timing of the acquisition57. Naturally, the reservation price influences the amount paid by an acquirer once negotiations have been concluded. An acquirer’s deal value proposal is the result of three mechanisms that act in parallel. These are an estimation of an acquirer’s synergies, an assessment of the target value from an acquirer perspective and a discount based on the degree of synergy risk that goes along with the acquisition. The calculation of an acquirer’s synergies incorporates the strategic rationale of the deal and is driven by synergistic future revenue, revenue preservation and cost savings stemming from not having to develop a technology internally and a potential publicity effect. The target’s value from an acquirer perspective is determined by what an acquirer cares about in the acquisition target. This can be revenue and traction, technology and team but possibly not administrative staff. Synergy risk stems from the chance that synergies are || 57 The presence of other potential buyers should also play a role but was not explicitly mentioned in interviews.

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not realizable due to a failed integration or market traction failing to materialize. Hence, integration and market risk drive synergy risk. There is no evidence for technology risk as a driver. In the context of performance- and functionality-focused acquisitions, the first mechanism yields that there is no systematic difference between performance- and functionality-focused acquisitions because there are scenarios of low and high synergy levels in each case. According to the second mechanism, functionality-focused acquisitions have a higher deal value than performance-focused ones. This is because functionality-focused acquisitions—as opposed to performance-focused ones—have traction that is included in the assessment of the target value. There is no evidence that mechanism three, a risk/uncertainty discount, plays a role in driving systematic deal value differences of performance- and functionality-focused acquisitions.

4.3 Discussion of qualitative results and conclusion There were three objective of this research. The first one was to determine if the distinction of performance- and functionality-focused acquisitions in the ICT can reasonably be made. The second and third one were to understand how and why decisions with regard to acquisition timing and acquisition deal value are made and how their outcomes differ with respect to performance- and functionality-focused acquisitions. The analysis of the 21 interviews conducted for the qualitative research followed these objectives. First, the framework of performance- and functionalityfocused acquisitions was studied in relation to known types of acquisitions. Second, a detailed analysis of risk in technology-focused acquisitions was conducted because the interviews emphasize the relevance of risk and theory predicts risk and uncertainty as a key driver of acquisition timing and deal value decisions. Third, acquisition timing and fourth, deal value mechanisms are analyzed in the context of technology-focused acquisitions in ICT and within the framework of performanceand functionality-focused acquisitions. Overall, qualitative research is in agreement with theoretical predictions to a large extent. The distinction between performance- and functionality-focused acquisitions can readily be made. Risk is important in technology-focused acquisitions in ICT and overall risk is lower for performance-focused acquisitions than for functionality-focused acquisitions. With respect to acquisition timing, performancefocused acquisitions take place earlier than a functionality-focused acquisition. Both types of acquisitions differ in terms of deal value. An important difference between theory and qualitative findings is that risk appears to play no relevant role in driving deal value differences of performance- and functionality-focused acquisitions.

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In the following, I will provide an in-depth discussion of the qualitative findings in the light of theory. I will show where qualitative results are in line with theory and explicitly point out differences. Also, the qualitative research led to the emergence of some additional findings and phenomena. These do not contradict but rather complement and augment theory and, thus, stand for the richness of qualitative research. In the following, the structure outlined in this paragraph is applied to each of the four analytical steps listed above. Qualitative research shows in agreement with theory that the concept of performance- and functionality-focused acquisitions is novel and constitutes a meaningful distinction in the context of technology-focused acquisitions as a strategic acquisition rationale (Bower, 2001). A contribution of qualitative research is to impose an ordering of acquisition types based on their proximity of the target’s business and technologies to that of the acquirer. This perspective yields that performance-focused acquisitions are rather close to an acquirer’s core business and share this property with acqui-hires while functionality-focused acquisitions are further removed and correlate to some extent with product additions. In the same way as theory, qualitative research recognizes the existence of straightforward or pure cases and sometimes rather complicated mixed cases—based, e.g., on leapfrogging (Götz and Astebro, 2006; Lee and Lim, 2001)—of performance- and functionality-focused acquisitions. Classification difficulties arise due to selecting proper performance dimensions, performance enabling new functionality or the ambiguity of the phrase “improving a product”. Theory and qualitative research agree that the acquisition categories performance-focused or functionality-focused are endogenous as they depend on the characteristics of a specific acquirer. Qualitative research—as opposed to theory—scratches on the surface of the technological foundations of a performance-focused or a functionality-focused acquisition by— somewhat sloppily—defining them as a “swap outs” and “gap fillers”, respectively. With respect to risk and uncertainty, qualitative research shows that market risk and technology risk—that are closely related to market uncertainty and technology uncertainty—are highly important in technology-focused acquisitions (Chaudhuri, 2005; Chaudhuri et al., 2005; Iansiti, 1995; Krishnan and Ulrich, 2001). In line with literature, qualitative research finds that financial risk and integration risk as part of synergy risk, play a role in technology-focused acquisitions (Coff, 1999). Qualitative research and theory agree with regards to the risk profiles of performance- and functionality-focused acquisitions. Market risk is higher for functionality-focused acquisitions mainly due to customer fit that is less clear. Technology risk is generally low but higher for performance-focused acquisitions because certain performance parameters need to be achieved. Qualitative research, as well as theory see the observability or measurability (Garvin, 1984) of performance as a central mechanism for lowering this risk. Qualitative research adds the distinction of basic research risk and development risk within this context. Overall, performance-focused acquisitions have a lower risk than functionality-focused ones.

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While theory derives this result with respect to a temporal dimension, qualitative research connects it to time at best implicitly. As expected, the qualitative results show in accordance with theory that acquisition timing is driven by risk and uncertainty. In addition, the notion of a risk or uncertainty threshold is present in both qualitative research and theory and confirms the connection to strategic decision making (Jensen, 1982; March, 1994). However, qualitative research contributes three more mechanisms for acquisition timing that may act jointly with the risk and uncertainty based mechanism and complement rather than replace it. Waiting is a viable acquisition strategy to influence acquisition in the first three mechanisms-completely in line with theory (Ransbotham and Mitra, 2010; Toxvaerd, 2008). All mechanisms except the last one are in line with the main theoretical prediction (hypothesis 1a—see section 3.4.2) that performance-focused acquisitions happen earlier in a target’s life cycle (see right side of figure 9). However, the last mechanisms predicts the opposite outcome but acts on a different level than the first three and can be voided by an acquirer using straightforward measures. Qualitative research qualifies the theory developed in section 3.4 by specifying the need to view general timing tendencies of performance- and functionality-focused acquisitions within the concept of windows of opportunity. This concept along with the proposed pre-requisites for an acquisition from a seller’s perspective are largely in line with extant literature (Graebner et al., 2010; Graebner and Eisenhardt, 2004). The findings for windows of opportunity from an acquirer’s perspective agree with respect to extant literature (see e.g., Iyer and Miller (2008)) in that acquirer’s performance drives acquisitions. Windows of acquisition opportunity help explain cases that seemingly contradict the prediction of performance-focused acquisitions happening earlier than performance-focused ones. The qualitative research on the deal value decision virtually replicates the auction-based framework used for determining an acquirer’s deal value proposition (Bulow et al., 1999; Dittmar et al., 2012; Ransbotham and Mitra, 2010). Common value largely corresponds to target value as assessed by an acquirer modified by target shareholder’s own valuation of their company. By adding the target shareholder’s perspective, qualitative research enriches the overall picture of deal value mechanisms in technology-focused acquisitions. Private value has its equivalent in acquirer synergies. An uncertainty/risk discount (Ransbotham and Mitra, 2010) is present in both qualitative research and theory. In this context, qualitative research highlights the relevance of integration risk. In accord with theory, qualitative research yields that private value/acquirer synergies do not differ systematically for performance- and functionality-focused acquisitions. Common value/target value is higher for functionality-focused acquisitions. Thus, qualitative research is consistent with hypothesis 2b (see section 3.4.3). In contrast to theory, qualitative research does not provide evidence for technology risk driving the risk/uncertainty discount in technology-focused acquisitions. Nor does it provide any evidence for

