Innovation trends in the space industry. Volume 25 9781786304919, 1786304910, 9781119694816, 1119694817, 9781119694847, 1119694841

Table of contents :
Cover......Page 1
Half-Title Page......Page 3
Title Page......Page 5
Copyright Page......Page 6
Contents......Page 7
Preface......Page 11
Introduction......Page 15
The evolution of the space industry in light of economic considerations......Page 17
Innovation strategies of space firms......Page 19
Strategic diagnosis of new technologies......Page 21
Structure of the book......Page 23
1. Theoretical and Empirical Framework......Page 25
1.1. Innovation management: introductory elements......Page 26
1.1.1. Diversity and legitimacy of innovation......Page 27
1.1.2. Typology of innovations......Page 29
1.1.3. Developing product innovations......Page 33
1.1.4. The industry cycle......Page 39
1.2. The space industry......Page 46
1.2.1. Why study the space industry?......Page 47
1.2.2. Sources and level of analysis......Page 48
1.2.3. The boundaries of the space industry......Page 49
1.2.4. Structure of the space industry......Page 52
2. The Emergence of Industry: The Influence of Demand......Page 67
2.1.1. Emergence as an object of study......Page 68
2.1.2. Characterizing emergence......Page 70
2.1.3. Method: sources and measurements......Page 73
2.1.4. Results......Page 77
2.1.5. Discussion......Page 80
2.2.1. Theoretical framework......Page 81
2.2.3. Results: influence of customers on the emergence of the space industry......Page 84
2.2.4. Discussion and implications......Page 89
2.3.1. Sources, data and indicators......Page 94
2.3.2. Loss of impetus resulting in technical change......Page 96
2.3.3. Influence of demand on technological change......Page 102
2.3.4. Discussion and conclusion......Page 108
3. Slow Adoption of Innovations: A Key Success Factor......Page 111
3.1.1. Introduction......Page 112
3.1.2. Inertia: a literature review......Page 114
3.1.3. Modeling a strategy of technological inertia based on reliability......Page 118
3.1.4. Research methodology......Page 121
3.1.5. Results......Page 129
3.1.6. Discussion and conclusion......Page 130
3.2.1. Introduction......Page 133
3.2.2. Organizational change: a literature review......Page 135
3.2.3. Modeling the organizational inertia strategy......Page 140
3.2.4. Methodology......Page 141
3.2.5. Results......Page 142
3.2.6. Discussion and conclusion......Page 150
4. Technological Discontinuities andStrategic Diagnosis......Page 155
4.1.1. Introduction......Page 156
4.1.2. The theory of disruptive innovations......Page 158
4.1.3. Model......Page 163
4.1.4. Methodology......Page 168
4.1.5. Results......Page 169
4.1.6. Discussion......Page 180
4.1.7. Conclusion......Page 182
Conclusion......Page 185
References......Page 193
Index......Page 215
Other titles from iSTE in Innovation, Entrepreneurship and Management......Page 221
EULA......Page 233

Citation preview

Innovation Trends in the Space Industry

Smart Innovation Set coordinated by Dimitri Uzunidis

Volume 25

Innovation Trends in the Space Industry

Victor Dos Santos Paulino

First published 2020 in Great Britain and the United States by ISTE Ltd and John Wiley & Sons, Inc.

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address: ISTE Ltd 27-37 St George’s Road London SW19 4EU UK

John Wiley & Sons, Inc. 111 River Street Hoboken, NJ 07030 USA

www.iste.co.uk

www.wiley.com

© ISTE Ltd 2020 The rights of Victor Dos Santos Paulino to be identified as the author of this work have been asserted by him in accordance with the Copyright, Designs and Patents Act 1988. Library of Congress Control Number: 2019950868 British Library Cataloguing-in-Publication Data A CIP record for this book is available from the British Library ISBN 978-1-78630-491-9

Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

ix

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

xiii

Chapter 1. Theoretical and Empirical Framework . . . .

1

1.1. Innovation management: introductory elements . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.1. Diversity and legitimacy of innovation 1.1.2. Typology of innovations. . . . . . . . . . . 1.1.3. Developing product innovations . . . . . 1.1.4. The industry cycle . . . . . . . . . . . . . . 1.2. The space industry . . . . . . . . . . . . . . . . 1.2.1. Why study the space industry? . . . . 1.2.2. Sources and level of analysis . . . . . . . 1.2.3. The boundaries of the space industry . 1.2.4. Structure of the space industry . . . . .

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2 3 5 9 15 22 23 24 25 28

Chapter 2. The Emergence of Industry: The Influence of Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

43

2.1. The space industry is in the emerging phase 2.1.1. Emergence as an object of study. . . . . . . 2.1.2. Characterizing emergence . . . . . . . . . . . 2.1.3. Method: sources and measurements . . . . 2.1.4. Results . . . . . . . . . . . . . . . . . . . . . . . . 2.1.5. Discussion . . . . . . . . . . . . . . . . . . . . . .

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44 44 46 49 53 56

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2.2. Customers shape the industry dynamics in the emergence phase . . . . . . . . . . . . . . . . . . . 2.2.1. Theoretical framework . . . . . . . . . . . . 2.2.2. Sources . . . . . . . . . . . . . . . . . . . . . . . 2.2.3. Results: influence of customers on the emergence of the space industry . . . . . . . . . . 2.2.4. Discussion and implications . . . . . . . . 2.3. Demand influences technological change . . 2.3.1. Sources, data and indicators . . . . . . . . 2.3.2. Loss of impetus resulting in technical change . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.3. Influence of demand on technological change . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.4. Discussion and conclusion . . . . . . . . . .

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57 57 60

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60 65 70 70

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78 84

Chapter 3. Slow Adoption of Innovations: A Key Success Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . .

87

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3.1. Slow adoption of technological innovations: a key success factor . . . . . . . . . . . . . . . . . . . . . . . 3.1.1. Introduction . . . . . . . . . . . . . . . . . . . . . . 3.1.2. Inertia: a literature review . . . . . . . . . . . . 3.1.3. Modeling a strategy of technological inertia based on reliability . . . . . . . . . . . . . . . . . . . . . . 3.1.4. Research methodology . . . . . . . . . . . . . . . 3.1.5. Results . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.6. Discussion and conclusion . . . . . . . . . . . . . 3.2. Slow adoption of organizational innovations: a key success factor . . . . . . . . . . . . . . . . . . . . . . . 3.2.1. Introduction . . . . . . . . . . . . . . . . . . . . . . 3.2.2. Organizational change: a literature review . 3.2.3. Modeling the organizational inertia strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.4. Methodology . . . . . . . . . . . . . . . . . . . . . . 3.2.5. Results . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.6. Discussion and conclusion . . . . . . . . . . . . .

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88 88 90

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94 97 105 106

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109 109 111

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116 117 118 126

Chapter 4. Technological Discontinuities and Strategic Diagnosis . . . . . . . . . . . . . . . . . . . . . . . . .

131

4.1. Disruptive innovations and threat analysis . . . . 4.1.1. Introduction . . . . . . . . . . . . . . . . . . . . . . .

132 132

Contents

4.1.2. The theory of disruptive innovations 4.1.3. Model . . . . . . . . . . . . . . . . . . . . . . 4.1.4. Methodology . . . . . . . . . . . . . . . . . 4.1.5. Results . . . . . . . . . . . . . . . . . . . . . 4.1.6. Discussion . . . . . . . . . . . . . . . . . . . 4.1.7. Conclusion . . . . . . . . . . . . . . . . . . .

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134 139 144 145 156 158

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191

 

Preface

Economic considerations have long remained secondary in space activities. For many years, satellites, space probes, and launchers have been purchased mainly by military customers and space agencies. For these customers, military, political prestige, and scientific considerations predominate. Taking advantage of space technologies to sell a telephone or meteorological service was a marginal objective within space activities. The end of the Cold War profoundly changed the space industry. It largely contributed to establishing the commercialization of space as a fundamental trend. Although there have been fluctuations since 1990, the weight of economic considerations has continued to increase. Since 2010, it has crossed such a threshold that represents a major challenge for all stakeholders in the industry. For example, developing space technologies by trying first and foremost to take advantage of technological and scientific opportunities becomes dangerous. More emphasis must now be placed on identifying market opportunities. Faced with this challenge, very little work has placed economic considerations and the transformation of the space industry at the center of their analysis. Space industry stakeholders may, therefore, have difficulty in legitimizing

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the new weight of economic considerations. The main objective of this book is to fill this gap by proposing a study of the space industry based on innovation management. We have identified three main issues: — The space industry was born out of the desire of the Russian and American military to gain an advantage at the beginning of the Cold War. How can we describe the evolution of this industry shaped by customers? — The space industry produces high-tech products that paradoxically carry few recent electronic components and are developed through processes largely inspired by what was done in the 1950s. How should the conservatism of this hightech industry be interpreted? — Small satellites feed the current phase of accelerating the commercialization of space and could be a substitute for traditional satellites built by existing firms. In this context, how can we help existing firms determine whether this technological promise is a threat to be taken seriously or rather a fad? This book is the result of research conducted since 2003 in the space industry. Since 2013, we have structured our research around the SIRIUS Chair (www.chaire-sirius.eu). The objective of this chair is to conduct research in management sciences and law applied to the space sector. The SIRIUS Chair also focuses on the dissemination of research and training. We supervise, for the Toulouse Business School, the activities in management science conducted in the SIRIUS Chair.

Preface

xi

We would like to thank Dimitri Uzunidis, Honorary President of the Réseau de Recherche sur l’Innovation (https://rrifr.univ-littoral.fr), for giving us the opportunity to publish this book.

Victor DOS SANTOS PAULINO October 2019

 

Introduction

In 1957, the successful launch of the first artificial satellite, Sputnik 1, led humanity into a new historical period: the space age (McDougall 1982). This new era has given rise to many achievements that have largely gone beyond the scope of space activities. Examples include manned space flights, the Moon landing, and telecommunications satellites. These outcomes are the result of intense innovation efforts guided by the desire to control the space environment, which is poorly known and very different from the terrestrial environment. Space activities have also always been illustrations of what high technology, progress, or a possible future for humanity could be. One thinks in particular of the many science fiction works that are set in the context of life in space and on other planets. In addition to the technological and cultural dimensions, space activities also have a strong military and political significance. The space industry was born out of the desire of the Russian and American military to gain an advantage at the beginning of the Cold War. For a long time, economic considerations were reduced in the space industry. In institutional markets, competition, prices, and profits were largely dominated by military and political considerations embodied in binding regulations. Examples include the protectionism of defense ministries

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and space agencies that exclude foreign suppliers from the market. At the beginning of the space conquest, the commercialization of space was not a priority for industry stakeholders. The purpose of putting satellites into orbit was not to sell commercial telecommunications and Earth observation services. However, this situation changed with the end of the Cold War. The commercial market, characterized by international competition and customers seeking to maximize their profits, has grown in size. Conversely, the size of the military and civilian institutional markets has shrunk. The commercialization of space has taken root as a fundamental trend, even if it may have experienced fluctuations. One example is the inflation and then bursting of the “Internet bubble” that impacted the commercial satellite communications market in the second half of the 1990s. We can also mention the acceleration of the commercialization of space that we are currently observing, dubbed “New Space” by industry stakeholders. With New Space, the commercialization of space has reached a level that gives economic considerations an importance never before achieved. This is a major challenge for industry stakeholders, who must partly redefine space activities in light of these issues. For example, taking into account technological, military, political, and regulatory considerations is increasingly seen as insufficient to ensure the survival of industry stakeholders. Similarly, conducting innovation efforts guided solely by the desire to control the space environment seems dangerous. This effort must now also include the commercialization of innovations. Faced with this new need to take better account of economic considerations, it can be seen that insufficient academic work is available. Some work in history, political science, and law can enlighten industry stakeholders. However, this work is not intended to place economic considerations at the center of their analysis. On the side of economics and management sciences, very little work deals with the space industry and even less with its transformation. The main

Introduction

xv

objective of this book is to fill this gap by proposing a study of the space industry based on innovation management. In our opinion, three interconnected innovation trajectories must be studied as a priority to understand the commercialization of space. First, it is necessary to describe the long-term evolution of the space industry in light of economic considerations. It then seems essential to us to characterize the innovation strategies of space firms (e.g. Airbus Defence and Space, Boeing, Thales Alenia Space). Finally, we believe it is important to help existing firms to better diagnose the current acceleration of the commercialization of space. The evolution of the space industry in light of economic considerations The space industry was born out of the desire of the Russian and American military to gain an advantage at the beginning of the Cold War. How can this type of evolution be described? What role do customers play in this evolution and innovation? Are there any characteristic phases in the dynamics of the space industry? To answer these questions, we propose to use the industry lifecycle theory (Anderson and Tushman 1990; Gustafsson et al. 2016; Klepper 1997, 2010; Klepper and Graddy 1990; Utterback and Abernathy 1975). This theory is one of the most important for understanding innovation (Dodgson et al. 2008). It breaks down the development of the industry into distinct phases: birth/emergence, growth/takeoff, maturity, and decline. In addition, it identifies several lifecycles such as product, innovation, and adoption. This makes it possible to study the dynamics of an industry according to essential dimensions such as sales and innovation rates. Lifecycle theory will be at the heart of this book, but it has some limitations. First, the influence of demand on the evolution

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of the industry is poorly understood since attention is generally focused on the role of producers (Di Stefano et al. 2012; Forbes and Kirsch 2011; Saviotti and Pyka 2013). This poses a challenge to understand the evolution of industries in which institutional customers, such as Ministries of Defence, play a major role, as is the case in space (Malik 2017; Mowery and Rosenberg 1989; Scranton 2006; Spencer et al. 2005). Another limitation is that the birth of an industry remains a relatively unstudied phenomenon. (Aldrich and Fiol 1994; Forbes and Kirsch 2011; Nygaard 2008; Zhen and Démil 2015). This seems to us to be problematic because the characteristics of the emergence phase seem to have lasted for many years in the space industry. Examples include low competition, niche markets, and intense innovation efforts. Studying the long-term evolution of the space industry in light of economic considerations leads us to show that customers have a strong influence on the emergence of the space industry and innovation. By customers, we mean Ministries of Defence, space agencies (e.g. NASA, CNES, ISRO), and commercial satellite operators (e.g. SES, Intelsat, Arabsat, Türksat). First, we show that the space industry is characterized by a long emergence phase that lasts more than 50 years (1). We then show that customers are shaping the industry by helping to create four distinct periods between 1957 and 2011 (2). The influence of customers is observed through the pursuit of five objectives. Military customers have created the industry by pursuing three objectives: military, national prestige, and science and technology. This has led to iconic space programs such as Sputnik and “GPS.” Space agencies (e.g. NASA, CNES, and ISRO) shape the industry by pursuing certain objectives similar to military customers such as those of national prestige and science and technology. However, they also pursue their own objective, that of economic benefits. Their iconic programs have been, for example, the Apollo and the International Space Station programs. Finally, commercial

Introduction

xvii

satellite operators have a single objective of their own: the pursuit of profits. By focusing on the weight of the science and technology objective among customers between 1957 and 2011, we observe a slowdown in innovation in the industry (3). Over time, the space industry has become kind of a “sleeping beauty” with underexploited technological and commercial potential. These results contribute to an understanding of the longterm evolution of the space industry in light of economic considerations such as the role of customers, innovation efforts, and industry lifecycle theory. These results also extend the scope of the lifecycle theory to the space industry. By emphasizing the central role of customers, we reinterpret the role of supply and demand during the emergence phase. We also observe that the pursuit of profits remains a secondary objective for customers during the emergence phase. Innovation strategies of space firms The slowdown in innovation observed in the space industry is leading to the emergence of a paradox. Satellites are high-tech products that carry few recent electronic components and are developed through processes largely inspired by what was done in the 1950s. How should the conservatism of this high-tech industry be interpreted? Are the innovation strategies of satellite producers irrational and do they jeopardize their survival? On the contrary, do space activities require a certain inertia? We have chosen to refer to evolutionary work to answer these questions. The interpretation of delays in the adoption of technological innovations is a matter of debate. The dominant view is that adoption delays are dangerous for the survival of organizations. Nevertheless, some research on industries close to the space industry has shown that too

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rapid adoption of new technologies can jeopardize the survival of organizations (Anderson and Tushman 1990; Musso 2009). One example is Rosenberg’s (1976) pioneering study on the aviation industry. Within the framework of this evolutionary work, there is also a debate about whether organizational change promotes organizational survival or mortality (Hannan and Freeman 1984; Nelson and Winter 1982). In this book, we consider that these different perspectives about the effects of innovation on organizations’ survival must be seen as complementary (Carroll and Teo 1996). The study of innovation strategies of space firms leads us to highlight the existence of a positive link between slow adoption of innovations, product reliability and organizational survival. On the one hand, we show that the slow adoption of technological innovations is rational behavior when it allows reliability to be maintained. This strategy is mainly explained by risk aversion. On the other hand, it appears that the slow adoption of organizational innovations promotes the survival of organizations when the environment is risky. This strategy aims to maintain the high levels of reliability achieved during successful space missions by achieving rigorous organizational replication (i.e. replication of processes, rules, and methods). There are several similarities between these two strategies, which we call inertia strategies. First, the inertia strategy is not immobility but a prudent adoption of innovations. Then, these strategies are observed in a risky context as is the case in space activities. Finally, these strategies generally lead to delays in the adoption of innovations, even in the case of a high-tech industry. These results explain why the slow adoption of innovations is a key success factor in the space industry. In a risky environment, this ensures the high levels of reliability required. We consider inertia strategies to be a rational response of space organizations. It is, therefore, necessary to

Introduction

xix

reinterpret the notion of inertia within innovation management. As Rosenberg (1976) suggested, adopting innovations carefully can be more effective for the survival of organizations than adopting them too quickly. As a result, some technological and organizational delays reflect the control of a key success factor and not a weakness that must be reduced by accelerating innovation. By studying the advantages of prudent adoption, we can interpret the conservatism displayed by a high-tech industry. However, our work ignores the risks of overly rigorous inertia strategies that would lead to underestimating high-potential innovations. The current acceleration of the commercialization of space forces existing firms to question whether they are facing innovations of this type. Strategic diagnosis of new technologies Artificial intelligence, big data, 3D printing, Internet of things, and miniaturization of satellites: these technologies feed into the last phase of the acceleration of the commercialization of space and could also be substitutes for some existing space technologies. In this context, existing firms are seeking to determine whether these technological promises are threats to be taken seriously or are rather a fad, which often exists with new technologies. To help existing space firms make this diagnosis, we turn to the theory of disruptive innovations. This theory, introduced by Christensen (1997), is currently one of the most widely used to understand technological discontinuities. It indicates that when it is launched, disruptive innovation is not a threat because it is not purchased by the customers of existing firms. Existing customers do not value the new performance criteria introduced by disruptive innovation. Only a small group of

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customers with a limited budget value these new criteria. In addition to not posing a threat to existing firms, disruptive innovation also does not present an opportunity because it involves low margins and volumes. Therefore, disruptive innovation is only proposed by new entrants. In the medium term, disruptive innovation becomes a major threat. On the one hand, the performance of the disruptive innovation increases, which encourages the customers of the existing firm to buy it. On the other hand, due to the existence of a first mover advantage, existing firms are unable to catch up with new entrants who will gradually dominate the market. To avoid the fate of companies like Kodak and Nokia, when a technological discontinuity is introduced, existing firms must determine whether it will become a disruptive innovation. Faced with this imperative, the theory of disruptive innovations has two weaknesses. The first is a confusing definition of the concept of disruptive innovation. All discontinuous innovations tend to be too quickly assimilated to disruptive innovations (Schmidt and Druehl 2008). This theory also presents a low predictive value for existing firms. The latter must act when the new technology is introduced; however, at that time, there are no criteria for making a decision. Indeed, it is difficult to identify among the technological promises those that will fail and those that will become disruptive innovations (Danneels 2004; Tellis 2006). To address these weaknesses, we explore the concept of potential disruptive innovation suggested by Danneels (2004). We propose a precise definition by identifying the short-term characteristics of disruptive innovations. By studying the case of satellite miniaturization in light of this definition, we show that small satellites are imperfect substitutes for traditional satellites. This new technology, which is part of the current acceleration of the commercialization of space, represents a low threat to existing firms.