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the relevance of the risk/uncertainty discount with respect to the deal value of performance- and functionality-focused acquisitions. Hence, qualitative research is not in line with hypothesis 2a (see section 3.4.3 and figure 11 in subsection 4.2.4.2 for a full overview). Acquirers, however, may view a risk/uncertainty discount as naturally incorporated in the calculation of synergies and target value so that they do not evaluate it as a separate contributing factor to deal value. If this were the case, the risk differentials described in subsection 4.2.2.3 would clearly indicate a higher discount for functionality-focused acquisitions in line with hypothesis 2a. In conclusion, qualitative research is largely in line with theory. Especially the predicted differences of performance- and functionality-focused acquisitions with respect to risk and uncertainty and acquisition timing (hypothesis 1a) are consistent with qualitative findings. In a similar vein, the deal value differences between performance- and functionality-focused acquisitions that can be purely explained by a maturity effect (hypothesis 2b) are in line with qualitative results, as well. This is not true for the predictions of deal value differences between the two types of technology whose reasoning hinges solely on a risk/uncertainty discount mechanism (hypothesis 2a) since here the same levels of maturity are assumed. This finding, however, needs to be seen in the light that a comparison of deal value between performance- and functionality-focused acquisitions at equal maturity levels may be somewhat hypothetical and/or difficult to perform in an interview situation. Naturally, the qualitative research has some limitations. While sample selection and retrospective reporting bias have been covered in sections 4.1.2 and 4.1.3, I want to highlight some issues regarding external validity or generalizability that persist despite having chosen a rigorous research approach (Eisenhardt, 1989; Glaser and Strauss, 1967). Focusing on the ICT industry has the advantage of a limited scope meaning that some factors are constant and can either be disregarded or do not show up in the analysis. Such factors may be the relevance of performance dimensions or standard product development or decision-making approaches. However, it might be these factors that vary for other industries possibly because of industry life cycle differences (Jones et al., 2001). This may pose a threat to external validity. This issue is somewhat attenuated by the fact that the ICT industry itself consists of multiple sub-industries based on distinctions such as software vs. hardware or B2B vs. B2C. The interviews have been drawn from a variety of these sub-industries improving external validity. While in total 21 interviews were conducted, the number of topics discussed in each interview was obviously limited. Thus, there are sometimes only some key statements in support of a specific phenomenon, which might reduce external validity. However, great care was taken to cover a multitude of perspectives to mitigate this potential issue. The selection of interview partners covered different backgrounds, namely heads of M&A of serial acquirers, founders of acquired and nonacquired startups—one founder of a non-acquired startup had in fact received an informal acquisition offer. In addition, a plethora of secondary data was consulted.

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An additional source of limitations may be a subjective approach to pattern matching.

5 Quantitative study—Performance and functionality in AI-related acquisitions The key objective of the quantitative study is to develop a deeper and richer understanding of the phenomenon of performance- and functionality-focused acquisitions. This is achieved by not only testing the findings of theory and qualitative research but also by complementing and extending them with novel insights. The quantitative study is thus grounded in the results of theoretical considerations (chapters 2 and 3) and the qualitative study (chapter 4). Combining qualitative and quantitative research as a mixed methods approach is particularly useful to study decision making within the context of performanceand functionality-focused acquisitions. This is true for several reasons. Mixed methods research enhances not only overall validity (Jick, 1979; Yauch and Steudel, 2003) but also the validity of new measures through building the quantitative research upon the qualitative results (Edmondson and McManus, 2007). The application of qualitative and quantitative methods to the same research problem somewhat compensates for the weaknesses of one method and builds upon the strengths of both (Creswell, 2014; Creswell and Plano Clark, 2011). The qualitative analysis in chapter 4 provides rich descriptions of the decision making mechanisms and drivers with respect to performance- and functionality-focused acquisitions. These clearly inform the quantitative analysis and enable a fuller interpretation of results (Yauch and Steudel, 2003). Confidence in qualitative results is improved through convergence (Edmondson and McManus, 2007). However, even diverging results provide additional insights (Jick, 1979). This chapter is organized as follows. Section 5.1 describes the methodology. In section 5.1.1 the empirical setting is presented introducing artificial intelligence (AI) software as the focal sub-industry of the quantitative study. Section 5.1.2 explains the sampling approach chosen for this study. Data and data collection are elaborated on in section 5.1.3. The variables of the quantitative model are the focus of section 5.2. Concretely, the choice of dependent variables is explained in section 5.2.1 while section 5.2.2 deals with the independent variable and section 5.2.3 with the controls. Data analysis results are described in sections 5.3 (descriptive results) and section 5.4 (results of hypothesis testing). Hypothesis test results are reported separately for the hypotheses on acquisition timing (section 5.4.1) and deal value (section 5.4.2). Discussion and conclusion follow in section 5.5.

DOI 10.1515/9783110562095-005

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5.1 Methodology 5.1.1 Empirical setting—Acquisitions in AI The quantitative study aims at testing the hypotheses on performance- and functionality-focused developed in sections 3.4.2 and 3.4.3. To this end, a large sample of acquisitions needs to be constructed consisting of a sufficient number of performance- and functionality-focused acquisition. As argued in section 4.1.2 the ICT industry and specifically the software industry represents an ideal setting for both types of technology-focused acquisitions (Brueller et al., 2015; Chaudhuri, 2005; Ranft and Lord, 2002). However, the software industry by itself is very broad consisting of multiple sub-industries among which the occurrence of performance- and functionality-focused acquisitions is not evenly distributed. For the quantitative analysis, I choose artificial intelligence (AI) software as the focal sub-industry within the software industry for the following three reasons. First, I expect a rather balanced distribution between performance- and functionality-focused acquisitions than within other sub-industries as AI is inherently performance driven and there is a rich set of applications. Second, AI as a sub-industry represents a technologyperspective as opposed to an application perspective, which would be assumed by focusing, e.g., on product lifecycle management (PLM) software as a sub-industry. The focus of this thesis is to study the relationship between technology and decision making instead of application and decision making so that a technology perspective is clearly warranted. Third, I expect a technology perspective to somewhat circumvent confounding effects that might emerge by having separate functionalities that are based on rather unrelated technologies within the same application as they might be driven by very different dynamics58. In the following, I will provide some background on the field of AI and the AI industry. In this context, I will elaborate on the first argument provided in the previous paragraph, i.e., the expectation of a relatively balanced occurrence of both performance- and functionality-focused acquisitions. The notion of artificial intelligence is originally based on the idea that “the brain can be viewed as an information processor” (Steels, 2007). There are multiple definitions of artificial intelligence which relate it to mimicking some aspect of human behavior such as “thinking humanly”, “acting humanly”, “thinking rationally” and “acting rationally” (Russel and Norvig, 2010). The Turing Test59 provides an operationalized definition of intelligence (Russel and Norvig, 2010). Research on artificial intelligence is in || 58 An example might be technologies for product data management and photorealistic rendering within the PLM industry. The underlying technologies of each functionality are likely separate. 59 The Turing Test was designed by Alan Turing. According to the test, a computer can be considered intelligent if it answers a set of written questions such that a human interrogator is incapable of distinguishing whether responses stem from a computer or a human (Russel and Norvig, 2010).

Methodology | 115

fact informed by a great variety of disciplines ranging from mathematics over economics to computer engineering (Russel and Norvig, 2010). From a technological perspective, the field of AI is comprised of a broad set of concepts, tools and algorithms that include intelligent agents, search, and machine learning (Russel and Norvig, 2010). These are applied to a great variety of tasks and problems such as game playing, natural language processing and machine translation, product recommendation, robotic (self-driving) vehicles, spam fighting, or speech recognition (Jordan and Mitchell, 2015; LeCun et al., 2015; Russel and Norvig, 2010). These applications are—at their very core—often driven by machine learning. Indeed machine learning stands out “as the method of choice for developing practical software” (Jordan and Mitchell, 2015). From a historical perspective, artificial intelligence dates back to the 1940s while the field was officially founded in 1956 (Russel and Norvig, 2010). Only in the 1980s did AI move from research to applications and transform into an industry (Russel and Norvig, 2010; Steels, 2007)60. In recent years interest in the field of artificial intelligence has sky-rocketed as evidenced by annual equity financing in AI having increased from $282 m to $2,388 m from 2011 to 201561 (CBInsights, 2016). This interest is mainly fueled by performance increases of AI algorithms over the last years. The AI industry is inherently performance driven as the notion of performance is deeply ingrained in AI algorithms. This is best explained on the example of machine learning. The objective of machine learning is essentially to fulfill a given task—such as classifying an e-mail as spam or not spam—based on training or “learning” the computer using a dataset (Jordan and Mitchell, 2015). The execution of this task by a machine learning algorithm is always associated with a measurable performance dimension such as classification accuracy or error rate. Performance in artificial intelligence is driven by the amount of available training data, conceptual and algorithmic advances, and hardware improvements (Brundage, 2016; Jordan and Mitchell, 2015; LeCun et al., 2015)62. In recent years, breakthroughs have been achieved on all three fronts due to the availability of enormous online data sets (Jordan and Mitchell, 2015), the use of deep neural network-based algorithms for machine learning (LeCun et al., 2015; Schmidhuber, 2015) and hardware-related improvements, e.g., exploiting faster CPUs and GPUs (Brundage, 2016; LeCun et al.,

|| 60 The early enthusiasm in AI combined with spectacular but intractable promises eventually gave rise to what was called the “AI winter” (Russel and Norvig, 2010)—a period of funding cuts and reduced interest. 61 Numbers based on disclosed deals only. 62 These factors are not ordered by relevance of their contribution. Brundage (2016) states that “algorithmic progress contributed about 50—100% as much to improvement in performance as did hardware progress”, while Russel and Norvig (2010) stress data availability as a driver of performance advances.

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2015) and parallel computing (Jordan and Mitchell, 2015). The ImageNet Large Scale Visual Recognition Challenge63 in which the world’s top AI researchers compete on classifying and detecting objects within a given dataset of images (Russakovsky et al., 2015) exemplifies the rapid performance improvements within the last few years alone. Figure 12 shows the improvements of object classification error rates of winning teams since the challenge’s inception in 2010 until 201664. Based on the ongoing performance improvements within AI and the great variety of applications of AI, I expect that there is not only ample opportunity for product development efforts but also for acquisitions directed at performance improvement or functionality addition. This should render the AI-industry a perfect setting for studying performanceand functionality-focused acquisitions with an expectedly balanced occurrence of both types of acquisitions. Image 30 classification error (%) 25

28.2 25.8

20 16.4 -89%

15 11.7 10 6.7 5

0 2010

3.6

11

12

13

14

15

3.0

2016 Year

Fig. 12: Performance improvement of winning teams in ImageNet competition

|| 63 http://image-net.org/ 64 Values for error rates found in (http://image-net.org/challenges/LSVRC/2015/results; http://image-net.org/challenges/LSVRC/2016/results; Russakovsky et al., 2015)

Methodology | 117

5.1.2 Sampling of AI-related acquisitions Quantitative research using statistical inference is ideally built upon a random sample to minimize bias and assign a clear meaning to probabilities (Berk, 2004). While a true random sample is an elusive goal, this research took great care to come close within the constraints of the empirical setting and the relationships to be tested. In addition to near-randomness, the sample was required to fulfill three criteria. First, acquisition targets need to be active within the AI industry. Second, the acquisitions need to be technology-focused. Third, the sample has to be large enough and contain a sufficient variety between performance- and functionality-focused acquisitions for econometric analysis. Acquisitions within the sample took place between 2005 and 201565. This timeframe was chosen to benefit from the recent AI and acquisition boom and to optimize availability and accessibility of secondary data (Puranam and Srikanth, 2007). The sample was then generated having the goal in mind of ideally identifying all acquisitions fitting these boundary conditions. No constraint was imposed on the geographic location of acquirers or targets. Serial as well as non-serial acquirers were sampled to analyze the effect of acquisition experience. Obvious sources of M&A data are the various M&A databases such as ThomsonOne and Crunchbase. Commonly, these allow filtering by industry using industry names or codes such as SIC66 or NAICS67. Unfortunately, there is no such code available for AI so that a different approach had to be followed. A keyword list consisting of 479 AI-related terms was generated to filter the business descriptions of acquisition targets that are usually available in M&A databases. The keyword list was compiled drawing from various sources of AI-related terminology such as the book by Russel and Norvig (2010). Two experts in AI, one senior researcher and one senior PhD student verified the entire keyword list regarding relevance and completeness. To facilitate this process, keywords were categorized and sorted on three levels in advance. A rather broad definition of AI was applied in the formation of the keyword list. See appendix A.1 for details on the creation, structure and contents of the keyword list.

|| 65 Date of acquisition announcement was used as a reference point. 66 SIC—Standard Industrial Classification 67 NAICS—North American Industry Classification System

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Step Filter 1 (Baseline 2005-15) 2 Keyword list

Google CB Acqui. TO Acqui. Acqui. data data data

Database combinat. 3 Data cleaning 4 Acquisition rating 1 5 PvF categorization

Data source (# of acquisitions): ThomsonOne (TO) Crunchbase (CB) 52,829 17,919 (all in(software) dustries) 601

413

Google search

289

1139 (601 TO, 352 479 artificial intelCB, 186 Google) ligence keywords used for filtering 794 298 PvF1 rating—interrater reliability 246 (78 perform., Agree- Cohen’s 168 functionality) ment Kappa 91% 78%

1 PvF—performance- vs. functionality-focused acquisition measured on a four-level scale

Fig. 13: Sampling approach

The generation of the sample followed five steps: (1) Acquisition baseline creation, (2) keyword-based filtering, (3), database combination and cleansing, (4) rating of AI and technology-focus, and (5) coding in terms of performance- or functionalityfocus. Figure 13 illustrates this approach and displays the size of sample at each step. In step (1) all acquisitions with NAICS codes 33461468, 51121069, and 51913070 in the given timeframe were retrieved from ThomsonOne and saved in a first database. All acquisitions from Crunchbase71 were compiled in a separate, second database. The result is a baseline of acquisitions between 2005 and 2015. In step (2) the keyword list was used to filter target business descriptions from ThomsonOne and Crunchbase72. Comparing the filtering results to a Google search

|| 68 334614—Software and Other Prerecorded Compact Disc, Tape, and Record Reproducing (www.census.gov). Note that the NAICS 2002 and 2007 codes 334611 and 334612 were aggregated into 334614. 69 511210—Software Publishers (www.census.gov) 70 519130—Internet Publishing and Broadcasting and Web Search Portals (www.census.gov) 71 A pre-selection of Crunchbase data based on NAICS code is neither necessary, nor is it possible. Acquisitions in Crunchbase have a natural focus on high tech industries, specifically ICT. NAICS codes are not provided. 72 Initially keywords such as “search engine” or “data mining” were included in the acquisition target search process. However, initial screening showed that most acquisition targets that were