Introduction

xxi

By precisely defining potential disruptive innovations, we help reduce the confusion surrounding the concept of disruptive innovation. We also propose an analysis grid to improve the diagnosis of the threat of potential substitutes. Structure of the book In Chapter 1, we introduce the theoretical and empirical framework of this book. We first present the definitions and theories used in innovation management. We then describe the space industry. In the following chapters, we successively study the three innovation trajectories mentioned above: the evolution of the space industry in light of economic considerations (Chapter 2), the innovation strategies of space firms (Chapter 3), and the strategic diagnosis of new technologies (Chapter 4).

 

1 Theoretical and Empirical Framework

In this chapter, we introduce the theoretical and empirical framework used to study the innovation trajectories in the space industry. In the first section, we present our approach to innovation management. As a relatively new activity and discipline, we first discuss its sources of legitimacy. We highlight its influence on the performance of firms and its ability to understand the central phenomenon of creative destruction (Dodgson et al. 2008; Tidd and Bessant 2009; Trott 2012). In a second step, we present our typology of innovations, which is based on three dimensions: the type, level of novelty, and cumulative nature of innovation (Ehrnberg 1995; Nelson 1995; Utterback and Abernathy 1975). We then discuss innovation as a process. We detail the different “stage-gate” processes that structure the development of innovations (Dodgson et al. 2008). Finally, we adopt a meso approach and present the lifecycle of the industry. This theory is one of the most important for understanding innovation (Dodgson et al. 2008). It allows a dynamic and simultaneous understanding of several essential dimensions of innovation, such as sales,

Innovation Trends in the Space Industry, First Edition. Victor Dos Santos Paulino. © ISTE Ltd 2020. Published by ISTE Ltd and John Wiley & Sons, Inc.

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Innovation Trends in the Space Industry

innovation rate, and client entry. We review the main characteristics of each phase of the cycle. In the section which follows, we introduce the space industry, which is the empirical framework for our study. We chose to study this industry because there is a lack of work in economics and management sciences applied to this industry. In addition, compared to other popular innovative industries, such as semiconductors and biotechnology, we believe that the space industry is a counterexample of an innovative industry (Dos Santos Paulino 2007; Dos Santos Paulino and Le Hir 2016). We then show that the space industry has blurred boundaries and is part of a larger whole, which is the space economy (Pérez et al. 2017). This economy includes downstream activities that use space technologies but are part of other industries (e.g. telecommunications, transportation). In the following section, we detail the structure of the space industry: products, customers, producers, and market rules (Barbaroux and Dos Santos Paulino 2013; Dos Santos Paulino and Barbaroux 2016). In a nutshell, the space industry produces spacecraft (e.g. satellites, probes), launchers, and ground equipment that are purchased by institutional (military and civil) and commercial customers (e.g. US Department of Defense, NASA, Intelsat). The products of the industry are generally consumed in an uncertain space environment. Producers are generally large organizations (e.g. Boeing, Airbus DS) that are impacted by market rules that deviate from those observed in competitive situations (e.g. national preference, export control). 1.1. Innovation management: introductory elements In this section, we introduce the definitions and theories in innovation management referred to in this book.

Theoretical and Empirical Framework

3

For firms, innovation is becoming an increasingly important part of their performance. As a discipline and field of research, innovation management legitimacy stems, in particular, from its ability to understand the phenomenon of creative destruction. The term innovation is ambiguous because it can be defined from two quite different angles. The term innovation refers to a process that describes the phases necessary, for example, to design new products. The term innovation is also used to describe the outcome of this process as the new product itself (Tidd and Bessant 2009). We will discuss these two dimensions of innovation in the following sections. We first characterize innovation as the result of a process through three dimensions: type (e.g. product, process), level of novelty, and cumulativeness (Ehrnberg 1995; Murmann and Frenken 2006; Nelson 1995; Utterback and Abernathy 1975). We then describe innovation as a process. We detail the two main strategies that structure this process (i.e. technology push and market pull). Then, we present the main evolutions and characteristics of the “stage-gate” processes implemented to develop innovations (Dodgson et al. 2008). In the last part, we adopt a meso perspective by looking at the lifecycle of the industry. This theory is one of the most important to understand innovation and consists of several lifecycles. We will detail the product, innovation, and adoption lifecycle (Libai et al. 2008; Moore 2002; Rogers 2003; Utterback and Abernathy 1975). 1.1.1. Diversity and legitimacy of innovation Innovation as a business activity, a taught discipline, and a field of research is a very vast field that relates to a variety of issues. Without being exhaustive, we can mention, for example, the innovation development processes implemented

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Innovation Trends in the Space Industry

by organizations, the impact of innovations on existing systems, the adoption and diffusion of innovations, or the dynamics of innovations in the industry. In addition, innovation may involve different levels of analysis, such as the individual, industry, or economic system (Lenfle 2008; Schaeffer et al. 2016). The broad spectrum of innovation will, therefore, lead to quite varied answers to the question “Why do firms innovate?”. Practitioners will give answers rooted in their professional activity, such as my boss wants our company to innovate and improve the company’s products. Students will often provide answers related to their interests and daily lives, such as improving the company’s image and dealing with technological changes. Scholars will be influenced by the theories they use and will provide explanations such as gaining strategic advantage and meeting customers’ needs. Despite the diversity of answers, there is usually a convergence toward the fact that innovation is increasingly contributing to firms’ performance. Despite this close link between innovation and performance, it should not be forgotten that innovation is a very risky activity for firms. Several studies have shown that between 40% and 60% of new products have launched on the market fail (Chiesa and Frattini 2011; Cierpicki et al. 2000; Tidd and Bessant 2009). In fact, innovation is a paradoxical activity because it is both a source of performance and failure. Schumpeter (1911) calls this paradox “creative destruction,” emphasizing that innovation simultaneously generates a phenomenon of destruction and creation. Creative destruction is observed at extremely varying levels, such as the firm, the products, the market, or even the industry. For example, in firms, creative destruction will make some skills obsolete and new ones emerge. When we look at the products of a particular firm, the phenomenon of cannibalization appears as another

Theoretical and Empirical Framework

5

dimension of creative destruction. At a more global level, innovation will lead to the disappearance of existing markets and sectors and the emergence of new markets and sectors. In our opinion, the creative destruction phenomenon is one of the foundations of the legitimacy of innovation as a field of study for researchers and a teaching discipline for teachers. For example, Christensen’s (1997) work shows that it is the introduction of substitutes leading to creative destruction phenomena that legitimize the use of concepts specific to innovation, such as the innovator’s dilemma and cannibalization. Without creative destruction, more general notions of substitution and differentiation would often be sufficient to understand the introduction of innovations to the market. 1.1.2. Typology of innovations Drawing on Dosi’s (1982) work, we define innovation as a change that involves novelty and is characterized by a direction and a pace. It is the direction of change that determines whether or not innovation is progress (Chouteau et al. 2017). In order to characterize innovation more precisely, the authors have proposed many typologies that are often based on Schumpeter’s (1911) seminal work. We consider it relevant to characterize innovations according to three dimensions: type, level of novelty, and cumulativeness. 1.1.2.1. Types of innovations Based on the Oslo Manual (OECD and Eurostat 2005), we use the following types of innovations: — Product innovation: the market launch of a new good or service in terms of its characteristics (e.g. launch of a new car model). — Process innovation: implementation of a new production or logistics method for products and services (e.g. implementation of the “just-in-time” production management

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Innovation Trends in the Space Industry

method). This type of innovation is often referred to as “organizational innovation” to emphasize that the change is more about task organization than equipment. In other chapters, we will often use this terminology. — Position innovation: implementation of a new marketing method involving significant changes in the form, distribution, promotion, or pricing of a product (e.g. selling Coca-Cola in supermarkets rather than pharmacies). This typology makes it possible to characterize innovations, but it should be noted that it is sometimes of limited use. Indeed, it is common for the same innovation to be in several categories at the same time. For example, the implementation of a cash dispenser is: (1) a process innovation, because bank employees no longer have to handle cash so much; (2) a product innovation, because customers have a new service; and also (3) a position innovation, because the distribution channel for the banking service is available 24 h a day and 7 days a week. This situation is also observed when we adopt the point of view of the client who uses innovation. For example, the adoption of a product innovation, such as a new ComputerAided Design (CAD) tool, involves organizational innovations to take advantage of it. The work on co-evolution has generalized this finding and has shown that innovation very often involves both technological and organizational changes (Nelson and Nelson 2002; Nelson and Sampat 2001; Rosenkopf and Tushman 1998). This work does not aim to solve the typology problems induced by the diverse nature of innovations. We rather consider that it is sometimes appropriate to adopt a more robust but less precise typology. We will then simply distinguish between, on the one hand, organizational innovations and, on the other hand, technological innovations. We also define technology as the products,

Theoretical and Empirical Framework

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services, and knowledge that are used by firms to generate existing and/or new products and services (Dos Santos Paulino 2007). 1.1.2.2. The level of novelty The second dimension used to characterize innovations refers to their level of novelty. The level of novelty allows a more detailed understanding of the creative destruction inherent in innovations. The higher it is, the more innovation will lead to significant creative destruction. Based on the work of Schumpeter (1911), it is common to retain two levels of novelty. This choice is a simplification that sometimes causes measurement problems; however, it is very useful to ensure the internal validity of innovation theories. It should then be noted that the terms used to name the two levels of novelty are highly diverse within innovation studies (Ehrnberg 1995). Innovations with a high level of novelty are discontinuous innovations, also sometimes referred to as radical, disruptive, and new deal innovations. These innovations correspond to a significant change in the performance criteria, which is used to describe the existing product. This change can go as far as the introduction of many new performance criteria. One example is the launch of the world’s first car, which will introduce a significant number of new performance criteria compared to horse transport. Due to their significant novelty, discontinuous innovations are infrequent, difficult to imitate, and very costly to develop for firms. These innovations generate a significant strategic advantage when they are successful; however, they have a very high failure rate. Innovations with a low level of novelty are continuous innovations, also sometimes referred to as incremental innovations. These innovations correspond to a small change in the performance criteria used to describe the existing

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Innovation Trends in the Space Industry

product. An example is the launch of a new version of an existing product (e.g. launch of iPhone 4, 4S, 5, 5S, 6, etc.). Unlike discontinuous innovations, continuous innovations are more frequent, easier to imitate, and less costly to develop. These innovations generate a lower strategic advantage when they are successful; however, they have a lower failure rate. 1.1.2.3. Cumulativeness The cumulative nature of innovation is less often taken into account than the type and level of novelty; however, it provides an even better understanding of the central phenomenon of creative destruction (Ehrnberg 1995; Murmann and Frenken 2006; Nelson 1995; Utterback and Abernathy 1975). Cumulative innovations lead to a low level of creative destruction because they will improve existing performance criteria without changing the bundle of performance criteria that are used to describe products. One example is a new gasoline engine whose only difference compared to existing engines is a higher level of fuel efficiency. Non-cumulative innovations, on the other hand, lead to a more significant level of creative destruction. In addition to improving existing performance criteria, they will also change the bundle of performance criteria. These innovations will, on the one hand, change the hierarchy of performance criteria and, on the other hand, add and remove criteria. Apple’s launch of iPhone 1, when Nokia’s traditional mobile phones dominated the market, is a good example. iPhone 1 reduced the importance of the battery life criterion because its autonomy was much lower than that of traditional mobile phones. It also removed the performance criteria for the physical keyboard as this technology disappeared from the phone. Finally, it added criteria related to the touch screen technology that was newly incorporated into the phone.

Theoretical and Empirical Framework

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As iPhone 1 illustrates, non-cumulative innovations lead to powerful creative destruction phenomena on dimensions such as the producer population, strategic competencies, and client needs. This is why, these innovations are often proposed by new entrants while existing firms favor cumulative innovations. 1.1.3. Developing product innovations The development of product innovations can be seen as a process of accumulating knowledge to clarify the characteristics of the future product that will be launched on the market. The choice of characteristics to be clarified is the result of the development strategy implemented by the firms. The “technology push” strategy and the “market pull” strategy are the two main strategies available (Dodgson et al. 2008). In the 1950s and 1960s, organizations first considered innovation according to the “technology push” strategy. Here, the starting point of the development process is the identification of technological and scientific opportunities (Dodgson et al. 2008). These opportunities are identified and validated through Research and Development (R&D) activities that are central to the development of innovation. Technical profiles, such as engineers, play a central role and producers sometimes devote the vast majority of resources to clarify the technical characteristics of the future product. The clarification of commercial characteristics, such as customer needs, distribution channel, and promotion, is then considered secondary. The implementation of the “technology push” strategy leads rather to the development of innovations with a significant level of novelty. When innovations are successful in the marketplace, they generate a significant strategic advantage. Unfortunately, they are often commercial

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Innovation Trends in the Space Industry

failures because they do not sufficiently consider the customer’s needs. The Concorde aircraft illustrates the danger of this strategy quite well. Concorde was the pioneer in civil supersonic aviation, thanks to the integration of numerous technological innovations that have gradually been generalized on all commercial aircraft (e.g. autopilot, electric controls). Despite the relevance of these technological innovations, Concorde is an innovation that has been a commercial failure. One of the reasons for this failure is that the firms involved have not sufficiently sought to clarify the commercial characteristics of the aircraft (Kechidi and Talbot 2013). From the mid-1960s onward, the second development strategy, known as “market pull,” emerged (Dodgson et al. 2008). The starting point of the development process is now the identification of market opportunities and no longer technological and scientific opportunities. These opportunities are identified and validated through market research. Commercial profiles, like those of the marketing department, play a central role here, to the point of sometimes devoting a very large part of resources to clarifying the commercial characteristics of the future product. As customers do not express a need for what they do not know, the “market pull” strategy leads rather to the development of innovations with a low level of novelty. Innovations are less often rejected by the market with this strategy than with the “push technology” strategy. On the other hand, the strategic advantage obtained in the event of success is generally lower because imitation is faster. New services are mostly developed with the “market pull” strategy because customers often contribute to producing the service (e.g. the behavior of the spectators of a live performance influences the perceived quality of the performance).

Theoretical and Empirical Framework

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The obvious complementarity of these two development strategies leads most firms today to seek to combine them. The objective is to create an innovation that is difficult to imitate and that will be easily adopted by customers. However, this combination is not necessarily balanced. For example, service firms will focus more on the “market pull” strategy, and high-tech firms focus on the “technology push” strategy. It should also be noted that the combination of these two strategies requires technical and commercial profiles to work together. This is sometimes difficult to implement, as the training, objectives, and vocabulary can be different between these two profiles (Dos Santos Paulino and Tahri 2014). 1.1.3.1. Innovation development processes The methods used to develop product innovations (goods and services) are generally based on “stage-gate” processes where the primitive unit is the project. Each innovation project must go through several stages to be launched on the market. At each stage, the project is transformed, due to the clarification of its technical and commercial characteristics. For example, initially, the project is an idea, then it becomes a concept, a prototype, an industrial product, and finally a new product launched on the market. To move from one stage to another, the project must pass through a “gate” that represents a selection made by a committee, for example. This rational and structured process varies greatly from one firm to another depending, for example, on the nature of the products and the willingness of firms to structure their innovation process. For example, service firms have the least structured processes, while firms developing complex goods, such as aircraft and satellites, have highly structured processes.

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Innovation Trends in the Space Industry

1.1.3.1.1. The first processes What is considered an effective “stage-gate” process has undergone significant changes over time (Dodgson et al. 2008). In the 1950s and 1960s, NASA and the US Department of Defense developed one of the first “stagegate” processes, whose steps are based on the maturity level of the technology. This process, commonly known as “TRL1,” is still widely used, in more or less altered versions, in the defense, aeronautical and space industries. The original TRL contained nine sequential steps that did not allow for reversals and in which critical technological developments had to be done internally. This process thus gives the impression that the projects are like an “army in progress” (Dos Santos Paulino 2007). In the TRL, the commercial characteristics of the future product are largely ignored because it is the “technology push” strategy that prevails. This often leads those who use it to see the TRL as a technology management tool and less as an innovation management tool. The TRL is part of the first generation of innovation development processes. In the second generation of processes, the “market pull” strategy prevails; however, a sequential process is maintained, with no turning back and where critical developments take place internally. 1.1.3.1.2. More recent processes The third generation of innovation development processes is more recent and presents significant developments. Firms combine “technology push” and “market pull” strategies. The “stage-gate” process is then based on the overlapping of steps as well as on reversals and temporary stops. Although more flexibility is now being sought, critical developments are still carried out internally (Dodgson et al. 2008). In our opinion,                                         1 Technology Readiness Level.

Theoretical and Empirical Framework

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many firms are now implementing processes that are similar to those of the third generation. The fourth-generation process adds to these characteristics the fact that it is relevant to consider critical developments by significantly relying on resources external to the firm. Collaborations with suppliers and customers are sought. Based on the characteristics of the most successful forms of fourth-generation processes, some authors suggest the existence of the fifth generation of innovation development processes. However, there is no consensus on the existence of this fifth generation. For some, it would not really be new compared to the fourth generation (Trott and Hartmann 2009). For others, the fifth generation would be a set of ideal characteristics identified by researchers, but not a set of characteristics actually observed in firms (Dodgson et al. 2008). We do not wish to take part in this debate and will limit ourselves to describe certain characteristics of “open innovation,” which is the most documented form of the latest generation of “stage-gate” processes (Pénin et al. 2011). 1.1.3.1.3. Open innovation Open innovation was popularized by Chesbrough (2003) and is emerging due to the emergence of new constraints and opportunities. These include, for example, rising development costs, the need to reduce development times and easier appropriation of external knowledge through ICT (Information and Communication Technologies). Open innovation has two dimensions that describe reversed knowledge flows: “inside-out” and “outside-in.” The main novelty of open innovation compared to previous innovation development processes can be seen in the “inside-out” dimension (Pénin et al. 2011). The firm will

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Innovation Trends in the Space Industry

seek to give value to knowledge that it has developed but that is outside its core business. The objective of the firm is to develop, with the help of partners, dormant knowledge and technologies that could still lead to successful innovations. The “inside-out” approach is based, for example, on technology licensing and the creation of spin-off companies. The “outside-in” dimension is more traditional in the sense that it consists in relying on partners to develop innovations that are within the firm’s core business. The authors often refer to the fact that engaging in extensive collaborations with suppliers and customers in critical developments leads to a broadening of the spectrum of skills needed to innovate. Using suppliers, for example, gives purchasing and supply chain departments a central role in innovation processes (Pérez et al. 2017). “Buyers” can, therefore, be responsible for innovation projects since they are in the best position to identify and obtain strategic knowledge from suppliers. Regular purchasing of technology then requires much more significant use of legal vehicles, such as licenses. Lawyers must also be more involved in the development of innovations. These transformations can be sometimes countered by technical profiles who occupy intermediate positions in the firm’s management. These profiles, which can be found, for example, in the R&D department, may have the impression that the supply chain department puts them in competition with suppliers in the allocation of resources dedicated to innovation. Research on NIH syndrome2 has described this phenomenon and its causes in depth (Katz and Allen 1982; Lichtenthaler and Ernst 2006; Trott 2012). For example, this syndrome would be more frequent when the suppliers are SMEs and the client is a large firm.                                         2 Not invented here.