Methodology | 119

revealed that some, predominantly smaller AI-related acquisitions either were not listed in ThomsonOne and Crunchbase or could not be found due to generic business descriptions in these databases. Hence, web searches based on the top ten keywords73 were conducted and stored in a third database. In step (3) the results from all three databases (ThomsonOne, Crunchbase, and Google search) are merged into a single dataset removing duplicates. This step results in a sample size of 1139 data points. Data cleansing reduced the sample size to 794 acquisitions. The cleansing procedure comprised inter alia the removal of mere investments, announced M&A transactions that did not close, acquisitions by private equity firms that obviously had no technology focus, and acquisitions with an unknown acquirer. Step (4) focuses on the rating of the remaining 794 acquisitions as to whether the target is active in the AI industry and whether the acquisition was technologyfocused. Rating followed a similar procedure as that described in Puranam et al. (2009). Two raters—one of them being the author of this study—rated the entire sample. A third, independent expert rater was assigned the task of a consensus rater to resolve disputed cases. Raters were provided with a pre-tested coding scheme (Léger and Quach, 2009). The rating of AI-relatedness and technology-focus were performed sequentially in that order. After the first round of rating 340 acquisitions remained in the sample. Acquisitions were dropped on the grounds that there was no evidence for the target’s product or service being based on AI technology. Such cases occurred when the AI-related keyword in the target’s business description was used as a marketing buzzword or in a non-AI-related context. Often times, however, simply no information on the target beyond the business description were available. An inter-rater agreement between the first two raters of 89% was achieved. The third rater and expert was a senior doctoral student in AI. For the remaining set of acquisitions the technology-focus of each acquisition was rated. Extant literature provides varying definitions of technology-sourcing via acquisition that are based either on “hard facts” such as target patenting, target size or deal value (Ahuja and Katila, 2001; Makri et al., 2010; Puranam and Srikanth, 2007; Ranft and Lord, 2002) or on the analysis of the acquisition intent (Makri et al., 2010; Puranam et al., 2009). This thesis follows a combined approach. Refer to appendix A.2 for details on the rating procedure. The new sample size is 298 acquisitions. Exclusion of an acquisition from the sample was mainly due to non-technology-related acquisition motives being most prominent. Again, an inter-rater agreement between the first two raters

|| identified based on these keywords were false positives, i.e., they were not active in AI. Hence, these acquisitions were dropped from the sample if they were not found via a different keyword. 73 An example search string is “acquires computer vision startup”. Keywords were ranked by frequency of occurrence in the ThomsonOne and Crunchbase databases.

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of 89% was achieved. The third rater and expert was a post-doctoral student in innovation management. In step (5) all 298 acquisitions in the remaining sample were categorized either as a performance-focused acquisitions, functionality-focused acquisitions, or as a mixture on a four-level ordinal scale. The levels were 1—clear performance focus, 2— major performance- and minor functionality-focus, 3—minor performance- and major functionality-focus, and 4—clear functionality-focus. Categorization of mixed or unclear cases was based on analyzing cues as to whether performance or functionality are more important in the acquisitions such as the salience or explicitness of the intended use of the acquired technology. For performance-focused acquisitions, it was made sure that the acquirer had a prior technology or product whose performance could be improved. See appendix A.3 for full account of the rating procedure. Great care was taken to determine acquisition motivations as acquirers often do not always reveal the real reasons for an acquisition, especially if they would be controversial (Angwin, 2007). However, performance improvement or functionality addition are certainly more acceptable acquisitions motives than e.g., hubris (Angwin, 2007) and should therefore suffer less from issues of misstated motivations or window-dressing. Two experts including the author of this thesis performed the rating using a predefined coding scheme. Disagreement was resolved through discussion. The resulting sample consists of 246 acquisitions—78 of those have a clear or major performance-focus and 168 have a clear or major functionalityfocus. Acquisitions were dropped from the sample if only insufficient or unconvincing information was available. A weighted inter-rater agreement (four-level scale) of 91% and a weighted Cohen’s kappa of 78% indicating substantial agreement (Hallgren, 2012) were achieved. For all rating procedures employed in steps (4) and (5) numerous secondary sources were coded. In step (5) alone, the two raters coded a combined number of over 530 secondary sources, i.e., a little more than two sources per acquisition on average but up to five sources for complex cases. Secondary sources consisted preferably of acquisition announcements and press releases by acquirers but also encompassed analyst reports, news articles on websites such as TechCrunch74 or Gigaom75 and archived target websites76. For all rating efforts the two primary raters wrote short statements for each acquisition laying down the reasoning for their rating. These usually included key quotations from secondary sources. Inter-rater agreements of 89% in step (4) and 91% in step (5) are in line with those achieved by other authors in the range of 85% to 98% (Lane and Lubatkin, 1998; Maxwell et al.,

|| 74 https://techcrunch.com/ 75 https://gigaom.com/ 76 Those were usually retrieved (https://archive.org/web/)

via

the

Internet

Archive's

Wayback

Machine

Methodology | 121

2011; Puranam et al., 2006; Puranam et al., 2009; Fischer and Henkel, 2012). The same applies to Cohen’s kappa where the results of other authors are in the range of 71% to 98% (Sutcliffe and McNamara, 2001; Stahl and Voigt, 2008; Nadler et al., 2003; Das and Van de Ven, 2000; Sabherwal and Chowa, 2006). In summary, a near-random sample of 246 technology-focused acquisitions in the field of AI was generated. Thereof, about one third have a clear or major performance-focus and roughly two thirds have a clear or major functionality-focus. Hence, all of the above criteria have been fulfilled rendering the sample suitable for testing of hypothesis on acquisition timing and deal value (see section 5.4). Section 5.3.1 provides a detailed description of the sample.

5.1.3 Data and data collection The main sample consists of 215 observations. This number is smaller than that of the original sample (see section 5.1.2) due to missing data. A size of 215 observations is in a similar range as that of other research on technology-focused acquisitions (Ransbotham and Mitra, 2010; Makri et al., 2010; Puranam and Srikanth, 2007; Puranam et al., 2009). Overall, data for 20 variables was collected over the timeframe of roughly one year. Even though the acquisitions in the dataset took place anytime within the timeframe 2005—2015, the dataset was treated as crosssectional. This was done as data analysis is not focusing on trends, patterns or the evolution of variables over time but rather in the variation among observations (Doane and Seward, 2013; Stock and Watson, 2007). Data collection is entirely based on secondary sources, mainly M&A databases and documents accessible via web search. The databases utilized for data collection comprise in alphabetical order Bloomberg, CrunchBase, MergerMarket, PATSTAT, ThomsonOne, Zephyr (Bureau van Dijk). All of them except PATSTAT contain extensive data on global M&A deals and often on public and private firms in general. PATSTAT was solely used for retrieving an acquisition target’s patenting data. For most variables, additional data was gathered via searching the web. Information accessed this way was found within sources such as acquisition press releases, news reports, published interviews, archived acquisition target’s websites, and LinkedIn profiles. For some variables, web search was the only source of data. The approach of combining several independent sources of data has been successfully utilized by other researchers to maximize accuracy and reliability (Ahuja and Katila, 2001; Chaudhuri, 2005; Ransbotham and Mitra, 2010). See table 3 for an overview of variables, their descriptions and the sources from which data was drawn. For many acquisitions data collection proved to be difficult due to missing data in M&A databases. Often an acquisition target’s website had been taken offline shortly after the acquisition. In these

122 | Quantitative study—Performance and functionality in AI-related acquisitions

cases the Wayback Machine of the Internet Archive77 proved to be a valuable source of information as it allows viewing the state of a website at specific dates in the past.

5.2 Variables The purpose of this section is to provide an overview of the variables used in the quantitative study, the motivation for their choice, their measurement and their treatment in extant literature, if applicable. This chapter starts with a discussion of the dependent variables that operationalize acquisition timing and deal value in section 5.2.1. Independent variables and controls are the focus of section 5.2.2. As noted previously, table 3 provides an overview of the variables and their description ordered by the type of variable in regression analysis. The variables used in the sections on descriptive results (5.3) and hypothesis testing (5.4) follow a specific naming convention and are abbreviated. They are shown in the third column of table 3. Variable names related to the acquisition target start with “T”, those related to the acquirer start with “A” and those pertaining to the acquisition or the acquisition environment start with a “D” for “deal”. Tab. 3: Variable names, descriptions, and sources

Sources3

Type

Variable name

Variable in model Description

DV1

Target Age

TAge

DV1

Target Product TProd_Avail Availability

Indication if target had launched a product (1) or not (0) at the time of the acquisition

WS

DV1

Target Size

TSize

Target's number of employees

CB, TO, WS, ZP

DV1

Deal Value

DDeal_Val

Acquisition deal price ($ million)— includes estimates and rumors

BB, CB, MM, TO, WS, ZP

DV1

Deal Value per DDV_TS Target Employee

Acquisition deal price ($ million) per number of target employees

BB, CB, MM, TO, WS, ZP

IV2

Deal Technolo- DTech_Type gy Type

Acquisition technology type in terms of performance- or functionalityfocus on an ordinal scale (1)–(4)

WS

IV2

Deal Technolo- DTech_Type_Fun gy Type (Dummy)

Acquisition technology type coded as WS performance-focus (0) or functionality-focus (1)

|| 77 https://archive.org/web/

Age of the target at the time of acqui- BB, CB, WS sition (years)

Variables | 123

Variable in model Description

Sources3

Control Acquirer Age

AAge

Age of the acquirer at the time of acquisition (years)

BB, CB, WS

Control Acquirer Experience

AExperience

Number of acquirer's acquisitions in 5 years prior to focal acquisition

CB, TO

Control Acquirer Size

ASize

Acquirer's number of employees

TO, WS, ZP

Control Deal Domestic

DDomestic

Indication if acquirer and target are from same country (1) or not (0)

BB, TO, WS

Control Deal Full Acquisition

DFullAcquisition

Indication if acquisition target was fully acquired (1) or not (0)

TO, WS

Control Deal in Seller’s DSellersMarket Market

Measure of the degree to which a seller's market in AI-acquisition is present

CB, TO, WS

Control Target Founder TFounder_AIBG AI Background

Indication if target's founder in WS technical role has a background in AI (1) or not (0)

Control Target Founder TFoundScientist er_Scientist

Indication if target’s founder in technical role performed university research at PhD-level and above (1) or not (0)

Type

Variable name

WS

Control Target Founder TFounder_TechBG Indication if target's founder in Technology technical role has degree in technolBackground ogy related subject (1) or not (0)

WS

Control Target Funding TFunding

Total amount of external funding received by target ($ million)

BB, CB, TO, WS

Control Target Patenting

TPatent

Number of patents associated with target prior to its acquisition

PS, WS

Control Target Private Status

TPrivate

Indication if target is private company (1) or not (0)

TO, WS

Indication if target is a spin-off from a university or research institute (1) or not (0)

WS

Control Target Spin-off TSpin_Off

1 DV—Dependent variable 2 IV—Independent variable 3 BB—Bloomberg; CB—CrunchBase; MM—MergerMarket; PS—PATSTAT; TO—ThomsonOne; WS— Web search (e.g., in acquisition press releases, news reports, published interviews, archived acquisition target’s websites, LinkedIn profiles); ZP—Zephyr

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5.2.1 Dependent variables—Acquisition timing and deal value 5.2.1.1 Acquisition timing Acquisition timing refers to the maturity level of the acquisition target at the time of acquisition. I use three measures to operationalize the construct of an acquisition target’s level of maturity, Target Age (TAge), Target Size (TSize) and Target Product Availability (TProd_Avail). The aim of using three measures is to triangulate the above construct from different perspectives to obtain a more accurate, multifaceted representation with higher generalizability. In the following, I will shortly elaborate on each measure’s definition, meaning, and the distinct advantages and disadvantages of its utilization starting with the last one. Target Product Availability (TProd_Avail) measures target maturity in terms of the launch of the target’s first product78. This measurement is relative to a specific point in time of the target’s life cycle so that the variable is dichotomous (before or after market launch) by definition. Target Product Availability is a novel measure for a target’s maturity but shares similarities with other event-based measures such as before or after an IPO that are described as indicators for a company’s change in its maturity level (Graebner et al., 2010)79. The core benefit of using Target Product Availability is its comparability across acquisition targets. Due to its dichotomous nature and the unequivocal definition of a product launch event, it is resistant to the influence of effects that cause the prolongation of the period until market launch for some startups but not for others such as unexpected technical difficulties or sudden changes in market dynamics. In addition, the qualitative study (see chapter 4) has provided evidence that a target’s product market launch is meaningful from an acquirer’s perspective. At the same time, however, the dichotomous nature of Target Product Availability is its greatest disadvantage. Acquirers might view the period a few months after market launch in the same way as the few months prior to market launch from a risk and uncertainty perspective depending on specific circumstances. An example may be a delay in product adoption. This indicates that a strict 1/0 coding might just “miss the mark” in assessing target maturity from an acquirer’s perspective. An additional disadvantage is a possible bias towards product availability (coded as “1”) at the time of acquisition regardless of performance or functionality-focus. Two reasons speak for this possible bias. First, a product market launch likely draws significant attention to a startup and it might be the first time that a potential acquirer realizes its existence. Second, stealth startups may make || 78 Data for this variable was collected by coding of secondary data found via web search. Especially the target's website as shown prior to the acquisition proved helpful. Prototypes or beta tests with test-customers were coded as 0—no product available while an official market launch was coded as 1—product available. 79 Another-though only structurally similar-measure of acquisition timing relates to the time before or after the passage of a technological standard (Warner, 2006).

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their first public appearance just shortly prior to market launch rendering an earlier acquisition virtually impossible. Target Age (TAge) captures a target’s maturity level relative to its establishment. It is calculated as the difference of date of foundation and date of acquisition80. It is an established measure of target maturity (Brueller et al., 2015; Chaudhuri et al., 2005). A clear advantage is the variable’s definition as a continuous variable measured in years making it robust to the issue of “not hitting the mark”. However, comparability of Target Age across a wide range of targets is difficult even if the study’s scope is limited to the AI industry. The developments of some technologies for specific applications may simply take longer than for others resulting in a potentially later acquisition in terms of Target Age. In addition, the actual date of incorporation is subject to some discretion of the founders. They may collaborate in a startup-like setting for a shorter or longer time without actually having founded a startup. Target Size (TSize) is just like Target Age a continuous measure of a target’s level of maturity and has been used for the same purpose in prior research (Brueller et al., 2015). Target Size corresponds to a target’s number of employees at the time of acquisition81. The variable benefits from virtually the same advantages and suffers from the same disadvantages as Target Age with respect to its operationalization of target maturity. The issue of comparability across targets may be exacerbated, though, because—in contrast to Target Age—Target Size has no universal reference point as its minimum value is defined for each target individually and essentially given by the size of its founding team82. An advantage of using Target Size jointly with Target Age to operationalize maturity is that the first may capture maturity changes rather well in situations where the second one fails. Quite generally, the reason is that a target’s number of employees likely varies differently with effects that delay a startup’s technology or product development and thus affect Target Age. While unexpected technological problems may influence a target’s age, the number of employees may remain completely unaltered. A small engineering team of constant size might just keep working until a problem is solved while the age

|| 80 The date of foundation is set to the first of January of the given year unless more specific information is available. The difference of the two date yields a number of days which is divided by 365 to produce a number in years with one digit after the decimal point. 81 Data on a target’s size at the time of acquisition was rarely available in M&A databases. Hence, other sources such as the target’s website as shown the time of acquisition, news coverage or information on LinkedIn had to be used. For few targets, linear extrapolation or interpolation based on several data points that by themselves were not close enough to the date of acquisition announcement was employed. 82 One could argue that a size of zero is a universal reference point. However, while a startup with an age of zero is feasible that same is not true for a startup with zero employees.