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1.1.4. The industry cycle The lifecycle of the industry is one of the most important theories for understanding innovation (Dodgson et al. 2008). It allows a dynamic and simultaneous understanding of several essential dimensions of innovation management, such as innovation sales, innovation rate, and the dynamics of market entry for suppliers and customers. It is first of all the product lifecycle that has interested the authors and in particular the marketing literature. Later, the work of Utterback and Abernathy (1975) sought to describe the lifecycle of innovations3. In parallel, the work initiated by Rogers (2003) has described the adoption/dissemination cycle of innovations4. The lifecycle of the industry is always divided into several sequential phases, the names and numbers of which vary. In this study, we focus on four phases that successively describe the emergence, growth, maturity, and decline of the industry. In Figure 1.1, we represent three dimensions of the industry’s lifecycle: (a) the product lifecycle, (b) the innovation lifecycle, and (c) the innovation adoption cycle. We observe that the lifecycle of the industry is made up of several dimensions generally represented by “bell” curves whose critical points indicate the passage from one phase to another. We comment on each of these phases below.                                         3 The publication of Utterback and Abernathy (1975) has inspired many other works, such as Teece (1986), Olleros (1986), Anderson and Tushman (1990), Klepper and Graddy (1990), Cainarca et al. (1992), Audretsch and Feldman (1996), Murmann and Frenken (2006), and Taylor and Taylor (2012). 4 Rogers’ work was first published in 1962 and has inspired many other works, such as von Hippel (1986), Mahajan et al. (1990), Christensen and Bower (1996), Moore (2002), Golder and Tellis (2004), Hauser et al. (2006), Taylor and Taylor (2012), and Frattini et al. (2014).

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Innovation Trends in the Space Industry

Figure 1.1. The industry cycle: three dimensions (a, b, and c). For a color version of the figures in this chapter, see www.iste.co.uk/dossantos/innovation.zip

1.1.4.1. The emergence phase During the emergence phase of the industry, sales are low and grow slowly (see Figure 1.1.a). There is a very high rate of product innovations due to the majority of non-cumulative discontinuous innovations. The products available thus present very different performance criteria, which make it

Theoretical and Empirical Framework

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difficult to compare them. There is considerable uncertainty about the performance criteria that will emerge in the market (Christensen and Bower 1996; Zhen and Démil 2015). Finally, the focus is on functional criteria rather than price or product image. Firms meet the specific needs of small groups of customers called “early adopters”5 (see Figure 1.1.c). These first adopters are strongly inclined to adopt innovations and gradually discover the product, what they can do with it and what needs it meets. Early adopters have several other distinctive characteristics: (1) they express the need for innovation before others; (2) they have high product expertise; (3) they have a high tolerance for risk and low quality; (4) they are slightly price-sensitive; and (5) they think adoption can have a very positive impact on them. It should be noted that early adopters may be individuals or organizations (Moore 2002; Rogers 2003). For products such as smartphones, early adopters are technophiles. In the case of industrial products such as aircraft, the early adopters are the Ministries of Defense. It is recognized that early adopters represent between 2% and 25% of the total population of adopters, although many studies generally use the figure of 16% (Libai et al. 2008; Mahajan et al. 1990; Muller and Yogev 2006).

                                        5 In several dictionaries, the term “adopt” refers to someone who adopts a child and not someone who adopts an innovation. Despite this, many leading French-speaking authors have chosen to use the term to refer to the person who adopts an innovation. These include Le Nagard-Assayag and Manceau (2011) who use it in the book Le marketing de l’innovation: Concevoir et lancer de nouveaux produits et services published by Dunod. It should also be noted that Emmanuelle Le Nagard-Assayag is co-editor-in-chief of the journal Décisions Marketing (ranked 3 FNEGE/CNRS in 2018) and Director of Research at the Université Paris Dauphine.

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Innovation Trends in the Space Industry

During the emergence phase, there is a small number of process innovations (see Figure 1.1.b). This corresponds to a situation where products are generally manufactured in small series and with low quality. The production units are small and rely on generalist production tools. There are often few suppliers available and producers must internalize the vast majority of production activities. Producers are organizations with informal structures led by entrepreneurial managers (Dodgson et al. 2008). 1.1.4.2. The growth phase During the growth phase of the industry, there is a rapid increase in sales and a lower level of product innovation (see Figure 1.1.a). From a technological point of view, the major event that occurs at the start of this phase is the emergence of an industrial standard that will gradually become the dominant design in the industry. This dominant design is not necessarily the best because it is the result of a social process based, for example, on consensus or a struggle between the different stakeholders. Thanks to the dominant design, there is a standardization of both the bundle of performance criteria and the hierarchy between the criteria. This will greatly reduce the level of uncertainty for adopters who now understand more easily what the product can be used for. This new technological context, on the one hand, triggers the exit of early adopters who have more difficulty finding products that suit them and, on the other hand, allows the entry of the second group of adopters called “mainstream adopters” (see Figure 1.1.c). Mainstream adopters are less likely to adopt innovations because of their distinctive characteristics: (1) they have less expertise on the product; (2) they have a lower tolerance for risk and poor quality; (3) they are more price-sensitive; and (4) they think that adoption will have a lower impact on their wellbeing/performance. Mainstream adopters are a group that represents between 98% and 75% of the total population of

Theoretical and Empirical Framework

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adopters, although many studies use the figure of 84% (Libai et al. 2008; Mahajan et al. 1990; Muller and Yogev 2006). When a more detailed representation of this population is required, scholars identify subgroups within the mainstream adopters, such as the early majority and the late majority. These different subgroups will have a decreasing inclination to adopt innovation. For products such as smartphones, it can be said that the mainstream of adopters are conservative and pragmatic customers. Regarding the adoption of industrial products such as aircraft, commercial airlines are an example. The emergence of the dominant design also greatly reduces uncertainty for producers. Standardization of performance criteria allows producers to devote more resources to improving the performance criteria that have emerged rather than identifying new criteria. In other words, there is a decline in discontinuous and noncumulative product innovations in favor of continuous and cumulative product innovations. The entry of mainstream adopters also calls for more process innovations that are essential to increase production volumes and improve quality (see Figure 1.1.b). The production tool changes through the use of more specialized means of production for manufacturing the product. A network of specialized suppliers emerges, and they are integrated into production thanks to many process innovations. Finally, producers are now more structured, with work organization based on project teams and groups with more specialized tasks (Dodgson et al. 2008). 1.1.4.3. Maturity and decline In this work, we group together the maturity and decline phases because they are of less interest to us. In the following chapters of this study, we wish to focus on the emergence phase.

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Innovation Trends in the Space Industry

During the maturity phase, sales volumes stabilize and then decline, which starts the decline phase (see Figure 1.1.a). From a technological point of view, we are witnessing a further decline in product innovations as the dominant design has now spread widely (see Figure 1.1.b). The products available are similar in terms of a bundle of performance criteria and hierarchy between criteria. In this context where the products available are less and less innovative, adopters make their choices above all on the basis of price. As some mainstream adopters begin to leave the market, there is a final subcategory of mainstream adopters called “laggards.” These adopters have delays in adoption because they are skeptical about innovations. In addition, they often adopt innovations because they are forced to do so. As regards process innovations, there is a decrease in their level, even if they remain at a higher level than product innovations (see Figure 1.1.b). Process changes will allow producers to reduce their cost of production by increasing economies of scale. Producers will adopt structures based on stronger and more rigid operational rules. They will also use highly specialized production tools and rely even more on their network of highly integrated suppliers. The decline phase is interrupted by the introduction of discontinuous and non-cumulative innovations. This makes it possible to start a new cycle that will be observed in the same industry or in a new industry. 1.1.4.4. Limitations of the industry lifecycle Although the lifecycle of industry is one of the most important theories for understanding innovation, it has several limitations (Dodgson et al. 2008). First, it is sometimes difficult to identify the boundaries of the industry with sufficient precision in the early years of the industry’s emergence. We can illustrate this with the aerospace

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industry. This industry has emerged through a gradual process of combining new technologies. At the same time, the growing divergence between aeronautical and space activities has raised the question of the emergence of two distinct industries: the aeronautical industry and the space industry. This evolution makes the treatment of the aerospace industry delicate. It is difficult to know whether this industry has disappeared, is continuing its cycle, or never really existed. Second, the lifecycle does not sufficiently describe why industries move from one phase to another (Klepper 1997 ; Klepper and Malerba 2010). The emergence of the dominant design provides interesting insights since it clarifies the transition from the emergence phase to the growth phase. However, it is useless when attempting to understand the other phases. Moreover, it is simplistic to consider that the transition from the emergence phase to the growth phase is determined solely by technological aspects (Gustafsson et al. 2016). Third, the analytical framework provided by the lifecycle of the industry has difficulties in understanding industrial diversity when used to conduct studies requiring significant empirical validity. For example, some industries have very short phases, while for other industries they are much longer. One example is the smartphone industry, which seems to have reached maturity in less than three decades in developed countries. On the other hand, civil air transport has not yet completed its growth phase in developed countries, after a hundred years of existence6. Other industries seem to have a development that differs from that described by the lifecycle. For example, the aerospace                                         6 It is recognized that the first smartphone is the IBM Simon launched in 1994 and that the first civil transport mission by air dates back to 1911.

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Innovation Trends in the Space Industry

industry has a high level of product innovation over a long period from 1945 to 1997. In order to address these three limitations, we consider that the lifecycle of the industry must be used as an ideal type that differs from reality. We then believe that it is possible to explain these differences by looking at the features of the industry studied. In other words, it is the contextualization of industry lifecycle theory that we believe reinforces the validity of this theory (Johns 2006). For example, it is often possible to explain the existence of a temporary period of declining sales during the growth phase when exogenous factors, such as wars and economic crises, are taken into account. 1.2. The space industry In this section, we present the space industry in a way that allows to take into account the economic considerations. First, we legitimize the place of this industry; we highlight the lack of work in management and economic sciences on this industry, and then the innovative nature of space activities (Dos Santos Paulino 2007; Dos Santos Paulino and Le Hir 2016). After presenting our sources, we study the boundaries of the space industry. We show that the industry has blurred boundaries and that it is part of a larger whole, which is the space economy (Pérez et al. 2017). The rest of this section consists of a detailed description of the structure of the industry: products, customers, market rules, and producers (Barbaroux and Dos Santos Paulino 2013; Dos Santos Paulino and Barbaroux 2016; Dos Santos Paulino and Callois 2010). The space industry manufactures launchers, spacecraft, and ground equipment that are purchased by military, civilian institutional, commercial, and mixed customers (e.g. military—civilian customers in communist countries).

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Launchers and spacecraft are bought in an uncertain space environment that results in poor reliability of industrial products (Dos Santos Paulino 2007). The structure of the industry is affected by several market rules that are far from those observed in competitive situations. Finally, the industry’s producers are a relatively stable group of large organizations. This is due to the significant entry barriers that characterize the space industry (Dos Santos Paulino and Le Hir 2016). 1.2.1. Why study the space industry? The space industry is regularly studied in the humanities and social sciences, such as history, political science, and law. Examples include the work conducted by John Lodgson in institutions such as the Space Policy Institute and Space Policy magazine. On the other hand, research on the space industry from the perspective of management and economic sciences is much rarer (Shove 2005). Our work aims to fill this gap by approaching this industry from an innovation management perspective. This low interest of authors in management and economics is quite surprising, given that many critical infrastructures in post-industrial economies are largely dependent on space technologies. We can mention for example: (1) telephony, radio and television services; (2) banking and financial market operations; (3) meteorology; (4) navigation systems for air, sea, and land transport; and (5) national security (Barbaroux and Dos Santos Paulino 2013; Shove 2005). The space industry is also a relevant subject of study for those interested in innovation because it falls in the category of high-tech industries with biotechnology, semiconductors, and information technology industries. Innovation plays a central role in the space industry, which can be observed in

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Innovation Trends in the Space Industry

many dimensions. For example, spacecraft allow humanity to extend its influence outside the Earth and space activities require very large research and development budgets. Although it is a high-tech industry, the space industry has not kept all its promises. There is a wide gap between the expectations raised by the space conquest of the 1970s and what space activities are today. The space industry appears to be a counterexample of an innovative industry and, as such, it has also attracted our curiosity (Dos Santos Paulino 2007). In recent years, the space industry has been changing rapidly. We are witnessing the arrival of new entrants who are seeking to build their success on new technologies and new strategies. These changes are important enough to awaken the “sleeping beauty” that the space industry had gradually become. This renewal of the dynamics of innovation in space activities has also aroused our curiosity. 1.2.2. Sources and level of analysis In this book, we use both qualitative and quantitative secondary sources. Qualitative sources were exploited thanks to a content analysis and are structured into three categories: (1) websites of space organizations (e.g. Bryce, Futron Corporation); (2) press articles (e.g. Space Review, Spaceflight); and (3) academic sources dealing with the space industry (e.g. Space Policy). Quantitative sources are open archival datasets accessible on the Internet (e.g. Claude Lafleur, Gunter Krebs, Findthedata, and EO Portal). These archival data are reliable sources of information used by the authors in their research (Barbaroux and Dos Santos Paulino 2013; Dos Santos Paulino and Le Hir 2016; Zelnio 2007) and by practitioners to conduct strategic intelligence (ASDEurospace 2015). Using these quantitative sources, we have

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built an original database used to describe the space industry. This database presents a selection of criteria describing the industry’s products for the period from 1957 to 2011 (e.g. customers, use, etc.). Our objective is to present the space industry as a whole in order to give the most global representation possible. We describe the industry by avoiding focusing exclusively on a particular set of countries, technologies, actors, and years. 1.2.3. The boundaries of the space industry 1.2.3.1. Blurred boundaries The space industry is rooted in the advances of science and technology in the first half of the 20th century (e.g. mathematics, chemistry, metallurgy, propulsion, electromagnetism, etc.). However, it is customary to consider the launch of the first artificial satellite, Sputnik 1, as the event triggering the birth of the space industry (Barbaroux and Dos Santos Paulino 2013). October 4, 1957, thus, marked the beginning of what McDougall called the “spaceage” (McDougall 1982, p. 1010). This success allows humanity to expand its activities to a new environment that disrupts existing value scales. With Sputnik, the Russians introduced a “saltation technology” (McDougall 1982). By saltation technology, we mean a technological discontinuity that is capable of generating a new industry. The boundaries of the space industry have changed significantly since the late 1950s. For a long time, the difference between the space industry and the older aviation industry was not obvious. There were organizations operating in what was then called the “aerospace” industry. In France, the Société Nationale Industrielle Aérospatiale, which later became Aérospatiale, is a perfect example of this situation (Kechidi and Talbot 2013).

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Innovation Trends in the Space Industry

Subsequently, the development of air transport and the emergence of services based on the use of satellites have stimulated the divergence of aeronautical and space activities. Within the aerospace industry, technologies, producers, customers, suppliers, and regulations have evolved in different ways. Gradually, the actors constituting the industry no longer recognized themselves as part of a single industry. This divergence took a major step forward in France at the end of the 1990s. It was at this time that Aérospatiale was dismantled in several parts that were merged with other organizations to create more specialized companies in space (Alcatel Space, Astrium) and in aeronautic (EADS). The space industry is now more and more approached autonomously; however, it remains an industry with blurred boundaries. For instance, more recent mergers again led to group space and aeronautical activities within the same companies (e.g. in mid-2010s Airbus absorbed Astrium to create its Space Systems business unit). The imperfect classification of the space activities in France, Europe, and the United States also illustrates the blurred boundaries of the industry. There are no NAF/NACE/NAICS codes that specifically designate space activities. Space activities are aggregated with other very different activities, such as missile manufacturing, telecommunications equipment, or research and development in life sciences, physics, and engineering (Hertzfeld 2002; OECD 2012). The last manifestation of the blurred boundaries of the space industry is the diversity of value chains observed. The production of telecommunications satellites is based on a value chain whose structure is partly different from that required for the production of meteorological satellites. Each value chain may also vary from one country to another. For example, the level of integration of actors tends to be lower in capitalist countries than in communist countries (Dos Santos Paulino and Barbaroux 2016).

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1.2.3.2. Space industry and space economy

Figure 1.2. Space economy: two value chains. For a color version of the figures in this chapter, see www.iste.co.uk/dossantos/innovation.zip

The space industry is the upstream value chain of a larger whole, which is the space economy. The downstream chain of this economy is composed of a heterogeneous set of industries and actors that depend on space technologies for their activities. Examples include (1) defense industries that use satellites to ensure national security; (2) the transportation industry that requires the GPS signal provided by satellites to manage fleets; (3) the

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Innovation Trends in the Space Industry

telecommunications industry that uses satellites to broadcast satellite television and provide telephone services; and (4) meteorological services that require satellites to make forecasts. In Figure 1.2, we provide a simplified representation of the space economy. Given the blurred boundaries of the space industry, it is difficult to accurately measure its turnover. Nevertheless, some studies estimate $339 billion in revenue for the global space industry (Bryce Space and Technology 2017). Although this figure should be taken with caution, it still gives an order of magnitude. The turnover of the space economy is much higher; however, it is impossible to calculate because the downstream value chain is very heterogeneous. In this study, we will focus more on the upstream value chain of the space economy than on its downstream value chain. 1.2.4. Structure of the space industry 1.2.4.1. Products The space industry manufactures three main categories of products: launchers, spacecraft, and ground equipment. When these products are manufactured in a coordinated manner to achieve a particular objective, they are often referred to as space programs. For example, the Apollo program has produced launchers, spacecraft and ground equipment that have allowed people to go to the Moon. In the economic literature, space industry products are considered as high-tech capital goods that are sometimes referred to as CoPS (Complex Products and Systems) (Ayerbe et al. 2014; Dedehayir et al. 2014; Dosi et al. 2003; Dos Santos Paulino and Callois 2010; Dos Santos Paulino and Le Hir 2016; Hobday 1998; Prencipe 2000).

Theoretical and Empirical Framework

29

1.2.4.1.1. Launchers Launchers are essential to place spacecraft in space. There are about 10 organizations in the world capable of this feat, mainly in the United States, Russia, Europe, China, Japan, and India. In the space industry, launchers are like the tip of the iceberg. They enjoy very strong media coverage; however, they represent only 1.6% of the industry’s turnover (Bryce Space and Technology 2017). In addition, launch activities are so regulated that the concepts developed in strategy and economics are regularly challenged. For example, the cost of production of a launcher is very difficult to determine, as its design and production are subsidized through complex mechanisms.