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clock keeps ticking for several months. Another advantage is a likely correlation between a target’s size and market traction as indicated by qualitative research. Unfortunately, all of the above measures of a target’s level of maturity are vulnerable to timing effects stemming from acquisition windows of opportunity (see subsection 4.2.3.1) that cannot be controlled for. Acquisitions may take place earlier or later than one of the mechanisms established in the qualitative study (see subsection 4.2.3.2) would suggest. However, there is no reason to believe that this effect differs systematically between performance- and functionality-focused acquisitions and thus introduces a systematic bias in regression analysis. In summary, three measures for a target’s level of maturity have been proposed. These are Target Age (TAge) in years, Target Size (TSize) in number of employees and Target Product Availability (TProd_Avail) as a dichotomous variable. All of them have distinct advantages and disadvantages that to some extent compensate each other. Thus, I expect the combination of these three variables to form a suitable operationalization of acquisition timing in terms of a target’s maturity level at the time of acquisition.

5.2.1.2 Deal value Deal value refers to the amount paid by an acquirer for a specific acquisition target in $ million. Hence, the variable Deal Value (DDeal_Val) is the natural measure. This research uses Deal Value per Target Employee (DDV_TS) as one additional variable. Both measures suffer from two drawbacks. First, the price paid for an acquisition target is the result of a negotiation. This means that unknown and therefore uncontrollable effects influence Deal Value and Deal Value per Target Employee. Second, as suggested by the qualitative analysis (see section 4.2.4), acquirers not only use detailed valuation procedures but also appear to fall back to crude heuristics such as flat rate pricing whose logic may not be clear. The derivation of hypotheses 2a and 2b (see section 3.4), however, is built upon the assumption of a rational evaluator that derives a target’s value based on the components of common value, private value and an uncertainty discount. Thus, negotiation and the use of crude valuation heuristics may increase the Deal Value distribution's variance. Generally, this should not be an issue, as there is no reason to believe that these effects correlate with performance- or functionality-focused acquisitions. However, if sample size is small other relevant relationships between variables may be clouded. Indeed, Deal Value is not available for the majority of acquisitions in the sample despite drawing data from six distinct sources (see table 3). The reason is that firms do not

Variables | 127

need to disclose deal values if they are immaterial from an accounting perspective (Cooney et al., 2009)83. Conditioning Deal Value on the number of a target's employees (DDV_TS) has been done by other researchers to either complement deal value as a measure (Brueller et al., 2015) or to operationalize another construct such as a target's quality (Puranam et al., 2009). A target's number of employees can be viewed as a rough proxy for a target's common value as assessed by an acquirer. Thus, the division of deal value by the number of a target's employees renders the resulting variable (DDV_TS) better comparable across acquisitions as differences of common value are taken into account. This is especially useful for hypotheses 2a and 2b in whose derivation deal value differences in performance- and functionality-focused acquisitions are attributed to varying uncertainty discounts.

5.2.2 Performance- and functionality-focus of acquisitions as the independent variable The independent variable of theoretical interest in this thesis is Deal Technology Type (DTech_Type and DTech_Type_Fun) that is built upon the distinction between performance- and functionality-focused acquisitions84. As described in section 5.1.2, Deal Technology Type is an ordinally scaled variable (DTech_Type) that assumes the values 1—clear performance-focus, 2—major performance- and minor functionality-focus, 3—minor performance- and major functionality-focus and 4— clear functionality-focus. Data for Deal Technology Type was collected based on coding of secondary sources. Refer to appendix A.2 for a detailed description of the coding approach. Other researchers such as Puranam et al. (2009) have successfully utilized an independent variable that stems from a coding procedure in their research. In the regression model, the variable Deal Technology Type appears in two forms—either in its original ordinal form with four categories (DTech_Type) or as a dichotomous variable that combines categories 1 and 2 as well as 3 and 4 (DTech_Type_Fun). The latter form has the benefit of an increased number of observations per category at the cost of losing some information.

|| 83 See https://www.sec.gov/interps/account/sab99.htm (accessed 01.02.2017) and https://www.quora.com/Why-would-an-acquisition-have-undisclosed-terms (accessed 01.02.2017) for additional information. 84 Note that interaction terms are formed with Deal Technology Type (see sections 5.4.1 and 5.4.2) and several control variables to test some hypotheses. These are obviously also of theoretical interest.

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5.2.3 Control variables related to acquirer, target, and deal Control variables relate to either acquirers, targets, or the deal. They were selected based on their potential influence on acquisition timing, deal value or more generally risk and uncertainty characteristics of performance- and functionality-focused acquisitions. Acquirer-related controls are Acquirer Age (AAge), Acquisition Experience (AExperience), and Acquirer Size (ASize). Acquirer Age (AAge) is measured in years and calculated as the date of acquisition less the acquirer's date of establishment85. Rising age promotes the creation of routines and inertia that might impact acquisition behavior (Karim and Mitchell, 2000; Shimizu and Hitt, 2005). Risk and uncertainty may be affected due to increased knowledge about competitors (Chakrabarti and Mitchell, 2013). Acquisition Experience (AExperience) refers to the number of acquisitions that an acquirer has conducted (Puranam et al., 2006; Hutzschenreuter et al., 2014; Ransbotham and Mitra, 2010; Warner, 2006; Makri et al., 2010) in the five years prior to the focal acquisition. Five years were chosen due to signs of diminishing value of experience reaching further back in time (Sampson, 2005; Sears and Hoetker, 2014). Acquisition Experience is likely to increase acquisition effectiveness (Kim et al., 2011). It may influence acquisition timing and deal value decisions due to learning (Ransbotham and Mitra, 2010; Hayward, 2002) and also affect integration risk and uncertainty (Puranam et al., 2009). Acquirer Size (ASize) corresponds to an acquirer's number of employees at the time of acquisition. Acquirer Size might influence target selection criteria (Puranam et al., 2009). Target-related controls cover the backgrounds of the target's founders, external funding, its legal status, pre-acquisition patenting, and the target's history. All of these are included because they may serve as signals of quality for an acquirer thereby reducing perceived risk and uncertainty. There are three variables concerning the founders’ backgrounds, namely Target Founder AI Background (TFounder_AIBG), Target Founder Scientist (TFounder_Scientist) and Target Founder Technology Background (TFounder_TechBG). All of them are collected for the founder in the most technical role. Concretely, this means that if the CEO and the CTO are among the founders then data pertains to the CTO. If there is only a CEO, then data is collected for her. LinkedIn and Bloomberg profiles provided the richest information for each variable. Target Founder AI Background (TFounder_AIBG) is a dummy variable indicating if the focal founder has prior experience in AI, through either university education or her former role at a different firm. Target Founder Scientist (TFounder_Scientist) is also a dummy

|| 85 As with Target Age, the date of foundation is set to the first of January of the given year unless more specific information is available. The difference of the two date yields a number of days, which is divided by 365 to produce a number in years with one digit after the decimal point.