Figure 1.3. Soyuz rocket at takeoff (Source: Pixabay). For a color version of the figures in this chapter, see www.iste.co.uk/dossantos/innovation.zip

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Innovation Trends in the Space Industry

1.2.4.1.2. Spacecraft Spacecraft are a very important unit of analysis throughout this study. There is a wide variety of spacecraft that can be grouped into three categories: satellites, space probes, and other spacecraft (e.g. space stations, cargo ships). Traditionally, satellites cost between $100 and $400 million, to which the client must add the launch price, which is two-third of the price of the satellite itself (Blanc 2010). Although there is significant variability, traditional satellites generally weigh several tons, have a lifetime of more than ten years, and have an electrical power of about 4,000 W.

Figure 1.4. Satellite (Source: Pixabay). For a color version of the figures in this chapter, see www.iste.co.uk/dossantos/innovation.zip

A complementary way to describe spacecraft is to group them by use. The launch in 1957 of the first artificial satellite (Sputnik 1) marked the birth of the space industry and the space application Research and Development. Five other applications will then quickly emerge. Also, in 1957, the Russians launched Sputnik 2 with the dog Laika onboard. Sputnik 2 will be the first satellite of the Scientific Research application. In 1959, the first spy satellite was launched, giving rise to the Earth observation application. Then, in 1960, the first spacecraft were launched in the Manned Vehicles and Navigation applications, respectively.

Theoretical and Empirical Framework

31

Finally, a few months later, the first telecommunications satellite was launched, resulting in the appearance of the last spatial application (Barbaroux and Dos Santos Paulino 2013; Dos Santos Paulino and Barbaroux 2016). Figure 1.5 shows the number of spacecraft launched for each of the six applications between 1957 and 2011.

Figure 1.5. Spacecraft launched by application between 1957 and 2011

It can be seen that the most common applications are Earth observation (about 33% of the total) and telecommunications (28%). In the first category, there are mostly satellites that are used to conduct espionage operations (e.g. photography, listening), followed by civilian Earth observation and meteorological satellites. The telecommunications application is composed of satellites that support the infrastructure needed to watch television and listen to radio. The other four types of applications are less represented. The Scientific Research application consists in developing spacecraft whose objective is to increase the stock of fundamental knowledge about the space environment. The Research and Development application corresponds to the

32

Innovation Trends in the Space Industry

development of spacecraft that make it possible to test and validate new technologies that will eventually be integrated into operational spacecraft (e.g. telecommunications and Earth observation satellites). After being placed in space by a launcher, the manned vehicles carry out transport and refueling missions. Finally, the navigation application allows you to offer services such as the American GPS, which is very useful for location (Dos Santos Paulino and Barbaroux 2016). 1.2.4.1.3. Ground equipment The third category of products made by the space industry is ground equipment, such as antennas for transmitting and receiving satellite signals.

Figure 1.6. Antennas in a ground station (Source: Pixabay). For a color version of the figures in this chapter, see www.iste.co.uk/dossantos/innovation.zip

A distinction must be made here between, on the one hand, ground equipment used by satellite manufacturers and space agencies and, on the other hand, ground

Theoretical and Empirical Framework

33

equipment used by end customers. Only the first category is part of the space industry. The equipment used by end customers is rather part of the downstream chain of the space economy and, in particular, the electronics industry. In this study, we will pay limited attention to ground equipment in the space industry and ignore those in the electronics industry. 1.2.4.1.4. High uncertainty and reduced product reliability Spacecraft are used in highly uncertain and inaccessible environments. This hinders the accumulation of knowledge and leads to the production of unreliable products. The Russian-led Venera program in the 1960s and 1970s is a perfect example of this situation. At the beginning of the space conquest, the planet Venus was considered a tropical paradise because of its proximity to the Sun (van Vogt 1969). However, the Russians discovered, thanks to the Venera probes, that this was absolutely not the case. The Russians had eight failures before they realized, thanks to Venera 9 (1975) that the planet Venus was far from having welcoming environmental conditions. The Russians discovered a world where the pressure is 90 times higher than that of the Earth7, where the temperature exceeds 490°C, and where sulfuric acid rains fall incessantly and quickly eat away at all types of equipment. This discovery did not discourage the Russians who continued to explore Venus. They built much stronger probes and launched up to version 15 of the Venera probes. Despite all their efforts to strengthen the technologies used, version 15, which was the most robust, only worked for a little over 2 h in the Venus environment (Brunier 2006; Dos Santos Paulino 2007).                                           7 This pressure is equivalent to what can be found on Earth at a depth of 1,000 m underwater.

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Innovation Trends in the Space Industry

Figure 1.7. Space probe (Source: Pixabay). For a color version of the figures in this chapter, see www.iste.co.uk/dossantos/innovation.zip

The places where spacecraft are used are very difficult to access because they are often thousands of kilometers away. For example, the majority of telecommunications satellites are placed in an orbit 36,000 km from the Earth. As a result, learning dynamics are severely hampered. Thus, 7% of the failures observed in these satellites have an unknown cause (Ritchie and Lallour 1998). This figure may seem low; however, it is not because it concerns spacecraft that have a commercial rather than scientific vocation (Dos Santos Paulino 2007). The inaccessibility of the space environment is reinforced by the fact that it is impossible to simulate all its properties on the ground. Some properties of the space environment are unknown, while others cannot be simulated due to physical constraints or insurmountable costs. This inability to test all space technologies would be the cause of 60% of satellite failures in orbit (Dos Santos Paulino 2007). Overall failure rates provide a more systematic representation of the lack of knowledge and associated reliability issues. For example, among the 218 scientific

Theoretical and Empirical Framework

35

space probes launched since the beginning of planetary exploration and which completed their mission in 2008, there is a 45% failure rate8 due to technical problems (Dos Santos Paulino 2014a). For launchers, the failure rate is 6.59% for all launches between 1998 and 2009 (Kyle 2011). This figure must be compared with the reliability of commercial aircraft, which is 99.99%. 1.2.4.2. Customers and market rules 1.2.4.2.1. The customers Sometimes, States are the unit of analysis used to describe the customers who purchase spacecraft9. This approach based on political science seems to us to be insufficient to understand the economic considerations at work in the space industry. Using States as a unit of analysis makes difficult the consideration of private multinational organizations that purchase satellites to market a service. It also masks the existence of different State organizations that purchase spacecraft with particular objectives. For example, space agencies pursue an economic development objective but not military organizations. By customer, we, therefore, mean a legal and organizational entity that purchases spacecraft and not a State (OECD 2014). There a few customers in the space industry and they can be grouped into four categories. Figure 1.8 shows the spacecraft purchases made by the industry’s four types of customers between 1957 and 2011.

                                        8 These figures have been calculated from Williams (2008) “Chronology of Lunar and Planetary Exploration.” 9 Many illustrations can be found in the articles published in the reference journal “Space Policy.” Examples include Broniatowski and Weigel (2008) and Chen (2016).

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Innovation Trends in the Space Industry

Figure 1.8. Purchases by customer type between 1957 and 2011

It can be seen that the vast majority of purchases are made by military and civilian institutional customers. These customers are national or supranational governmental organizations such as Ministries of Defense, NATO10, and NASA. Military customers are the largest purchasers of spacecraft, accounting for 58% of all spacecraft purchased between 1957 and 2011. Spacecraft are an effective means of improving defense and security infrastructure. In addition, it is important to note that military customers purchase spacecraft in all application categories. Civilian institutional customers are the second largest purchasers of spacecraft, accounting for 21% of purchases between 1957 and 2011. These are mainly space agencies that purchase mainly scientific, research and development spacecraft (e.g. NASA, CNES, and ESA). Commercial customers account for 12% of purchases and mainly purchase telecommunications satellites. These customers are satellite operators with a fleet of satellites (e.g. Intelsat, Eutelsat, Arabsat, SES, etc.).                                         10 North Atlantic Treaty Organization.

Theoretical and Empirical Framework

37

Their job generally consists of renting bandwidth to organizations in the telecommunications industry. The mixed group represents a heterogeneous set of customers in which there will be military—civilian organizations belonging, for example, to communist countries and public— private partnerships. Mixed customers represent only 9% of total purchases but they have played a decisive role in the industry by often taking the risk of buying first (Barbaroux and Dos Santos Paulino 2013; Dos Santos Paulino and Barbaroux 2016). Mixed customers purchase spacecraft in all application categories. Customers place cyclical orders, which give a cyclical character to the entire industry. It is often the replacement of a spacecraft or an end-of-life space program that triggers an order. In addition, these cycles are long because customers implement strategies built with a time horizon ranging from about 10 years to several decades. This situation legitimizes longitudinal approaches to studying the space industry. The space industry is strongly influenced by demand (Barbaroux and Dos Santos Paulino 2013; Dedehayir et al. 2014; Dos Santos Paulino and Barbaroux 2016). This influence can be observed through four main dimensions: — Military and civilian institutional customers have created the industry through their desire to have space technologies that help them achieve military and political status objectives. These two objectives have structured the evolution of the industry through the phenomenon of path dependence (Bruggeman 2002; David 1985). — Military and civilian institutional customers have financed innovation by massively purchasing scientific and research and development spacecraft. These purchases have greatly influenced the direction and speed of the industry’s innovation dynamics.

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Innovation Trends in the Space Industry

— The customers are expert customers who decide precisely on the technical specifications of the spacecraft. Moreover, for a long time, producers have had the function of producing technologies based on the specification documents provided by civilian institutional customers. — Military and civilian institutional customers have the ability to influence market rules. We detail this point below. 1.2.4.2.2. Market rules and protectionism The existence of civilian—military and institutional customers leads to the observation of protectionist-inspired market rules. These rules have a significant impact on the structure of the industry because, for example, the share of purchases made by these customers varies between 40% and 100% between 1957 and 2011. Industrial products are the first subject to export control (De La Chapelle 2015; Hudson 2008; Masson-Zwaan 2017; Mineiro 2011). These regulations aim to limit the proliferation of war materials and exist in all countries. Export controls apply to arms but also to dual-use goods, including space industry products. A dual-use item is a product or service that is likely to have both civilian and military use. The sale of spacecraft to foreign civilian customers is therefore subject to the issuance of an export license by the producer’s government. Generally, producers scrupulously comply with this binding regulation because the sanctions in the event of infringement are very dissuasive. Space industry products have the particularity of being systems composed of technologies from different countries. Exporting, therefore, requires obtaining licenses from each of the countries from which the technologies originate. The regulation of a particular country, therefore, has a supranational influence. For example, European producers often have to apply for an export license from the United

Theoretical and Empirical Framework

39

States to export spacecraft made with American US technologies. Requests may be refused on the grounds that the United States does not want American technologies to be used by customers from certain states. This is the case, for example, with Chinese and Iranian customers. In other words, it is impossible for French producers to sell satellites incorporating American technologies to customers in these countries (Mineiro 2011). For other countries, the situation is much more ambiguous because everything will depend on the technology that producers want to export. However, the list of exportable technologies is neither public nor common to all States. In this case, the sale of technologies will depend on a process whose outcome, duration, and cost are partly random. Export control is a regulation that can generate high barriers to entry for foreign customers. Ultimately, this regulation is a form of technological protectionism that hinders the spread of space industry products. Another type of protectionism, similar to educational protectionism, also strongly influences the structure of the industry. There is a national preference among military and civilian institutional customers that leads them to systematically favor domestic producers over foreign producers (Bini 2007; Gaubert and Lebeau 2009; Pisano 2006; Zelnio 2007; Zervos 2008). This preference leads to the creation of captive markets in which there is little competition between producers and high prices. The foundations of national preference are based on the objectives of building an industrial base, but also on national security and technological independence. In Figure 1.9, we present the main institutional markets in 2016. The size of each area is proportional to the size of each national market. Relatively simple when the client is backed by a single State, the national preference rule is more sophisticated when it comes to a supranational customer. The implementation of “geographical return” within the European Space Agency (ESA) illustrates this. Purchases

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Innovation Trends in the Space Industry

from producers must be in proportion to their State’s contribution to the ESA budget. For example, French producers should benefit from about 24% of ESA purchases because the funds paid by France contribute 24% of ESA’s budget. Like national preference, geographical return sometimes gives secondary importance to technological performance and price. Indeed, if the geographical return is not proportional, Member States may leave ESA, which is considered more harmful than the distorted competition.

Figure 1.9. Main institutional markets [Source: adapted from Révillon (2019)]

1.2.4.3. Producers The space industry producers are mainly large companies, the best known of which are Airbus Defence and Space, Thales Alenia Space, and OHB11 in Europe; Boeing and Lockheed Martin in the United States; CAST12 in China; and Lavochkin and Reshetnev in Russia.                                         11 Orbital high-technology Bremen. 12 China Academy of Space Technology.

Theoretical and Empirical Framework

41

The production of spacecraft, launchers and, to a lesser extent, ground equipment is an activity that is based on several key success factors. First, there is a significant capital requirement as producers use expensive facilities to produce and test their products (e.g. clean rooms, testing facilities). As client payments are made throughout the life of the satellites, this requires producers to have a significant financial base (Dos Santos Paulino and Le Hir 2016). Manufacturing expenses must be advanced before receiving full payments from the customer. Knowledge to ensure reliability is another key factor for success. Very few producers in the world have sufficient knowledge of the space environment to be able to produce reliable products. This knowledge is acquired through continuous and significant investments in technology and process improvement. Producers, for example, adopted processes to increase reliability at an early stage, such as TRL13, PERT14, and PPP15. The existence of many processes to ensure reliability has led to space technology producers being classified as high-reliability organizations (Dos Santos Paulino 2007, 2009; Weick and Roberts 1993). These organizations give reliability a central role and are responsible, for example, for the production and management of nuclear power plants and air traffic control systems. Effective adaptation to market rules, such as export control and national preference, is the last key factor for success. This requires building and maintaining close relationships with military, civilian institutional, and governmental customers.

                                        13 Technology readiness level. 14 Program evaluation and review technique. 15 Phased project planning.

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Innovation Trends in the Space Industry

These key success factors are difficult entry barriers for new entrants to overcome. Thus, until very recently, producers were a small and relatively stable group of organizations. However, this situation has tended to change in recent years with the entry of new producers, such as SpaceX, Blue Origin, and OneWeb. These firms are often backed by very charismatic entrepreneurs and have considerable financial resources (e.g. Elon Musk, founder of PayPal and Tesla; Jeff Bezos, founder of Amazon) (Dos Santos Paulino and Le Hir 2016). Spacecraft are poorly standardized and are generally manufactured in very small batches (i.e. 1—2 units). There is a constant dialogue between the producer and his client in order to manufacture what is considered to be a “high-tech artisanal product.” Customers often redefine product specifications during the manufacturing process, which lasts between 2 and 7 years. It is rare for a producer to have all the skills to manufacture a spacecraft on his own. It must be supported by a sophisticated supply chain. Prime contractors are responsible for a significant part of the design and assembly of spacecraft. The first-level suppliers are responsible for subsystems, such as payload and platform. The second-level suppliers are in charge of critical equipment, such as antennas and propulsion systems. Finally, the third and fourth level suppliers produce special equipment and engineering services. This supply chain is not fixed. The same company can be the prime contractor and then the first- and second-level suppliers. National preference and geographical return rules are often the explanation for the variable position of producers in the supply chain. This is the case, for example, of Airbus Defence and Space and Thales Alenia Space, which are chosen in turn as prime contractors and suppliers by the French space agency.

2 The Emergence of Industry: The Influence of Demand

The space industry was born out of the desire of the Russian and American military to gain an advantage at the beginning of the Cold War. How can this type of evolution be described? What role do customers play in this evolution and innovation? Are there any characteristic phases in the industrial dynamics? By studying the evolution of sales, the entry of adopters and the rate of innovation, we show, in section 2.1, that the space industry is an emerging phase between the launch of the first artificial satellite in 1957 and 2011. This result leads us to say that the space industry has a long emergence phase (Barbaroux and Dos Santos Paulino 2013; Dos Santos Paulino 2014b). In section 2.2, we examine the essential role played by customers during the emergence of the space industry. Customers have shaped the space industry by pursuing five objectives: (1) military, (2) national prestige, (3) science and technology, (4) economic benefits, and (5) profits. In order of importance, military customers, then civilian institutional customers (e.g. space agencies), and finally commercial customers have shaped the industry. We show that the emergence phase itself is composed of several periods that

Innovation Trends in the Space Industry, First Edition. Victor Dos Santos Paulino. © ISTE Ltd 2020. Published by ISTE Ltd and John Wiley & Sons, Inc.

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Innovation Trends in the Space Industry

describe a characteristic evolution. First, there are periods dominated by military customers, then periods of military disengagement coinciding with a strengthening of commercial customers. Finally, we show that there are three models of national support for the space industry (Barbaroux and Dos Santos Paulino 2013; Dos Santos Paulino 2011). In section 2.3, we analyze more precisely the influence of demand on the dynamics of technological change in the space industry. We observe a tendency to lose momentum in these dynamics by studying: technology transfer, technology adoption, patenting, and innovation. We show that this evolution is explained by the variation in the weight given by demand to the science and technology objective. Over time, the weight of this objective is impacted by: (1) the desire of customers to diversify space applications, (2) the decline in military budgets, (3) the evolution of opportunities in the industry, and (4) customer expectations in terms of reliability. These results suggest a strong link between product reliability, the slowdown of innovation, and the survival of the space industry (Dos Santos Paulino 2011, 2014b; Dos Santos Paulino and Callois 2010). 2.1. The space industry is in the emerging phase 2.1.1. Emergence as an object of study When we look at the evolution of demand, we see that some products have slower adoption dynamics than others. For example, we observe those products such as television, telephone, and aircraft have emergence phases that last between 20 and 45 years. On the other hand, products such as the Internet, PC, and mobile phones have emergence phases that last less than 10 years.

The Emergence of Industry: The Influence of Demand

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Figure 2.1. Adoption of product innovations (Source: Unknown, Forbes Magazine, July 7, 1997). For a color version of the figures in this chapter, see www.iste.co.uk/dossantos/innovation.zip

The existence of long emergence phases for some products leads us to consider that the emergence phase is legitimate as a subject of study in its own right. According to Forbes and Kirsch (2011, p. 592), this period conditions “our understanding of the processes that occur in the later phases of the industry’s lifecycle.” Due to the existence of a path dependence, the industrial structure that is put in place during the emergence phase will constitute the foundation on which the industry will grow, mature, and decline. Despite the importance of this particular moment, it can be seen that the emergence of a new industry is a relatively unexplored phenomenon in the literature (Aldrich and Fiol 1994; Forbes and Kirsch 2011; Zhen and Démil 2015). Approaches that address the evolution of industries, including industry lifecycle theory (Utterback and Abernathy 1975), address industrial dynamics by studying

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Innovation Trends in the Space Industry

technological changes, and changes in industry structure throughout the life of the industry, from its birth to its decline (Klepper and Graddy 1990). Similarly, population ecology theory (Hannan and Freeman 1977) addresses the issue of industrial dynamics in terms of changes in the number and size of firms during the development of industry (Peltoniemi 2014). While these theories make it possible to break down the development process of industry into distinct phases (birth/emergence, growth, maturity, and decline) and to associate them with certain stylized facts (e.g. growth rate of the number of firms, distribution of firm size, technological and innovation regimes), some authors propose a specific analysis of the emergence phase of the industry (Nygaard 2008). According to Forbes and Kirsch (2011, p. 589), this is because “emerging industries are difficult to study because it is often difficult to identify emerging industries before they have become mature.” Not only it is difficult to accurately identify the time sequence corresponding to the emergence phase of the industry, but many industries disappear before they grow, making the emergence phenomenon difficult to study empirically and theoretically (Forbes and Kirsch 2011). 2.1.2. Characterizing emergence Based on the concept of the industry’s lifecycle, it is possible to characterize the emergence phase of the industry through three complementary dimensions: sales evolution, entry of adopters, and innovation rate. The evolution of sales is described by the product lifecycle shown in Figure 2.2.

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Figure 2.2. Product lifecycle. For a color version of the figures in this chapter, see www.iste.co.uk/dossantos/innovation.zip

The emergence phase, also known as the “launch,” is characterized by low sales and a low growth rate. The growth phase describes a take-off in sales, following the increase in their growth rate. The maturity phase has high sales and a zero-growth rate. The decline phase describes a decrease in sales. As the phases are defined in relation to each other, it is not obvious to identify the emergence phase until the growth phase is sufficiently advanced. Another dimension to characterize the emergence phase of the industry is the study of the market entry of adopter categories. In Figure 2.3, we represent the adoption cycle of innovations according to a dual market perspective composed of early adopters and mainstream adopters (Libai et al. 2008). It should be noted that, during the emergence phase, most purchases are made by early adopters. The second category of adopters only becomes dominant during the growth phase (Libai et al. 2008). More specifically, the total sales to mainstream adopters must be greater than the total sales to early adopters in order to be able to claim that the emergence phase has ended.

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Figure 2.3. Innovation adoption cycle. For a color version of the figures in this chapter, see www.iste.co.uk/dossantos/innovation.zip

The last way to characterize the emergence phase is based on the innovation lifecycle. This cycle was initially proposed by Utterback and Abernathy (1975) and Figure 2.4 represents it as now accepted (Taylor and Taylor 2012). The emergence phase is called “fluid” and is characterized by a high and decreasing rate of product innovation (Utterback and Abernathy 1975). This first phase ends with the emergence of a dominant design that constitutes a relatively stable set of performance criteria for the product in question (Anderson and Tushman 1990). It should be noted that the dominant design will only appear if the technological variants of the product are in competition. This allows a selection of variants and leads to observing the emergence of a “consensus” on the performance criteria that will prevail (Anderson and Tushman 1990). After the emergence of the dominant design, the rate of product innovation will continue to decline to a near-zero level that will be observed throughout the last phase of the cycle.

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49

Rate of product innovation

Dominant design

Fluid

Transition

Specific

Time Figure 2.4. Innovation lifecycle. For a color version of the figures in this chapter, see www.iste.co.uk/dossantos/innovation.zip

2.1.3. Method: sources and measurements 2.1.3.1. Sources To demonstrate that the space industry has been in an emerging phase since 1957, we use secondary sources. On the one hand, we use reports and publications describing space activities from political, historical, economic, and strategic perspectives (e.g. Space Policy magazine, reports from OECD, NASA, the European Space Agency, ASDEurospace, etc.); on the other hand, we rely on open archive data. Forbes and Kirsch (2011) consider that the adoption of a longitudinal method makes it possible to overcome some of the difficulties associated with the study of the emergence of a new industry. The choice to use archival data appears relevant insofar as they have “a broader scope than the data associated with any other study. In addition, archival data

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Innovation Trends in the Space Industry

are more easily accessible than the proprietary databases usually used in organizational research” (Forbes and Kirsch 2011, p. 596). The archival data used taken from websites that present the spacecraft launches that have taken place since 1957. In particular, we used the data posted online by NASA, Claude Lafleur, Jonathan McDowell, Gunter Krebs, and Encyclopedia Astronautica. Where necessary, we also consulted sites that describe the nature of the actors in the space industry in more detail. In particular, we have used the information available on the following websites: Global Security and Federation of American Scientists. By merging the content collected in these different websites, we have built an original database that includes all spacecraft launched between 1957 and 2011. For each spacecraft, we have the launch date, the type of customer (military, commercial, etc.), and the field of application (telecommunications, earth observation, etc.). Thanks to our database, we are able to trace the curves of the lifecycle of the product, innovation, and adoption. 2.1.3.2. Measurement of variables To measure the sales volumes required to draw the product lifecycle, we will use spacecraft launch volumes. We recognize that the sale of a spacecraft is not a single transaction. For example, the first payment may be made when the spacecraft is designed, while the last payment may be made years after the customer has started using the spacecraft. Despite this, because the launch is a key moment in the different stages of the sale, it is, in our opinion, a good indicator of the sale. To measure the product innovations required to draw the innovation lifecycle, we will use the launch volumes of spacecraft in the Research and Development (R&D) and

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Scientific Research categories1. Scientific spacecraft involves the development of new technologies that will be used to accumulate fundamental knowledge about the space environment. R&D spacecraft involves the development of new technologies that will be tested in orbit before being added to the producer’s catalog. The other categories of spacecraft do not carry technologies that would not previously have been tested and validated in orbit using scientific and R&D spacecraft. As a result, these other categories cannot be considered as product innovations. Authors studying the entry of adopters into a market suggest examining the date of entry of customers and their inclination to purchase innovative products to determine to which category of adopters they belong (Goldenberg et al. 2002; Libai et al. 2008; Rogers 2003). According to this method, early adopters will be those customers who both enter the market first and have a high inclination to buy innovations. Conversely, mainstream adopters will be those customers who both enter later and have a low inclination to buy innovations. In the space industry, there are four main categories of customers: military customers (i.e. Ministries of Defense), civilian institutional customers (i.e. space agencies), commercial customers (i.e. satellite operators), and mixed customers (i.e. public—private partnerships and militarycivilian customers existing in communist countries). To measure the date of customer entry, we first identified the date on which each organization purchased its first spacecraft. Since the adoption of spacecraft is impacted by each country’s national innovation system, we then calculated the average entry date for each of the four customer categories. Using the average entry date as an indicator avoids, for example, the error of stating that the first Chinese customer is a mainstream adopter because it                                         1 We give a detailed presentation of these two categories of spacecraft in Chapter 1.

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purchased its first spacecraft after the first American customer belonging to the mainstream adopters. The first Chinese customer must be considered an early adopter because it is part of a national innovation system different from the American system. To measure the inclination of customers to buy innovative products, we reused the indicator developed to measure the rate of product innovation. A category of customers will have a strong inclination to buy innovative products if the share of R&D and scientific spacecraft is high in the total number of spacecraft they buy. By using these two indicators, we establish the link between the four categories of spacecraft customers and two categories of adopters that exist in the literature. We present our results in Table 2.1. Average entry date

Rate of R&D and scientific spacecraft in total purchases

Category of adopters

Civilian institutional customers (e.g. space agencies)

1995

64%

Early adopters

Military customers (e.g. Ministries of Defense)

1991

13%

Early adopters

Mixed customers

1990

37%

Early adopters

Commercial customers (e.g. satellite operators)

1999

4%

Mainstream adopters

Customer category

Table 2.1. Customers and adopters

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2.1.4. Results It can be seen in Figure 2.5 that the curve describing spacecraft launches between 1957 and 2011 is very different from the typical lifecycle curve of the product, which shows a “bell” shape.

Figure 2.5. Evolution of spacecraft launches. For a color version of the figures in this chapter, see www.iste.co.uk/dossantos/innovation.zip

A first possible interpretation would be to consider that the launch curve does not have an emergence phase but starts immediately with a growth phase that stops in 1965, since it is at this time that the peak of launches is observed. This peak would start the maturity phase that has an end very difficult to determine. The absence of an emergency phase opens the possibility of a second interpretation in which the launch curve describes a long emergence phase that is not yet complete. This second interpretation is confirmed by the other indicators. Figure 2.6. shows the evolution of the product innovation rate measured by the number of R&D and scientific spacecraft in total launches. It should be noted that the first few years describe an evolution close to the typical curve of

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the innovation lifecycle. However, significant differences appear over time. Instead of a long period characterized by decreasing and tending toward zero innovation rates, there is only a minimum of 4% in 1982, followed by a period of slow growth between 1982 and 1993, and after by a period of stronger growth until 2011.

Figure 2.6. Rate of product innovation in the space industry. For a color version of the figures in this chapter, see www.iste.co.uk/dossantos/ innovation.zip

We consider that the periods observed in Figure 2.6 describe the evolution of the innovation lifecycle in the emergence phase; on the one hand, because the product innovation rate remains permanently high and does not describe a decreasing curve toward zero; on the other hand, because the space industry does not have a dominant design. Due to the significant weight of institutional, customers (military and civilian) in overall procurement, the space industry consists of a multitude of relatively independent

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markets in which technologies face limited competition (OECD 2011). The “national preference” logic adopted by these customers leads to a preference for domestic technologies, even if they are less efficient than foreign technologies. This situation excludes the existence of sufficient competition to allow sufficient technological variations to disappear and thus prevents the emergence of dominant design (Barbaroux and Dos Santos Paulino 2013).

Figure 2.7. Entry dynamics of adopters into the space industry. For a color version of the figures in this chapter, see www.iste.co.uk/dossantos/innovation.zip

The study of the configuration of adopters on the market confirms the existence of a long emergence phase in the space industry. Figure 2.7 shows that the majority of purchases are made by early adopters (i.e. military, civilian institutional and mixed customers). The mainstream adopters (i.e. commercial customers) were dominant for only a short period of time in the late 1990s. In other words, the configuration of adopters that prevails during the growth

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phase has not been established. Rather, it is the pattern that prevails in the emergence phase that is observed between 1957 and 2011. Our findings on the evolution of sales, product innovation rates and configuration of adopters’ categories prove that the space industry is in an emerging phase between 1957 and 2011. 2.1.5. Discussion We consider that it is not exceptional to observe an emergency phase that lasts 55 years. For example, it took 45 years for aircraft to complete their emergence phase (see Figure 2.1 with comments at the beginning of the section). This result puts us into perspective that our observations concerning launch volumes, innovation rate, and categories of adopters. If the space industry enters a growth phase 1 day: (1) launches are expected to grow strongly, (2) the rate of product innovation is expected to decline significantly, (3) and purchases by institutional, civil, and military customers are expected to be low compared to purchases by commercial customers. The possible start of the growth phase will be a major shock for industry stakeholders; however, it is impossible to state that this will ever happen. Indeed, many industries disappear before entering the growth phase (Forbes and Kirsch 2011). The end of the emergence phase will also make it possible to observe more competition between producers. The persistence of the space industry in the emergence phase for several decades leads to the legitimization of the emergence phase as an object of study in the analysis of the industry’s lifecycle. Indeed, five decades is a sufficiently long period on the scale of an industry. Our results also validate the proposed method and indicators to identify if an industry is in the emergence

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phase. This method is based on the study of threedimensional characteristic of the industry: the sales, the configuration of adopter categories, and the rate of innovation. The situation of the space industry in 2011 leads us to consider that the emergence phase will continue for a few more years. Our results also imply that everything that has been said about this industry between 1957 and 2011 helps to characterize its emergence. 2.2. Customers shape the industry dynamics in the emergence phase We have shown in the previous section that the space industry has been in the emergence phase between 1957 and 2011. This result leads us to ask ourselves several questions: What are the forces that structure the emergence phase? Is the emergence phase itself composed of subphases? We will answer these questions in this section. 2.2.1. Theoretical framework Industry lifecycle literature usually focuses on the producers, referred to as “entrants,” to study the industry emergence (Di Stefano et al. 2012; Forbes and Kirsch 2011; Saviotti and Pyka 2013). This literature studies the characteristic of entrants such as the sector they come from, the technologies they use, their level of concentration and competition, and the entry and exit rates (Barbaroux and Dos Santos Paulino 2013). Incoming entrants “are typically small companies with experience in related technologies. They may be users of the new product, or they may be companies resulting from the spin-off of an existing company. They often introduce discontinuous product innovations, based on their knowledge

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of user needs and/or the technological means available to meet them. This period is thus characterized by a strong technological variation (Anderson and Tushman 1990; Olleros 1986). During the emergence phase, market shares change rapidly, while successful innovators replace their less efficient rivals” (Klepper and Graddy 1990, p. 35). The chances of survival of entrants are low because “they must learn new roles without models and establish links with an environment that does not understand them or acknowledge their existence” (Aldrich and Fiol 1994, p. 648). The growth of firms is relatively low, and their number tends to increase gradually (despite a relatively low survival rate). Audrescht (1991, p. 444) also considers that the emergence phase of the industry is often dominated by an “entrepreneurial” type technological regime in which technological opportunities are numerous, industrial concentration is low (Breschi et al. 2000), and the entry rate of new innovative firms is high (Audrescht and Feldman 1996). Despite these major contributions to the understanding of industrial dynamics, industry lifecycle literature recognizes a marginal influence of demand on the emergence of industries. This is problematic in understanding the emergence of industries in which public customers play a decisive role in promoting and supporting development (Spencer et al. 2005). This is — or has been — the case in many industries, such as aeronautics, computer science, nuclear and, of course, space (Malik 2017; Mowery and Rosenberg 1989; Scranton 2006). Military and civilian institutional customers finance basic and applied research. By being early adopters, they also assume the risk associated with the implementation of technologies (Dos Santos Paulino 2011). Without these customers, many industries would have remained confined to simple research activities for longer or would never have been created. Spencer et al. (2005) also show that a country’s political and institutional configuration directly influences the process of emergence of new industries in that country. The influence

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of public customers on industry emergence may, therefore, be different between countries. Recognizing customers as the main unit of analysis provides a central place to the notion of need. During the emergence phase that characterizes the space industry, customers do not express a specific need for a particular product (Klepper 1996; Moore 2002; Rogers 2003). The absence of a dominant design prevents the identification of clear and comparable performance criteria between available technologies. Even if the term “need” is sometimes used, the studies on the space activities prefer terms such as “objective” and “reason” (Gilks 1997; Kay 1998; Launius 2006; McDougall 1985; OECD 2014). In addition to explaining customer purchases, objectives aid understanding of the sources of industry legitimacy (Aldrich and Fiol 1994). For example, the defense objective pursued by military customers when they purchase a military satellite tells us about the sources of legitimacy in the space industry. This reasoning implies that an industry that persists in the emergence phase has insufficient legitimacy. Aldrich and Fiol (1994) added that not all sources of legitimacy are equal. They insist on the powerful role of those who provide access to the resources necessary for survival. In a note sent to the President of the United States in 1961, the Director of NASA and the US Secretary of Defense stipulated the four objectives that space projects must pursue: “The first was scientific knowledge, the second was commercial/civil value, and the third was military missions. The final reason was for purposes of national prestige” (Day 1996). Although sometimes referred to differently, these four objectives are accepted in several studies analyzing, at different times, the causes of purchases made by institutional, military, and civilian customers (Kay 1998; Launius 2006; McDougall 1985; OECD 2007; van Dyke 1964; Williamson 1985). In order to better reflect the objective pursued by commercial customers, the “commercial/civil

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value” objective should be split. On the one hand, we propose the “profits” objective, which explains only the purchases of commercial customers, and on the other hand, the “economic benefits” objective of space activities, which explains the purchases of civilian institutional customers. Five objectives, therefore, explain the purchasing of spacecraft by all customers: (1) military, (2) national prestige, (3) science and technology, (4) economic benefits, and (5) profits (Dos Santos Paulino and Barbaroux 2016). 2.2.2. Sources We use the same sources as in the previous section. We recall them briefly here. On the one hand, we use reports and publications describing the space sector. On the other hand, we rely on archival data from websites that provide detailed information on the spacecraft launches. Using this archival data, we have built an original database that includes all spacecraft launched between 1957 and 2011. For each spacecraft, we have the date of launch, use (military, civilian institutional, and commercial) and information describing the order (i.e. customer name, nationality, institutional nature). Due to the structure of our database, it should be noted that we are considering the entry of a new customer in the industry through the launch date of its first spacecraft. 2.2.3. Results: influence of customers on the emergence of the space industry Figure 2.8 shows the evolution of spacecraft launched between 1957 and 2011 according to their use. We retained three uses for spacecraft: military, civil institutional, and commercial.

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Figure 2.8. Evolution of the number of spacecraft launches according to their use [Source: adapted from Barbaroux and Dos Santos Paulino (2013)]. For a color version of the figures in this chapter, see www.iste.co.uk/dossantos/ innovation.zip

First, we observe that the emergence of the space sector is characterized by two main eras: the Cold War (1957—1990) and post-Cold War (1990—2011). These two eras were themselves composed of periods with different durations and configurations: the military boom (1957—1965), stabilization (1965—1990), military withdrawal (1990—2004), and balance (2004—2011). We show that military customers played a decisive role during these four periods. 2.2.3.1. The military boom: 1957–1965 The first period (1957—1965), which we call the “military boom,” was a manifestation of the Cold War (Figure 2.8). At that time, military customers played a dominant role, with 64% of spacecraft launched being for military use. Space customers were primarily American and Russian and mainly pursued military, prestige, and science and technology

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objectives (Barbaroux and Dos Santos Paulino 2013; McDougall 1982, 1985). At that time, both superpowers considered that space offered decisive military advantages to dominate the other in the Cold War. By providing new communication, observation, and pointing services, space technologies improved the effectiveness of surveillance and nuclear systems. Spatial successes were also used as propaganda tools to demonstrate the superiority of one unit over the other. For example, the success of Sputnik 1 (1957) was a kind of trauma for the Americans. This helps to explain the start of what would later be called the Space Race, which the military boom is part of. The science and technology objective aimed to acquire the fundamental and operational knowledge necessary to pursue the other objectives. During this period, a very limited number of stakeholders were involved. The Alouette (1962) and Asterix (1965) satellites allowed Canada and France to join the Russians and Americans, respectively, among the nations that have spacecraft in orbit. It was also during this period that the first civilian actors appeared: in 1958, NASA funded Pioneer 1 with the help of the US Department of Defense (DoD) and in 1962, AT&T financed the first commercial satellite and thus gave birth to the commercial civilian segment. 2.2.3.2. Stabilization: 1965–1990 From 1965 onward, there was some stabilization in the total number of launches (about 160/year) and in the configuration of the relative weight of all uses (Figure 2.8). Until the 1970s, the objectives of space remained unchanged because the socio-political factors observed in the previous period continued, i.e. Cold War, Space Race, massive budgets, propaganda, and lack of spatial knowledge. After the 1970s, there were some changes in the objectives.