Variables | 129

variable measuring whether the focal founder has conducted university-level research as either a Professor, PostDoc, or PhD-student prior to founding the acquisition target. Target Founder Technology Background (TFounder_TechBG) is a dummy variable signifying whether the focal founder has obtained a university degree in a technical subject such as engineering or a non-technical one such as management86. These three variables provide a good indication of how close an acquisition target operates to the forefront of technology. According to Fontana and Nesta (2009) this influences acquisition likelihood. Target Funding (TFunding) measure the amount of funding in USD million that a target has received from external investors prior to its acquisition. According to Graebner et al. (2010) especially venture capital funded firms are “particularly vibrant sources of technical innovation and new products”. Hence, Target Funding is a clear signal of value affecting perceived risk and uncertainty. Target Private Status (TPrivate) is a dummy variable reflecting whether a target is a private company (coded as 1) or not (coded as 0), i.e., a public company or a subsidiary. A target's legal status impacts an acquirer's target selection decisions and perceived risk and uncertainty through different levels of information asymmetry (Chakrabarti and Mitchell, 2013) and likely also deal value (Capron and Shen, 2007; Koeplin et al., 2000). Target Patenting (TPatent) corresponds to the amount of pending or already granted patents assigned to the target or one of its founders. Patent data was retrieved from the PATSTAT database. All patent offices were included and the patent kinds utility model and design patent were excluded. Patent families were used to avoid duplicates. Please refer to Nagel (2016) who conducted his master’s thesis on the topic of performance- and functionality-focused acquisitions as a driver of innovative success for additional details on the data collection procedure for this variable. Note that the variable TPatent may be slightly right-truncated as the available version of PATSTAT has a data cut-off roughly in July 2015 and a patent’s publication occurs only some time after its initial filing. Most likely, this issue only affects startups acquired in 2015. The issue of PATSTAT’s data cut-off could be circumvented to some degree by checking the number of patents for startups acquired in 2015 against the results of a Google patent search conducted in late 2016. In general, TPatent is a relevant control because patents are expected to represent a quality signal for acquirers reducing technology uncertainty (Warner, 2006). Target Spin-Off (TSpin_Off) is a dummy variable indicating whether the acquisition target is a former spin-off of a university, research institute or company. This may also be a quality signal for acquirers.

|| 86 This definition is similar to Patzelt et al. (2009) and Dimov and Shepherd (2005) with the exception that it focuses on the education of an individual instead of that of an entire group.

130 | Quantitative study—Performance and functionality in AI-related acquisitions

Controls related to the deal and the deal environment are Deal Domestic (DDomestic), Deal Full Acquisition (DFullAcquisition), and Deal Seller's Market (DSellersMarket). Deal Domestic (DDomestic) is a dummy variable signifying whether the acquirer and target are from the same country or not. Domestic acquisitions are associated with lower risk and uncertainty than their cross-border counterparts (Frey and Hussinger, 2006). Deal Full Acquisition (DFullAcquisition) is a dummy variable accounting for whether the full target, i.e., all of its assets or only some of them, were acquired. Naturally, this influences deal value. The variable Deal Seller's Market (DSellersMarket) reflects the bargaining power that sellers— the shareholders of acquisition targets—have in the market for corporate control. This variable is proxied via the number of acquisitions in the data set in a given year. According to Granstrand (1990) a seller's market leads to “higher prices, and under-developed firms with unfinished technology being acquired” if combined with competition among potential acquirers. Indeed, there is evidence signifying an intensifying seller's market in AI87. Note that no financial variables except Deal Value and Target Funding are included. This is because a considerable share of acquirers and targets are private firms or subsidiaries so that financial data is not readily available (Badertscher et al., 2013). There is evidence that financial variables might not be important in relation to acquisition timing and deal value. In their study of target age and acquisition value, Ransbotham and Mitra (2010) find that “none of the financial variables in the model are significant in the regression results”. Hence, exclusion of financial variables in this study—though done involuntarily—might not be detrimental to the results. Toeholds—an indication if the acquirer has invested in the target prior to its acquisition (Hutzschenreuter et al., 2014; Bulow et al., 1999)—may be relevant as they influence acquisition risk and uncertainty (see section 4.2.2) and deal value (Betton et al., 2009). However, only four cases of toeholds could be identified. Controlling for these cases did not change regression results (see section 5.4) so that no separate control variable was introduced88. Also note that some variables change roles depending on the hypothesis tested. For example, Target Age is a dependent variable when studying acquisition timing but becomes a control when analyzing Deal Value as the dependent variable.

|| 87 There is an article posing the question if Google is “[…] Cornering the Market on Deep Learning” (https://www.technologyreview.com/s/524026/is-google-cornering-the-market-on-deep-learning/ accessed 12.01.2017). According to the dataset used in the present thesis, Google is the most active acquirer in AI in the timeframe 2005—2015. 88 All acquisitions with toeholds (Indisys, Olaworks, Omek, Saffron) were performed by Intel after an initial investment by Intel Capital. Some of these investments by Intel Capital may have been strategic as Intel Capital specifically mentions “gap fillers” as an investment category (Intel Capital, 2016).

Descriptive results | 131

Also note that all continuous control variables are transformed before being used in hypothesis testing. Transformations are achieved either by taking the natural logarithm or the inverse hyperbolic sine for TPatent and AExperience as these variable can assume zero as a value. Section 5.3.3 elaborates on this.

5.3 Descriptive results The objective of this section is to gain a deep understanding of the dataset and the variables therein as a preparation for the hypothesis tests in section 5.4 and the discussion in section 5.5. First, I will characterize the dataset and the variables based on descriptive statistics (section 5.3.1). Then, an analysis of the correlations among variables follows in section 5.3.2. In section 5.3.3, I introduce several transformations of variables based on uni-variate and bi-variate analyses of variables.

5.3.1 Characterization of dataset and variables The characterization of the variables in the dataset is mainly based on the overview of descriptive statistics in table 4 and table 5. As noted previously the main sample encompasses 215 observations. Due to missing data on Deal Value, a smaller subsample consisting of 83 observations had to be introduced. First, I will comment on the main sample focusing on selected key variables and the comparison of descriptive statistics to related studies, where possible. Second, I will comment on the descriptive statistics of the dependent variables in the main sample (N=215)89 and the sub-sample (N=83) partitioned by Deal Technology Type. Last, I will explain some hidden, yet relevant properties of the dataset. The dependent variable TAge shows a large range from 0.1 years to 30.2 years with a mean target age at acquisition of 6.5 years. The number of a target's employees ranges from 2 to 200 with a mean of 32 employees. These values for Target Age and Target Size are similar to those of prior research on technology-focused acquisitions (Ahuja and Katila, 2001; Puranam et al., 2009; Granstrand and Sjölander, 1990; Cefis and Marsili, 2012; Chaudhuri, 2005). Product availability (TProd_Avail) is given for 83% of all acquisitions in the sample. This is slightly higher than the value in Chaudhuri's (2005) sample, who find that 59% of targets were acquired post product launch. Deal value covers a range between $0.66 million and $390.00 million with a mean value of $61.92 million. Deal value per target employee (DDV_TS)

|| 89 Note that the main sample contains two acquisitions of technology directly from university. These were treated in the same way as other acquisitions. Removing these datapoints only leads to minor changes in regression results (section 5.4).