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There was a decline in the national prestige and science and technology objectives that traditionally supported institutional civil launches (e.g. space agencies). There was now enough knowledge to take advantage of several essential space applications (e.g. observation, communication). In addition, space successes were less effective as propaganda tools. For example, the Apollo program ended in 1972 with relative indifference. This exhaustion led space agencies to propose the objective of economic benefits derived from space to maintain their influence on the industry (Ruttan 2006). This new objective itself supported the profit objective pursued by commercial customers, whose purchases were no longer anecdotal (Figure 2.8). The rise of economic benefits and profit objectives to support the space industry was widespread among the intermediate space powers that continued to enter and strengthen their influence. Examples include the entry of Japan (1966), Europe (1967), China (1970), and India (1975), as well as several new commercial customers, such as Hughes Communication (1983) and the European Satellite Company (1988). However, these entries and the development of new civil objectives did not compensate for the reduction in objectives traditionally assigned to institutional civil customers. Especially since at the same time, there was a strengthening of the military objective as a driving force for space activities. The so-called “Second Cold War” period, which includes the war in Afghanistan, the Euromissile Crisis and the Strategic Defense Initiative (also called “Star Wars”), is the cause of the increase in military influence on the evolution of the space industry. 2.2.3.3. Military withdrawal: 1990–2004 The third period (1990—2002), which we call “military withdrawal,” marked a radical change in the volumes and

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configurations of the relative weight of military, civil institutional, and commercial uses (Figure 2.8). With the end of the Cold War, there was a large drop in the number of spacecraft launches for military use. For the first time, military customers’ purchases were lower than civilian customers’ purchases. In the civil segment, Figure 2.8 shows that the influence of commercial customers was extremely irregular, unlike that of civilian institutional customers. This irregularity was mainly due to the inflation and bursting of the Internet Bubble in the second half of the 1990s. These developments reflected a collapse of the military objective among customers, partially offset by the increase in the objectives aiming to obtain economic benefits and profits from space activities. These last two objectives then became two additional driving forces significantly contributing to the dynamics of the space industry. The profit objective was driven, in particular, by the arrival of several new commercial customers, which often resulted from the order of a first telecommunications satellite by a new space nation. These nations were mostly developing countries, such as Pakistan (1990), Thailand (1993), Turkey (1994), Egypt (1998), Algeria (2002), or Nigeria (2003). 2.2.3.4. Balance: 2004–2011 The last period began in 2004 and continued until 2011. This period was marked by a certain “renaissance” of the space industry, following an increase in purchases by all types of customers, especially military customers (Figure 2.8). This led to a balance between the three pillars of space: military, civil institutional, and commercial activities. This new configuration was the result of an increase in purchases: (1) from American and Russian customers; (2) from intermediate space powers, such as France, Israel, and China; and (3) from developing countries [e.g. Venezuela (2008) and Vietnam (2008)]. The increase in the latter group was mainly due to the entry of new commercial customers

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and very little to the entry of civil institutional customers. Finally, this period reflected a situation where there was a relative balance between the five objectives of space: military, economic benefits, profit, science and technology and national prestige. 2.2.4. Discussion and implications 2.2.4.1. Static analysis The study of the evolution of the space industry from 1957 to 2011 confirms the essential role played by customers in the emergence of industries. We show that the emergence of a new industry did not always result from the initiative of individual entrepreneurs able to perceive and seize opportunities in an uncertain environment. In the space sector, as in other industries producing dual-use goods (military and civil), it is institutional customers that explain and shape the emergence of industries. Military customers have played a major role in the evolution of the space industry, since 54% of spacecraft between 1957 and 2011 were for military use. These customers brought about and shaped the space industry in order to pursue military objectives as well as national prestige and science and technology objectives. With 37% of spacecraft for institutional civil use, civil institutional customers had a less important role in the evolution of the industry. They nevertheless contributed to the emergence of the industry, essentially pursuing objectives of national prestige, science and technology and, to a lesser extent, economic benefits. Finally, the existence of 9% of commercial spacecraft underlined the weaker role of commercial customers pursuing a profit objective. Regardless of the country’s economic regime — communist or capitalist — the emergence of the space industry required a strong commitment from the public authorities. The cost of

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space programs needed the support of large public organizations, which obtained and coordinated very important resources (e.g. ministries of defense, space agencies). The significant resources required implies that the sectoral innovation system of capitalist countries has many similarities with that of communist countries (Barbaroux and Dos Santos Paulino 2013; Laperche and Uzunidis 2007). Despite these common points, we observe the existence of three models of support for the emergence of the space industry. The first model is one proposed by the two superpowers, who financed 76% of spacecraft between 1957 and 2011. Here, military customers played a major role. On the one hand, they strongly supported the domestic industry with their massive and regular orders; on the other hand, military customers also supported civil space programs. For example, it was military customers who sponsored the first scientific and R&D satellites (Sputnik 1 in 1957 and Sputnik 2 with Laika the dog onboard). This situation was more pronounced in Russia than in the United States, where it was sometimes difficult to distinguish between civilian and military organizations. This is the case, for example, of OKB-1, led by the father of Russian space activities, Sergei Korolev. The second model we identify is that of intermediate space powers that have launched between 20 and 200 spacecraft. Here, we find nations such as France, China, Japan, India, and Germany. In this model, industry support is primarily provided by civilian institutional customers, such as space agencies (e.g. European Space Agency). The financing of spacecraft by the military takes place in a second phase and spills over very little into civilian use. For example, the first French spacecraft was launched in 1965 and was for civilian use. It was then not until 1995 that the French Direction Générale de l’Armement (DGA) sponsored its first spacecraft.

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The third model of support we identify is implemented by small space nations (less than 15 spacecraft). These nations are generally developing countries, including Brazil, Thailand, and Turkey, among others. In these countries, the first spacecraft are commercial satellites sponsored by customers who are private firms, or who are destined to become so (e.g. Embratel, Türksat, Shin Satellite). These customers, generally supported by political authorities, stimulate the emergence of a domestic space industry, thanks to technology transfers from producers. In this third support model, military influence is very weak. 2.2.4.2. Dynamic analysis Thanks to the case of the space industry, we show that the emergence phase itself is composed of several long periods. Unlike what is assumed by the “bell” shaped curve of the product lifecycle, we observed that the emergence phase is not a homogeneous period in which sales increase slowly and steadily. During the emergence phase, there may be periods of growth, stability, and decline. More specifically, we observed the existence of periods dominated by military customers and then periods of military withdrawal with a strengthening of commercial customers. This result seems to be generalizable to all industries producing dual-use goods, such as electronics and aeronautics (Kechidi and Talbot 2013; Mowery and Rosenberg 1989). However, we do not know whether the withdrawal of military customers to commercial customers is specific to the emergence phases of these industries or whether it is explained by the end of the Cold War. For example, in the related field of public-funded R&D, Laperche (2001) noticed, at the end of the 1980s, disengagement of the government in favor of private stakeholders. According to her, this disengagement is partly explained by the easing of international military tensions.

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During the first period, known as the military boom period (1957—1965), space activities derived their legitimacy from the fact that they were considered by each of the superpowers as a privileged means of achieving their military, national prestige, and science and technology objectives. In particular, there was a very rapid increase in the number of launches, with a peak of 180 launches in 1965 (see Figure 2.8). At the time, the USSR and the United States believed that space was a means of gaining influence over the enemy in the Cold War period. In 1965, a new period began in which, on the one hand, there was stability in global launches, and therefore in global legitimacy, and, on the other hand, there was an evolution in objectives that reflected a change in the industry’s sources of legitimacy. From the mid-1970s to the end of the Apollo program, there was a weakening of the legitimacy of civil space activities. This change was best observed in the United States. The end of the Apollo program with relative indifference reflected a change in the link between space activities and national prestige. From then on, civilian institutional space programs needed to justify their usefulness in terms of economic benefits. This new constraint was further reinforced by the fact that agencies such as NASA are now competing for funding with other civil government organizations (Logsdon et al. 1998). However, these changes in the objectives pursued by existing civilian institutional customers are not sufficient to compensate for the legitimacy lost by civil space activities. The situation is different with regard to the legitimacy of military space activities. It tends to grow and thus make the military objective the only solid pillar to support the emergence of the industry.

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From 1990 onward, the end of the Cold War led to a massive withdrawal of military customers because the military objective lost its importance. It was said at the time that nations wanted to receive the “peace dividend.” This led to a large reduction of the industry’s legitimacy and jeopardized its survival since less than 80 spacecraft were launched in 2004. At that point, we were very far from the peak of 180 launches reached in 1965. Forbes and Kirsch (2011) emphasize that industries can disappear before they enter the growth phase. What we see in the space industry at that moment sheds light on this phenomenon. An industry can disappear if the objectives that explain customers’ purchases are no longer necessary. The risk of the space industry disappearing will be mitigated by the evolution of civil customers objectives. First, there is a continued increase in purchases by civilian institutional customers from medium-sized space powers and developing countries. These customers will continue to strengthen the objective of economic benefits and also revitalize the objective of national prestige and science and technology. The socio-political context and knowledge stock of developing countries are indeed very different from those of superpowers. The increase in the profit objective, related to the increase in purchases by commercial customers, will also help to prevent the industry from disappearing. Unfortunately for the development of the industry, the legitimacy conferred by the profit objective remains fragile in the 1990s. The very sharp drop in the number of commercial launches after the bursting of the Internet Bubble is evidence from this (Figure 2.8). From 2004 onward, the increase in launches underlines a new period of growth in global legitimacy for the industry. For the first time in its evolution, its five sources of legitimacy appear to be

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relatively balanced: military, economic benefits, profits, science and technology and national prestige. 2.3. Demand influences technological change Several authors have shown that the dynamics of technological change have a significant impact on the evolution of industries (Cainarca et al. 1992; Klepper 1996; Taylor and Taylor 2012). We have decided to deepen our understanding of the emergence of the space industry by focusing on the evolution of technological change. In this section, we analyze the influence of demand on the dynamics of technological change in the space industry. We observe a tendency to lose momentum in these dynamics by studying: technology transfer, technology adoption, patenting, and innovation. We show that this evolution is explained by the variation in the weight given by demand to the science and technology objective. Over time, the weight of this objective is impacted by: (1) the desire of customers to diversify space applications, (2) the decline in military budgets, (3) the evolution of opportunities in the industry, and (4) customer expectations in terms of reliability. These results suggest a strong link between product reliability, the slowdown of innovation, and the survival of the space industry (Dos Santos Paulino 2007, 2011, 2014b; Dos Santos Paulino and Callois 2009, 2010). 2.3.1. Sources, data and indicators In order to demonstrate the existence of a slowdown in the dynamics of technological change in the space industry, we use three categories of sources. We first exploit reports and publications describing space activities from a technological, economic, and strategic perspective (e.g. NASA reports, academic publications). We then use the original

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database that we have built with archival data again. This database has already been used in the two previous sections. Finally, we decided to measure the number of patents filed in the space sector to estimate the industry’s ability to propose technological innovation. Below we detail the methodology that is used to extract patent data. 2.3.1.1. Methodology for extracting patent data From a managerial point of view, a patent is defined as “a legal title that confers to its holder the exclusive right to exploit the invention for a given period of time. The granting of protection involves long and costly procedures for analyzing the conformity of the invention with the legal conditions necessary to obtain it: being new, inventive, likely to have industrial application and to bring about a concrete industrial result” (Mitkova 2005, p. 60). For a more legal definition of this intellectual property right, we can refer to the work of Azéma and Galloux (2017). Our work on patents is based, on the one hand, on the assumption that patenting activity results from a deliberate behavior aimed at creating technological innovation (GomesCasseres et al. 2006; Griliches 1984; Pénin 2003) and, on the other hand, on the assumption that the patent portfolio is an imperfect indicator of the knowledge produced, while being an acceptable approximation (Ayerbe et al. 2012; Griliches 1990). To extract the number of patents filed by the space industry, we used the method proposed by the INPI (Institut National de la Propriété Industrielle). According to a report dated May 2005, it is possible to match industries with the technology classes assigned to patents. This correspondence makes it possible to make a sectoral distinction based on the knowledge produced and not on the participants or the nature of the products sold. We have thus extracted the patents associated with the B64G technology class entitled

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“Astronautics, vehicles or equipment for this purpose” which appears to be the closest to the space sector2. Patents were extracted from the database provided by the European Patent Office (EPO). This database includes patents filed with European offices but also those applied for via the so-called “PCT” procedure (Patent Cooperation Treaty) dating from 1978. This procedure aims to simplify the application for the applicant who applies only to one office while obtaining a patent valid in all the signatory countries of the Treaty (120 at the time of this study). The database of the EPO thus ensures a strong international dimension to the data collected. The period that interests us is 1957; however, it is impossible to have patent statistics for this period because the data were not usable until 19803. Due to this constraint, we have chosen the period from 1980 to 2004. 2.3.2. Loss of impetus resulting in technical change The space industry is one of the high-tech industries and, as such, it uses the most advanced technologies of its time. The achievements of the space industry are its examples. Examples include the development of launchers that provide access to space, the launch of space probes that contribute to better compression of the celestial environment (i.e. space,                                         2 In choosing this class, we have adopted a restrictive approach to the space domain. We are aware that space organizations have a wide portfolio of skills and can file patents in other classes. For example, they are often also involved in defence or aeronautics. However, we made this choice in order not to extract patents that would have no connection with the space domain. 3 It was only from 1976 and the Munich Convention that patent filing procedures were harmonized in Europe. Previously, it was necessary to contact each national office to obtain protection in the desired countries. In addition, it is recognized that it took a few years for applicants to actually make use of this new harmonized system.

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planets) and the development of increasingly powerful satellites that provide telecommunications, meteorological, and navigation services. Despite this flattering situation, we are seeing a certain slowdown in the dynamics of technological change in the space industry. We observe this decline in four dimensions: technology transfer, technology adoption, patenting, and innovation. 2.3.2.1. Loss of impetus in technology transfer With the emergence of the “economic benefits” objective in the mid-1970s, the space industry sought to transfer the technologies it had developed for its own needs to various sectors of activity, such as health (e.g. medical imaging, prostheses), civil security (e.g. fireproof clothing for firefighters), transport (e.g. aerodynamics, insulation), energy (e.g. photovoltaic cells), or meteorology (e.g. satellite images) (NASA 2009a, 2009b). In the United States, these technology transfers, sometimes referred to as “spin-offs,” have mainly benefited the aeronautics, military, electronics, robotics, and telecom sectors. At the European level, the work of Bach et al. (1991) mentions that it is the aeronautics and military fields that have benefited most from the technological spin-offs of the programs founded by the European Space Agency. Several studies point out that technology transfers by the space industry have tended to decrease since the mid-1980s (Bach et al. 1991; Bach et al. 2002; Cowan and Foray 1995; Dos Santos Paulino and Callois 2009, 2010). The work of Bach et al. (2002) indicates, in particular, a decrease in indirect effects outside the space industry, for programs financed by ESA, for the period 1977—1991, compared to the period 1962—1984. Indirect effects measure the impact4 of a                                         4 The impact measured concerns effects on technologies, effects on organization and methods, effects on labor productivity, and commercial effects.

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space program on the economy as a whole after removing the effects on the space industry. 2.3.2.2. Slower adoption of innovations Spacecraft are largely dependent on electronic components to carry out their missions. Based on the work of Mowery and Rosenberg (1989, pp. 123—160) and Cowan and Foray (1995), we can distinguish three phases in the adoption of new semiconductors by the space industry. The first phase runs from 19475 to the early 1970s. The American government accounted for 100% of the demand for integrated circuits between 1947 and 1962; this weight dropped to 72% in 1965 and then to 37% in 1968 (Mowery and Rosenberg 1989, p. 147). This demand was largely dominated by the Ministry of Defense and NASA. For example, these two institutions accounted for more than 80% of government R&D funds between 1953 and 1965 (Mowery and Rosenberg 1989). Although the relative weight of institutional demand decreased during this period, it still remains an important market opportunity for semiconductor producers. The military—spatial demand for semiconductors was sufficient for semiconductor suppliers to meet its specific requirements in terms of reliability and robustness. For example, semiconductors needed to remain operational when exposed to high-temperature amplitudes and high radiation. In other words, during this first phase, military and space customers were able to adopt the most technologically advanced semiconductors (Dos Santos Paulino 2011; Dos Santos Paulino and Callois 2009, 2010). The second phase ran from the early 1970s to 1994 and characterized by a much lower weight of military and space demand in the overall demand for semiconductors. For example, in the 1990s, this demand stabilized at around 10% (Alic et al. 1992, p. 260; Foray 1990, p. 107). With the                                         5 Date of the invention of the transistor.

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development of the use of semiconductors in other industries (e.g. IT, automotive, etc.), it has become impossible for military and space demand to require that all semiconductors meet high requirements in terms of reliability and robustness. This leads to the emergence of two categories of semiconductors. On the one hand, we have semiconductors used by the military and space industries, known as “high-reliability electronic components.” These components are very expensive, very reliable, and very robust, but they have the disadvantage of not being technologically advanced because they do not follow Moore’s law6. On the other hand, we have semiconductors used by other industries, known as “commercial electronic components.” These components are inexpensive, moderately reliable, and not very robust, but they are the most technologically advanced because they follow Moore’s law. For example, according to the CNES (Centre National d’Études Spatiales) (2003, p. 5), commercial components do not support spatial radiation and tolerate temperature amplitudes within the range [0°C; +70°C], while highreliability components resist radiation and temperatures within the range [ 55°C; +125°C]. In terms of price, the high-reliability components of the MIL range are on average three times more expensive than their commercial counterparts. The SCC-B range is up to 615 times more expensive than its commercial equivalents (CNES 2003, pp. 4—5; Dos Santos Paulino 2007, p. 273). As military and space customers prefer high-reliability components to commercial component determinants, they no longer adopt the most technologically advanced semiconductors at that time. More specifically, the computing power gap between the two categories of semiconductors has been growing and, in the mid-1990s, military and space customers were adopting                                         6 The current wording of this law is: the number of transistors existing on a silicon microprocessor doubles every 2 years. The one who proposed this law is none other than the co-founder of Intel, Gordon Moore (1965).

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obsolete semiconductors compared to those used in everyday electronics products (Dos Santos Paulino 2011; Dos Santos Paulino and Callois 2009, 2010). The third phase started when the power and price gap became a problem. In the mid-1990s, US Secretary of Defense William Perry argued that the existing adoption strategy should be reversed, and more commercial components should be adopted (Chinworth 2001). The advantage of this change was to be able to win in the areas of price and technological performance while accepting a negligible decrease in reliability and robustness. Unfortunately, in the space sector, the failure of several satellites quickly led to a reduction in the extent of the use of commercial components. For example, in the mid-2000s, in civilian and military programs supervised by CNES, the use of these components did not exceed 20%, while highreliability components accounted for 80% (Dos Santos Paulino 2007). In general, the use of commercial components has only become the majority in low-budget space missions aimed at validating very specific new technologies (Jiang et al. 2006). In other words, in the mid-2000s, the space sector continued to use mostly obsolete semiconductors, although the situation was less problematic than in the mid1990s (Dos Santos Paulino 2011; Dos Santos Paulino and Callois 2009, 2010). 2.3.2.3. Patents Figure 2.9 shows the evolution of the number of patents filed in the technological class “Astronautics, vehicles, or equipment for this purpose” which appears to be the closest to the technologies produced by the space industry. We observe that the space industry has tended to increase the number of patents filed each year, from 10 patents registered in 1980 to 57 in 2004. However, this increase was slowing down when we look at the growth rate of the number of patents registered before and after 1990. During the 1980s

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and until the end of the Cold War, the average annual growth rate was 28%. This rate dropped to 5% between 1990 and 2004. The existence of a period of sustained growth, followed by a period of more modest growth, leads us to consider that there has been a decline in the space industry’s ability to file patents since the end of the Cold War.