132 | Quantitative study—Performance and functionality in AI-related acquisitions

has a minimum value of $0.01 million and a maximum of $10.53 million with a mean of $1.89 million. This is in line with Puranam et al.'s (2009) study. Maximum target size of 200 employees and maximum deal value of $390.00 million are by construction below the limits for technology-focused acquisitions provided by literature (Puranam et al., 2009; Makri et al., 2010). Overall, these results indicate that the key dependent variables' characteristics are similar to those in related extant research. This speaks not only for the appropriateness of the sampling and data collection approach but also for the generalizability of the research. Tab. 4: Descriptive statistics

Type

Variable in model

DV1

TAge

215

6.46

DV

1

N Mean

Std. Dev.

Min.

5.06

0.1

Max. Measurement 30.2 Years

TProd_Avail

215

0.83

0.37

0

1 Dummy

DV1

TSize

215 32.18

35.69

2

200 Employees

DV1

DDeal_Val

83 61.92

74.33

0.66

DV1

DDV_TS

83

1.89

2.05

0.01

IV2

DTech_Type

215

3.00

1.20

1

IV2

DTech_Type_Fun

215

0.67

0.47

0

Control AAge

215 23.15

28.10

0.8

Control AExperience

215 12.52

18.97

Control ASize

215 35.92

152.11

Control DDomestic

215

0.67

0.47

0

1 Dummy

Control DFull

215

0.92

0.27

0

1 Dummy

Control DSellersMarket

215 40.24

19.24

3

Control TFounder_AIBG

215

0.78

0.42

0

1 Dummy

Control TFounder_Scientist

215

0.46

0.50

0

1 Dummy

Control TFounder_TechBG

215

0.92

0.27

0

1 Dummy

83 12.39

Control TFunding

0

390 $ million 10.53 $ million/employee 4 Ordinal (1—4) 1 Dummy (1—F; 0—P) 249.2 Years 98 Number in last 5 years

0.01 2100.00 1000 employees

65 # acquisitions in year

17.52

0.00

Control TPatent

215

3.70

9.26

0

88.83 $ million

Control TPrivate

215

0.92

0.27

0

1 Dummy

Control TSpin_Off

215

0.14

0.35

0

1 Dummy

64 Count

1 DV—Dependent variable 2 IV—Independent variable

Some of the controls show rather extreme values. The maximum acquirer age of 249 years stems from the acquisition of Collectrium by Christie's which was founded in 1766. The large maximum value for the acquirer's number of employees is due to Walmart acquiring Kosmix.

Descriptive results | 133

Tab. 5: Descriptive statistics of dependent variables partitioned by Deal Technology Type

Independent Variable DTech_Type Overall

Dependent Variable

N

TAge (Years) Mean Std. Dev.

(3)

(4) (0 = 1&2)

(1 = 3&4)

215

42

29

31

113

71

144

6.46

3.84

5.69

6.76

7.55

4.60

7.38

2.89

3.55

4.40

5.79

3.29

5.51

0.1

0.1

1.0

1.1

1.4

0.1

1.1

Max.

30.2

11.8

14.6

20.5

30.2

14.6

30.2

0.83

0.67

0.83

0.90

0.88

0.73

0.88

0.37

0.48

0.38

0.30

0.33

0.45

0.32

0

0

0

0

0

0

0

Max.

1

1

1

1

1

1

1

Mean

32.18

16.63

23.68

30.28

40.66

19.51

38.43

Std. Dev.

35.69

16.84

23.37

34.87

41.05

19.92

39.92

Min.

2

2

2

3

3

2

3

Max.

200

75

100

175

200

100

200

83

19

8

14

42

27

56

Mean

61.92

49.90

60.13

60.91

68.03

52.93

66.25

Std. Dev.

74.33

55.14

44.70

68.34

88.24

51.63

83.17

N

DDV_TS ($ million/ employee)

(2)

5.06

Min.

DDeal_Val ($ million)

(1)

Min.

TProd_Avail Mean (Dummy) Std. Dev.

TSize (Employees)

DTech_Type_Fun

Min.

0.66

1.00

1.20

5.70

0.66

1.00

0.66

Max.

390.00

190.00

150.00

200.00

390.00

190.00

390.00

Mean

1.89

2.16

2.02

2.12

1.66

2.12

1.78

Std. Dev.

2.05

1.58

1.59

2.77

2.08

1.55

2.25

Min.

0.01

0.10

0.17

0.18

0.01

0.10

0.01

Max.

10.53

5.43

5.23

10.53

10.00

5.43

10.53

Table 5 provides an overview of descriptive statistics of the dependent variables partitioned by Deal Technology Type as an ordinal variable (DTech_Type) or as a dichotomous variable (DTech_Type_Fun). This view is especially helpful to gain deeper insights into the structure of the dataset with deal technology type in terms of a performance- or functionality-focus as a categorization. The dependent variables TAge, TProd_Avail and TSize are provided for the main sample (N=215) while DDeal_Val and DDV_TS are given for the smaller sub-sample (N=83) only due to missing data. In the main sample, deal technology type 4 accounts for roughly half of all observations while the rest is about equally distributed among categories 1—3.

134 | Quantitative study—Performance and functionality in AI-related acquisitions

This should not be an issue as the number of observations within each category is sufficiently large. For each of the three dependent variables, the mean values partitioned by deal technology type indicate a consistent trend from lower target maturity to higher target maturity proceeding from clear performance-focus to clear functionality-focus. The same trend is apparent if Deal Technology Type is coded as a dichotomous variable. In the smaller sub-sample with 83 observations the share observations in each of the four categories of deal technology type is roughly the same as in the main sample (deviation smaller than 5%). This indicates that there was likely no systematic effect reducing data availability for a single category only. However, the total number of observations in each category is low—especially for categories 2 and 3. Hence, interpreting analyses of DDeal_Val and DDV_TS with four categories for Deal Technology Type may not be feasible as random effects may strongly influence results. This should not be an issue, however, if Deal Technology Type is condensed to two categories. Neither the mean value of DDeal_Val nor of DDV_TS exhibits any clear behavioral trend progressing from performance to functionality-focused acquisitions. AI applications and technology among targets Number of acquisitions (N = 215) 59 59

Top 10 AI-acquirers (2005–2015) Number of acquisitions 13 10

42

3

3

3

2

SLAM Pattern recognition (non ML1-based)

9

ML1-platform Simulation/ Game AI

16

Speech recognition Facial recognition (ML1)

Other (ML1) Image recognition (ML1)

Natural language processing

Semantic search

19

4

Twitter

4

Salesforce

4

Nuance

5

Intel

5

Amazon

6

Yahoo!

7

Microsoft Facebook & Oculus VR

Apple

Google

8

1 ML – machine learning Note. All applications may employ machine learning at their core. It is explicitly noted if this is the case for all or none of the applications.

Fig. 14: Structure of dataset—top acquirers (left) and distribution of applications and technologies (right)

In total, the 215 acquisitions in the sample were performed by 145 acquirers. The 23 acquirers that have conducted more than one of the acquisitions account for a little less than half (93) of all acquisitions. The most prolific acquirer of AI firms was

Descriptive results | 135

Google closely followed by Apple. See the left panel of figure 14 for an overview of the top 10 acquirers with 4 or more acquisitions between 2005 and 2015. Roughly two thirds of the acquisitions in the sample are domestic and in more than half of the acquisitions both target and acquirer are based in the US. An interesting perspective on the sample is given by the distribution of AI applications and technologies among acquisition targets. See the right panel of figure 14 for an overview. Natural language processing and image recognition are by far the most common applications of AI technology among the acquired firms. Typically, machine learning powers these applications. The category “Other” includes applications such as advertising, cybersecurity and recommendation engines that are often powered by machine learning, as well.

5.3.2 Correlations Pairwise correlations among variables provide first insights into the relationships that are further explored via multivariate methods. The full set of pairwise correlations is displayed in table 6 and for transformed variables90 in table 13 in the appendix. In the following, I will shortly comment on select significant (p