19 80 19 82 19 84 19 86 19 88 19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04

100 90 80 70 60 50 40 30 20 10 0

Figure 2.9. Patents filed in space technologies [Source: Dos Santos Paulino (2011)]

2.3.2.4. The development of innovations Figure 2.10 shows the evolution of the innovation rate, which is measured by the share of R&D and scientific spacecraft in the total spacecraft launched. There is a first period, between 1957 and 1982, characterized by a decrease in the innovation rate with the lowest rate at 4%. A second period, between 1982 and 1993, is then observed, marked by slow growth of the innovation rate in total launches. Finally, there was a third phase characterized by stronger growth

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that gradually accelerated until 2011, but never reached the levels observed before 1960. In other words, although we observe an inverted “J” curve, we can observe a general slowdown in innovation between 1957 and 2011.

Figure 2.10. Rate of innovation in launched spacecraft [Source: Dos Santos Paulino (2014b)]. For a color version of the figures in this chapter, see www.iste.co.uk/dossantos/innovation.zip

2.3.3. Influence of demand on technological change In section 2.2, we showed that the evolution of the space industry between 1957 and 2011 was shaped by the evolution of demand. We will use this result to show how demand influences technological change in the four dimensions studied: technology transfer, technology adoption, patenting and innovation. The influence of demand on technological change leads us to distinguish three periods: the emergence of operational missions and the need for reliability (1957—1970), the start of

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space commercialization and the strengthening of the need for reliability (1970—1993), and new opportunities (1993— 2011). 2.3.3.1. Emergence of operational missions and the need for reliability: 1957–1970 When it emerged, the space industry only manufactured R&D and scientific spacecraft; however, the sharp increase in launch volumes was accompanied by a diversification of spacecraft applications. For example, in 1959, the DARPA (American Defense Advanced Research Project Agency) funded the first spy satellite and created the Earth Observation application domain. In 1960, the Russian and American military funded the first spacecraft, respectively, in the fields of manned vehicle and navigation applications. Finally, in the same year, DARPA funded the first telecommunications satellite. This diversification process, desired by military and civilian institutional customers, contributed to a decrease in the relative weight of R&D and scientific spacecraft in the total number of spacecraft launched (see Figure 2.10). This diversification was accompanied by a desire among military and civilian institutional customers to use spacecraft in operational missions (Dos Santos Paulino 2011). Customers were no longer satisfied with the first that demonstrated the feasibility of using certain technologies in space, as is the case with R&D and scientific applications. They wanted spacecraft to be reliable enough to be used in operational missions. For example, they wanted observation satellites to conduct real spying missions, navigation satellites to ensure the precise location of warships, or communication satellites to make telephone calls between continents without interruption of service. The desire to use spacecraft for operational missions placed the criterion of technical reliability at the heart of technological change. This was an essential condition for

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being able to conduct operational missions in a very uncertain environment. Indeed, spacecraft have the particularity of being used in space, which is a very different environment from the terrestrial environment. This difference generates a significant lack of knowledge that leads to very frequent technical problems. For example, among the 218 scientific space probes launched since the beginning of planetary exploration and which completed their mission in 2008, there was a failure rate of 45%7, due to technical problems. No product with an operational purpose can have such a high failure rate. In essence, it is very difficult to guarantee that there will be no mistakes in an innovation process. To innovate, organizations have no choice but to engage in an iterative process (Nelson and Winter 1982). The operational needs of customers will weaken the dynamics of technological innovation. In order to offer technologies with an acceptable failure rate, reliability will gradually become a major selection criterion in the technical changes envisaged. Technologies developed by the space industry, as well as those adopted in other industries, are concerned. It was at that time, for example, that institutional customers demanded that spacecraft be manufactured with extremely robust semiconductors that must withstand very hightemperature amplitudes and high radiation. The decline in the rate of innovation observed up to 1960 in Figure 2.10 is, in our opinion, also explained by this phenomenon. 2.3.3.2. Commercialization of space and strengthening the need for reliability: 1970–1993 During this phase, we saw a further slowdown in the dynamics of technological change in the space industry. We observed: (1) a further decline in the rate of innovation (Figure 2.10), (2) difficulty for the space industry to adopt                                         7 These figures have been calculated from Williams (2008) “Chronology of Lunar and Planetary Exploration.”

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the most technologically advanced semiconductors, and (3) a certain slowdown in technology transfer between space and other activities. These changes were primarily due to the influence of military institutional customers. There was a weakening of the science and technology objective among these customers as there was now enough knowledge to benefit from several essential space applications (e.g. communication, observation). The increased need for reliability imposed by commercial customers also helped to explain the dynamics of technological change (Dos Santos Paulino 2011; Dos Santos Paulino and Callois 2009, 2010). At that time, there was an increase in purchases by commercial customers (see Figure 2.8, section 2.2, Chapter 2). This increase can be explained by the fact that these customers saw commercial opportunities to lease satellite communications capacity (Dos Santos Paulino 2011). As commercial customers pursued a profit objective, they imposed new constraints that required greater reliability (Dos Santos Paulino 2007; Sennequier 2000). For example, commercial customers required that the risk of failure be transferred to spacecraft producers. This implied the refusal to fund alternative satellites and the implementation of payments throughout the lifetime of the satellite (about 10 years). Commercial customers also avoided launch risk by requiring that ownership be transferred when the satellite was in orbit. The pursuit of the profit objective also led commercial customers to put all producers in competition and to retain the offer with the best technical and economic performance. Unlike institutional markets, producers were no longer on captive markets in which national customers bought primarily from domestic producers. Producers were adapting to all these changes by reinforcing the importance of reliability in their technological choices.

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The development of commercial demand-led civilian institutional customers (i.e. space agencies) to help producers increase the reliability of their products. This assistance enabled civilian institutional customers to maintain their legitimacy between 1970 and 1993 and corresponds to an increase in the economic benefits objective (Krige et al. 2000; Ruttan 2006). Indeed, the space agencies realized at that time that the development of commercial uses of satellites had significant economic repercussions for the space industry but also, more broadly, on national GDP (e.g. broadcasting of television programs, telephony). Space agencies decided that a significant portion of their budgets should be used to finance the development of technologies that were sufficiently reliable to meet the reliability expectations of commercial customers. These so-called “reliability” programs were used, for example, to give flight hours to new technologies so that commercial customers will accept them (Michot 2004). This led to a decrease in purchases in scientific spacecraft. In the end, the strengthening of the reliability criteria imposed by customers led to the weakening of the trial and error processes. Since iteration was essential to achieve innovations (Nelson and Winter 1982), we can explain the weakening of the dynamics of technological change observed during this period. The strengthening of the reliability criterion also led to the marginalization of the space industry. Suppliers from other industries were more reluctant to work with an industry that had both disproportionate reliability requirements and low volumes. It was during this period, for example, that the mainstream semiconductor producers refused to adapt to the requirements of the space industry. This forced producers to use high-reliability semiconductors that were less technologically advanced.

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2.3.3.3. New opportunities: 1993–2011 From the mid-1990s onward, contradictory developments in the dynamics of technological change could be seen. On the one hand, there was both an increase in the rate of innovation (Figure 2.10) and a desire to adopt more up-todate technologies (i.e. NASA’s new procurement policy); on the other hand, there was a decline in the number of patents filed until 2004 (Figure 2.9). The end of the Cold War created very strong pressure on the budgets of military customers. This fueled the weakening of the dynamics of technical change observed in the previous phase. However, from the mid-2000s onward, there was some increase in the volume of purchases made by military customers. This contributed to the recovery of the science and technology objective and the revitalization of the dynamics of change (Figure 2.8). As far as civilian customers were concerned, on the contrary, the end of the Cold War brought with it opportunities that have a positive effect on the dynamics of technological change. There was an increase in the volume of purchases made by commercial customers, which encouraged civilian institutional customers to provide more support to their domestic industry. By helping domestic producers to win markets, civilian institutional customers strengthened their own legitimacy. Civilian institutional customers increased the weight of their purchases in R&D equipment in order to help producers improve their supply. They also sought to reduce the reliability burden on producers, so that spacecraft carried fewer obsolete technologies. In addition, civilian institutional customers of medium space powers increased the weight of their purchases in scientific equipment. In the end, by giving more weight to the science and technology objective, civilian institutional customers also contributed to revitalizing the dynamics of change.

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2.3.4. Discussion and conclusion In this section, we found that there was a tendency for the dynamics of technological change in the space industry to slow down. We have observed this evolution through four dimensions: technology transfer, technology adoption, patenting, and innovation development. Our observations are consistent with those of other authors (Brunier 2006; Culp 2008; Potteck 1999; Ruttan 2006). Their work shows, as ours do, that the dynamics of technological change in the industry have tended to slow over time. The first contribution of this study lies in the fact that this dynamic is explained by the behavior of demand. We first showed that the evolution of the science and technology objective offered the first level of analysis. The influence of this objective has tended to be reduced in the purchases of military and civilian institutional customers. In particular, the purchase of scientific spacecraft was the most affected. We then deepened our understanding of the loss of impetus in technological change by studying the evolution of customer expectations more closely. We have thus highlighted factors such as the desire of customers to diversify space applications, the decline in military budgets, the evolution of opportunities in industry and expectations in terms of reliability. It thus appears that, in emerging industries, demand can shape technological change more than producers. This result shows that it may be relevant to question the central role that has been given to producers in most of the work that studies the emergence of industries (Di Stefano et al. 2012; Forbes and Kirsch 2011; Saviotti and Pyka 2013). The establishment of a link between reliability, the dynamics of technological change, and the survival of the industry appears, in our opinion, to be the second contribution of this study. Since the work on the innovation lifecycle initiated by Utterback and Abernathy (1975), it has

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been recognized that slowing down the dynamics of technological change is necessary to allow industries to grow and to survive (Taylor and Taylor 2012). Our study shows that increased reliability plays a central role in this mechanism. Improving reliability simultaneously contributes to slowing down the dynamics of technological change and to the survival of emerging industries. In order to take advantage of technological innovations in operational and commercial uses, customers are demanding increased reliability. On the one hand, increasing reliability contributes to the development of technology opportunities and, on the other hand, it helps to limit the iterative processes required to achieve innovations. The link between reliability, innovation, and survival has already been studied in some work on organizational change. Examples include the work initiated by Hannan and Freeman (1984) on population ecology and by Roberts (1990) on High Reliability Organizations (HROs). These authors show that the survival of organizations operating in highrisk environments depends on achieving and maintaining high levels of reliability. They then add that it is the rigorous reproduction of routines and rules that ensures high levels of reliability. Our work complements and reinforces this work by adopting a meso approach and focusing on technological change. In particular, we show that the link between increased reliability, weakened innovation, and survival is not only about organizational change.

 

3 Slow Adoption of Innovations: A Key Success Factor

In the previous chapter, we observed a certain slowdown in innovation in the space industry between 1957 and 2011. This phenomenon led to the emergence of a paradox illustrated, for example, by the fact that satellites are hightech products that carry a few recent electronic components. How can this conservatism be interpreted in an innovative industry? Are the innovation strategies of satellite producers’ irrational and threatening their survival? Do space activities legitimize the existence of certain inertia in the adoption of innovations? In this chapter, we will answer these questions by systematizing the link previously observed between reliability, survival, and the slowing down of innovation. In section 3.1, we examine the adoption of technological innovations. We show that slow adoption behavior contributing to reliability is a rational choice when the cost of failure and technological uncertainty are high (Dos Santos Paulino 2014a). We identify this behavior as a technological inertia strategy based on reliability. By technological inertia strategy, we mean a slow adoption of new technologies in order to maintain product reliability. This strategy does not imply the systematic rejection of any novelty but may

Innovation Trends in the Space Industry, First Edition. Victor Dos Santos Paulino. © ISTE Ltd 2020. Published by ISTE Ltd and John Wiley & Sons, Inc.

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nevertheless lead to delays in the adoption of novelties. The identification of this strategy of inertia implies that it is wrong to claim that technological delays are systematically weaknesses for organizations (Dos Santos Paulino 2014a). The survival of organizations in an industry based on competition through reliability depends on mastering high levels of reliability (Dos Santos Paulino 2009; Dos Santos Paulino and Callois 2009). Delays can, therefore, be the manifestation of the mastery of a specific key success factor. In section 3.2, we examine the case of the adoption of organizational innovations (Dos Santos Paulino 2009). By constructing an original model, we show that the slow adoption of organizational innovations also promotes organizational survival when the environment is risky (Dos Santos Paulino 2006, 2007, 2009, 2012). We identify this behavior as an organizational inertia strategy. We show that the slow adoption of organizational innovations may be a key success factor. In a risky environment, this strategy ensures the high levels of reliability required. Therefore, the observation of organizational delays should be seen as a manifestation of the mastery of a key success factor and not as a weakness that must be reduced by accelerating organizational change. 3.1. Slow adoption of technological innovations: a key success factor 3.1.1. Introduction The space industry has had increasing difficulty in adopting the most advanced electronic components (Dos Santos Paulino and Callois 2009, 2010). It has also had difficulty adopting other categories of technological innovation, such as batteries and embedded software (Dos Santos Paulino 2006, 2007, 2011; Potteck 1999).

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These adoption difficulties lead spacecraft to present technological delays. In other words, these high-tech products can be made up of obsolete technologies compared to the technologies embedded in our everyday products, such as smartphones or laptops. The interpretation of delays in the adoption of technological innovations is subject to debate among authors studying innovation. The majority view is that delays in adoption are dangerous for the survival of organizations. The intuitive rationale behind this perspective is that the rapid adoption of new and more advanced technologies will increase the competitive advantage of early adopters and weaken that of laggards. On the contrary, other authors consider that adopting new and more advanced technologies too quickly can jeopardize the competitive advantage of adopters (Anderson and Tushman 1990; Hargadon and Douglas 2001; Musso 2009; Olleros 1986; Rosenberg 1976). These authors thus consider delays in adoption as positive situations for the survival of organizations. This second perspective is based on the fact that change is marked by creative destruction (Schumpeter 1911). The adoption of new and more advanced technologies can destroy existing competitive advantage without creating a new and better one. Admitting this possibility is recognizing, as Mokyr (1992) and Carroll and Teo (1996) do, that there is a risk associated with change. Although not the majority, this second perspective has been already confirmed in several industries: aeronautics, cement, glass, minicomputers, automobiles, light bulbs, and PVC tubes (Anderson and Tushman 1990; Hargadon and Douglas 2001; Musso 2009; Olleros 1986; Rosenberg 1976). In this section, our objective is to apply this second perspective to the space activities because they face a high level of risk. We will detail how a significant risk associated with change leads firms to favor slow adoption dynamics. This effective behavior, which we call “technological inertia

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strategy,” results from technological delays.

risk

aversion

and

leads

to

3.1.2. Inertia: a literature review 3.1.2.1. Slow technical change is not irrational Schumpeter’s work (1911) introduced the hypothesis that there is a link between successful innovations and superprofits. Later, the innovation literature adopted the idea that technological change is a source of progress (Nelson 1995) and many studies have shown that firms with slow technological change dynamics threaten their survival (Assink 2006). When situations of technological backwardness are the result of strategic errors, this work makes proposals to limit these errors. On the other hand, when delays are the result of deliberate strategies, firms are considered irrational. Mokyr (1992) also points out that firms that deliberately refuse to adopt new technologies and prefer to continue using old ones are often considered technophobic by the innovation literature. This interpretation of slow technological change enabled Meeus and Oerlemans (2000) to assert that the literature on innovation is pro-innovation biased. This bias has two dimensions: on the one hand, what refers to change is interpreted positively, while what refers to stability is interpreted negatively; on the other hand, research has focused on what refers to change and has abandoned what refers to stability (Constant 2002). Although we accept the hypothesis that the slow dynamics of technological change are often dangerous for the survival of firms, we reject the assumption that they are systematically dangerous (Dos Santos Paulino 2007, 2014a). This assumption is a simplification that prevents the paradoxical nature of innovation, captured by the central concept of creative destruction, from being taken into account.

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Although demonstrating the relevance of slow technological change is generally not the concern of the innovation literature, concepts evoking the relevance of stability are at the heart of the models. For example, the authors use the concepts of routines (Nelson and Winter 1982), retention (Anderson and Tushman 1990), and exploitation (March 1991; Levinthal and March 1993) to convince us of the link between innovation, competitive advantage, and firm survival. For example, the work initiated by March (1991) on organizational ambidextrousness has shown that firms that only conduct exploration activities will find it difficult to appropriate their innovation efforts. It is therefore relevant for firms to better balance exploration and exploitation activities, even if it means presenting slower dynamics of technological change. 3.1.2.2. Some inertia strategies Our starting point for understanding the value for firms of implementing slow adoption strategies is to recognize that change involves a risk that can threaten their survival (Carroll and Teo 1996; Dos Santos Paulino 2014a; Mokyr 1992). These strategies, which we call “inertia strategies,” are rational because they help firms to cope with the risk associated with change. This risk is observed, for example, in the fact that 62% of product innovations are failures (Tidd and Bessant 2009). The literature has identified several types of risks associated with change that legitimize the slow adoption of new technologies. The works of Rosenberg (1976), Olleros (1986), and Anderson and Tushman (1990) focus on the risk of premature obsolescence of new technologies. This risk can occur in a variety of contexts. For example, when several incompatible new technologies are available at the same time. In this case, it is risky for firms to adopt a technology as long as they do not know the technology that will become the industry standard. Indeed, technologies that are not going to prevail will disappear, or survive in a niche market,

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making them prematurely obsolete. It is also risky to adopt technologies when several of them improve very quickly without knowing which one will ultimately perform best. In this case, early adoption can lead firms to align themselves with a technology that has a lower potential. According to the authors, the risk of obsolescence will lead firms to adopt a wait-and-see strategy with regard to new technologies. This strategy, which we consider to be a type of inertia strategy, has been observed in various industries, such as aeronautics, cement, glass, minicomputers, and automobiles (Anderson and Tushman 1990; Olleros 1986; Rosenberg 1976; Tidd and Bessant 2009). Other authors are interested in the risk of high transfer costs when firms adopt new technology (Hargadon and Douglas 2001; Musso 2009). This risk can be explained by the lack of regulations that ensure that the performance potential of the new technology can be exploited (Hargadon and Douglas 2001). The risk may also be explained by the existence of an uncertain learning cost associated with the adoption of the new technology (Musso 2009). In this case, adopters do not adopt because they cannot estimate the cost of learning. This second type of inertia strategy has been observed in industries such as those of light bulbs and PVC pipes. Prencipe (2000) and Dosi et al. (2003) use the risk of reduced reliability to legitimize another strategy leading to the slow adoption of new technologies. They indicate, for example, that jet engine producers have delayed the adoption of new electronic engine control technology for fear of declining reliability. Although this new technology significantly improved the performance of jet engines, the companies continued to use the old hydromechanical control technology because its reliability had been proven. The new electronic control technology was first tested in the less critical subsystems of the aircraft before being used for engine control. Throughout this period, producers have

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preferred to take the risk of selling engines with limited performance rather than taking the risk of reducing reliability. This third type of inertia strategy emphasizes that, in certain industries such as aeronautics, reliability is more important than performance. 3.1.2.3. Link between uncertainty, reliability and slow adoption of novelty In an economic modeling study, Heiner (1983) introduced the notion of reliability condition to describe adoption behavior that is similar to what has been studied by Prencipe (2000) and Dosi et al. (2003). An agent will be able to select a new action from outside his initial repertoire of actions when the reliability associated with the new action exceeds the reliability required to increase performance. If the new action does not meet this condition, the agent will select an action from its initial repertoire of actions. In Heiner’s work (1983), it appears that adoption is hampered by the risk of choosing a new action that will reduce reliability. It can, therefore, be said that the higher the risk of reducing reliability, the more agents limit the adoption of novelty. The work initiated by Hannan and Freeman (1984) clarifies from another angle the relationship between uncertainty, reliability, and low adoption of novelty (Kelly and Amburgey 1991; Singh et al. 1986). First, they indicate that an uncertain environment favors the survival of organizations with high levels of reliability and traceability. They then add that the best way for organizations to maintain high levels of reliability and traceability is to replicate their organizational structure. By emphasizing the role of replication, this work presents, from another perspective, the strong link between uncertainty, reliability, and the slow adoption of novelty. The work initiated by Hannan and Freeman (1984), as well as that of Heiner (1983), provides a better

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understanding of this essential link. However, they remain inadequate to understand in detail the slow adoption of new technologies when there is a risk of reducing reliability. Indeed, Heiner uses very abstract variables in his model which are difficult to measure. The work of Hannan and Freeman (1984) deals with organizational change, not technological change. In addition, they do not recognize the ability of organizations to act on their strengths and weaknesses in a satisfactory way. 3.1.3. Modeling a strategy of technological inertia based on reliability 3.1.3.1. The technological risk In order to study in detail an inertia strategy based on the reliability constraint, we will rely on the concept of technological risk, which is defined as a risk on the performance of the product. The most obvious manifestation of this risk is failure. Risk management indicates that risk has two dimensions: loss and uncertainty (Chapman and Ward 2003; Cooper and Chapman 1987; March and Shapira 1987; Roberts 1990). In this study, losses represent the cost of the failure, which corresponds to the price of the defective product, but which can also include the negative consequences of the failure (Fischhoff et al. 1984). One thinks, for example, of a discount demanded by the customer for the defective product. The second dimension of technological risk is technological uncertainty caused by a lack of knowledge about the behavior of the product. The implementation of a cognitivist approach leads to the consideration that uncertainty can be extrinsic and intrinsic (DoD 1986). Extrinsic uncertainty refers to a lack of knowledge about the characteristics of the physical environment in which the product is used. This uncertainty exists for products used in

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so-called “hostile” environments, as is the case in air, sea, and space activities. Extrinsic uncertainty can be reduced by a learning process that increases the knowledge about the hostile environment. This learning is a long-term process that implies that in the short term, the level of extrinsic uncertainty is fixed. When products are used in generic environments, they are subject only to intrinsic uncertainty. This second category of uncertainty is caused by a lack of knowledge about the characteristics of the product itself. Intrinsic uncertainty refers, for example, to the failure rate of a desktop computer during normal use. The same category of technology may have different levels of intrinsic uncertainty. For example, technologies considered reliable have a lower level of intrinsic uncertainty than technologies considered less reliable. Similarly, because novelty leads to an increase in uncertainty (Hannan and Freeman 1984; Heiner 1983), new technologies have a higher intrinsic technological uncertainty than technologies that have already been proven. As a result, the intrinsic uncertainty can be reduced in the short term by choosing the technology deemed most reliable. These two types of uncertainty ultimately allow technological risk to be described from a perspective in which technological risk consists of an extrinsic risk and an intrinsic risk. 3.1.3.2. The inertia strategy The inertia strategy exists in the context of complex and expensive products that will be used in hostile environments. Since producers are not in a position to manufacture these products on their own, they purchase many systems and components from suppliers. In other words, producers must adopt technologies to create products that are subject to a high extrinsic technological risk. In this context, the principle of risk aversion will increase the importance of the

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reliability criterion in technology adoption (Dos Santos Paulino 2014a). Indeed, in the short term, producers cannot reduce extrinsic uncertainty, but they can reduce intrinsic uncertainty by choosing more reliable technologies. Therefore, for example, there will be a relative decrease in the importance of criteria such as weight, power consumption, and size, in favor of reliability. Producers will adopt the most reliable technologies, even if other technologies are better on other criteria. This rational choice promotes the reuse of proven technologies and limits the adoption of new technologies. On the one hand, because new technologies are generally less reliable than proven technologies, and on the other hand, the reuse of technologies that have demonstrated their reliability is a convincing approach to maintaining reliability. In order to offer reliable products to their customers, producers limit the intrinsic technological risk involved in adopting new technologies. This behavior describes a particular form of inertia strategy that is based on reliability. This strategy involves sacrificing certain performance criteria, such as weight, power consumption, and size, in favor of reliability. On the one hand, the reuse of proven technologies and the careful adoption of new technologies lead producers to delay the adoption of new technologies; on the other hand, this strategy of inertia helps producers to offer reliable products, despite a high-extrinsic technological risk. In other words, this strategy of inertia promotes the survival of producers, despite the fact that it encourages delays in adoption (Dos Santos Paulino 2014a). We synthesize this strategy of inertia based on reliability with the following hypothesis: Hypothesis 1: The higher the extrinsic technological uncertainty and the cost of the failure, the more the firm adopts an inertia strategy based on reliability.

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3.1.4. Research methodology 3.1.4.1. The context We will test hypothesis 1 using data collected from a European prime contractor in the space industry. We chose a space firm because it operates in a context where the three variables of our hypothesis are observed: failure cost, extrinsic technological uncertainty, and inertia in the adoption of new technologies based on reliability. Insurance theory states that the cost of disasters is a negative function of their frequency. This rule applies to the majority of activities but not to space activities where failures are frequent and very costly (Ritchie and Lallour 1998). A space prime contractor manufactures expensive and complex systems by integrating many technologies obtained from suppliers (e.g. satellites, space probes, and ground stations; Dos Santos Paulino 2007; Hobday 1998). For example, a telecommunications satellite weighs several tons, is composed of thousands of components, and costs approximately the same as an Airbus A320 airliner. Space products are used in hostile environments, such as space, which generates very high-extrinsic technological uncertainty for space firms. More specifically, firms lack the knowledge to operate in the space environment, which results in a high failure rate. For example, among the 799 spacecraft launches made between 1998 and 2009, the failure rate is 6.59% (Kyle 2011). Similarly, among the 218 scientific space probes launched since the beginning of planetary exploration and which completed their mission in 2008, there is a 45%1 failure rate due to technical problems (Dos Santos Paulino 2014a).                                         1 These figures have been calculated by Williams “Chronology of Lunar and Planetary Exploration.”

(2008)

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In the inertia strategy that we are promoting, we consider that reliability requirements play a central role in the slow adoption of new technologies. It appears that space firms are very concerned by these requirements because they belong to the group of high reliability organizations according to Roberts (1990) and Vaughan (1990). These organizations have high reliability requirements because they operate complex and risky technical systems. For example, Potteck (1999) indicates that there is a tendency to overestimate safety margins within space organizations. If engineers know that to transmit a signal they need to use a 55-cmdiameter antenna, they will design a 60-cm-diameter antenna to protect against technological risk. Thus, when spacecraft operate normally, their life span often extends well beyond their expected life span. 3.1.4.2. The data We collected our data in 2008 from a profitable European prime contractor. These data were extracted from databases built by the quality and purchasing departments. Our data describe 573 categories of intermediate products that the firm can purchase from suppliers to manufacture spacecraft and ground stations. These categories of intermediate products, which we consider technologies, can be, for example, subsystems (e.g. antenna and engine) and components (e.g. electronic components and cables). The purchasing and quality departments have classified the 573 technology categories into four groups: the flight group refers to technologies embedded in spacecraft and used in space or on other stars; the ground mission group refers to technologies embedded in ground stations and used externally (e.g. control software); the influential and noninfluential ground groups refer to technologies that are not embedded in spacecraft but are necessary for their manufacture (e.g. miscellaneous tools and equipment used in cleanrooms). The latter two groups of technologies are used

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on the ground by the prime contractor in sensitive areas, such as clean rooms and non-sensitive areas. 3.1.4.3. Measurement of variables 3.1.4.3.1. The cost of the failure Knowing that space products are expensive, the cost of failure is high for space firms. We propose to estimate it from two perspectives. We first calculated the average price of each of the 573 technology to obtain a first estimate of the cost of the failure, noted C. In this estimate, the cost of the failure is equal to the price of the defective technology. We, therefore, assume that the failure of a technology does not have negative consequences for the company, such as granting a discount to a customer who has purchased a defective product. In the second estimate, noted K, we integrate these negative consequences and consider that the failure of a technology can have a cost higher than its own price. We integrate the negative consequences of the failure by relying on the different environments in which space technologies are used. The environment makes it possible to identify when technologies are used and whether they can be easily repaired in the event of a failure. When a failure occurs in space, the technology is used during the mission and is not easily accessible to attempt a repair. For example, astronauts cannot be sent to repair a satellite in orbit, only software updates are possible. In case of a failure in orbit, the performance of the space mission is seriously degraded, which encourages the customer to request a discount. Since flight technologies are used in space, they have the highest failure cost, K = C × 2. The influential and non-influential ground technologies have the lowest failure cost, K = C. These technologies are used on the firm’s premises during the production of space systems. This implies, on the one hand, that they are not used during the space mission and, on the other hand, that they are accessible and easily

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repairable. Ground mission technologies have an intermediate failure cost, K = C × 1.5. On the one hand, they are used outdoors and in very remote locations, making their accessibility and repair more difficult than for influential and non-influential ground technologies; on the other hand, they are used during the space mission. Table 3.1 presents descriptive statistics of the cost of the failure by each estimation method.

C

€1.787

Standard deviation €1.060

K

€2.732

€1.986

Average

Min

Max

Median

€0.319

€2.880

€1.645

€0.319

€5.761

€2.880

Note: million euros Table 3.1. Cost of the failure

3.1.4.3.2. Extrinsic technological uncertainty We measure intrinsic technological uncertainty using the method proposed in MIL-HDBK-217 (DoD 1986). This manual is a reference in failure prediction for space and military technologies. The failure rate is estimated using a formula composed of the average failure rate per 10,000,000 h of use under normal conditions multiplied by several coefficients that allow the estimate to be refined. In the formula, there are always coefficients that take into account the quality of the technical components (e.g. components meeting the MIL. SPEC standard) and coefficients that take into account the stress imposed by the operating environment. The coefficients describing the operating environment measure the extrinsic technological uncertainty. It can be seen that the more hostile the operating environment, the higher the coefficients. The MILHDBK-217 manual presents the value of these coefficients for several technologies used in thirteen categories of operating environments, including Submarine, Ground,

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Naval, Missile Launch, Air Flight, and Space Flight. We have identified among these 13 environments those where the four groups of technologies purchased by the space prime contractor are used. The technologies of the non-influential and influential ground groups are used in the benign ground environment, the technologies of the ground mission group are used in the fixed ground environment, and the flight technologies are used in the Missile Launch and Space Flight environments. Then, using the data presented in the MIL-HDBK-217 manual, we calculated the average of the coefficients describing the operational environment of the technologies purchased by the space prime contractor. The values obtained constitute the first estimate of the extrinsic technological uncertainty noted as U. Since the more hostile the environment, the higher the average coefficients, we have decided to propose a second simpler estimate of the extrinsic technological uncertainty noted in V. Table 3.2 shows the distribution of technologies and the values of extrinsic technological uncertainty according to the two estimation methods. Technology groups Non-influential and influential ground Ground mission Flight

Number of technologies

Extrinsic technological uncertainty U (average V (arbitrary values) values)

253 (44.1%)

0.92

1

92 (16.1%)

2.91

2

228 (39.8%)

19.32

3

Table 3.2. Extrinsic uncertainty values and technology distribution

It can be seen that the majority of the technologies purchased by the space prime contractor belong to the

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non-influential and influential ground groups. Although the purpose of space technologies is to fly, the flight group corresponds to a smaller number of technologies purchased. We obtain values ranging from 0.92 to 19.32 for the first estimate of the extrinsic technological uncertainty calculated using the MIL-HDBK-217 (U) manual. We arbitrarily chose values 1, 2, and 3 for the second estimate noted V. 3.1.4.3.3. Inertia based on reliability Through the work of Heiner (1983) and Hannan and Freeman (1984), we have highlighted the close link between reliability and inertia in the adoption of novelty. We will deepen this link through quality assurance, which is defined as a systematic program of monitoring and evaluating technologies purchased from suppliers. This method, which is very useful for ensuring reliability, is very popular in space activities since, as early as 1994, 96.6% of aerospace organizations had quality assurance certifications, such as ISO standards (Ravix 2000). Many authors studying both innovation and quality assurance point out that quality assurance slows down innovation dynamics (Foray 1993; Sauleau and Mathy 1997; Savall and Zardet 1996). Meeus and Oerlemans (2000) went further and used quality assurance as an indicator of inertia. In this study, we do the same and consider that certain quality assurance behaviors promote both the reuse of proven and reliable technologies, and limit the adoption of new technologies. For each technology that can be purchased from a supplier, the European prime contractor’s quality department implements several quality control and evaluation actions. We have selected five specific actions that contribute to reliability control and, in doing so, limit the adoption of new technologies and promote the reuse of proven and reliable technologies:

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— The technology ti has been added to the quality department database. This means that reliability is important for this technology. — The quality department has checked whether the technology ti meets quality assurance standards, such as ISO, RG, Aero, and RAQ. — The technology ti was evaluated after the purchase or after a quality audit. — The technology ti received a positive evaluation after evaluation. — The technology ti is a technology to be preferred. We then calculated how many of these shares relate to each of the technologies that can be purchased by the prime contractor. We present our results in Table 3.3. Intensity of the inertia strategy 0 1 2 3 4 5

Number of technologies 230 (40.1%) 62 (10.8%) 39 (6.8%) 23 (4.0%) 168 (29.3%) 51 (8.9%)

Table 3.3. Distribution of technologies according to the intensity of the inertia strategy

A technology with a score of 5 is a technology for which the inertia strategy based on reliability is the most intense. Conversely, a technology with a score of 0 is not affected by the reliability-based inertia strategy. It can be seen that the majority of the technologies purchased by the prime contractor are not affected by the inertia strategy based on reliability. The technologies most affected by this strategy

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represent only 8.9% of the technologies that can be purchased. The average intensity of inertia strategies is relatively low, at 1.9. 3.1.4.4. Formal model We test hypothesis 1, which we recall further on, with a formal model. Hypothesis 1: The higher the extrinsic technological uncertainty and the cost of the failure, the more the firm adopts an inertia strategy based on reliability. We propose three versions of the model to compare the results obtained with the different methods estimating the cost of failure and the extrinsic uncertainty: â

â

â

[1]

â

â

â

[2]

â

â

â

[3]

The intensity of the inertia strategy for technology i is noted Ii. The level of extrinsic technological uncertainty for technology i is noted as Ui when we use the values obtained with the estimation method based on the MIL-HDBK-217 manual. This level of uncertainty is noted as Vi when we use the values obtained with the arbitrary estimation method. The cost of failure for technology i is noted Ci when we use the values obtained with the method based on a cost of failure equal to the cost of the technology. The cost of failure is noted Ki when we use the values obtained with the method based on a failure cost that may exceed the cost of the technology. Finally, i = 1, 2,... 573 because there are 573 technologies. We estimate the coefficients â0, â1, and â2 with the Ordered Logistic Regression method (OLR) and the Ordinary

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Least Square method (OLS). We choose the OLR method because the explanatory variable is qualitative, multinomial, and ordered. We use the OLS method to make comparisons. 3.1.5. Results We show our results in Table 3.4.

Version 1 Variables

OLR

MCO

Coefficients Version 2 OLR

MCO

Version 3 OLR

MCO

Extrinsic 1.76* 1.14* technological (14.79) (19.33) uncertainty (V) Extrinsic 0.14* 0.09* 0.22* 0.13* technological (11.58) (14.09) (15.60) (23.45) uncertainty (U) Cost of failure 1.34* 0.79* 1.56* 0.70* (C) (13.50) (15.58) (14.00) (11.92) Cost of failure 0.70* 0.40* (K) (11.92) (12.86) Intercept 0.029 1.66* 0.57* ( 0.31) ( 11.64) ( 5.65) McKelvey and 0.62 0.61 0.70 Zavoina’s R2 R2 0.56 0.57 0.637 Prob > χ2 0.00 0.00 0.00 Prob > F 0.00 0.00 0.00 Observations

573 573 573 573 573 573 Notes: z-statistics and t-statistics in brackets. *Significant at the 2% threshold. Table 3.4. Regression results

Our results validate hypothesis 1. We observe in the three versions of the formal model a positive relationship between,

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on the one hand, the level of extrinsic technological uncertainty and the cost of failure and, on the other hand, the intensity of the inertia strategy based on reliability. The coefficients obtained with the OLR method are interpreted as follows: a 1-point increase in the extrinsic technological uncertainty (V) increases the intensity of the inertia strategy by 1.76 points, all other things being equal. Beyond the fact that the three versions of the model validate hypothesis 1, we observe some differences in the results obtained with each model version. In version 1, extrinsic technological uncertainty has more influence than the cost of failure on the implementation of the inertia strategy. In versions 2 and 3, the opposite situation is observed. Since version 3 of the model has the highest R2 coefficients, we can say that the estimation of extrinsic technological uncertainty based on the MIL-HDBK-217 manual and the estimation of the cost of failure based on a cost equal to the price of the technology are the best choices. In addition, in version 3 of the model, the inertia strategy is influenced more by the cost of the failure than by the extrinsic technological uncertainty. This result is quite intuitive, and it underlines that the main problem is not to make no mistakes but to bear the cost of the failure. 3.1.6. Discussion and conclusion In this study, we precisely characterized a strategy of technological inertia based on reliability. We have also shown that its implementation is both a rational and efficient choice when the cost of the failure and the extrinsic technological uncertainty are high. This type of strategy can lead to delays in the adoption of new technologies.

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In our view, firms that implement an inertia strategy should be included in the category of adopters called “laggards” by industry lifecycle theory. These firms adopt new technologies later because they have higher expectations in terms of reliability than firms in the early adopters’ group. For example, space firms are laggards in the adoption of new electronic components compared to smartphone firms who are early adopters. In our view, space firms are not laggards because they are technophobic and irrational, they are laggards because they have higher requirements in terms of reliability than smartphone firms. Our results lead us to consider that it is wrong to state that technological delays systematically constitute weaknesses for firms (Dos Santos Paulino 2014a). On the contrary, some delays must be seen as manifestations of the mastery of the high levels of reliability necessary to be competitive in the industry. Delays in the adoption of new technologies can be a manifestation of the mastery of a key success factor (Dos Santos Paulino and Callois 2009). This result should lead to legitimizing some technological delays observed when performing the internal strategic diagnosis. Legitimizing some delays in the adoption of new technologies is an important contribution of this study (Dos Santos Paulino 2006). Despite this, we do not consider immobility and conservatism to be the solution. By inertia strategy, we mean a slow adoption of new technologies and not their systematic rejection. Delays result from the time required by firms to find a way to integrate the new technologies into the space products without scarifying the reliability. This particular adoption process is called “spatialization” by space firms. In our opinion, a systematic rejection or a too slow adoption of new technologies has to be avoided even if reliability is a key success factor. A too long delay in adoption would increase the threat of substitutes for space technologies. For example, terrestrial

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telecommunications networks would be an easier alternative to telecommunications satellites. By recognizing that innovation is a risky process, this study aims to better take into account the creative destruction that remains, for us, one of the main characteristics of innovation. This work confirms, in the field of technological innovation, some of the results obtained by the work initiated by Hannan and Freeman (1984) on organizational innovation (Beugelsdijk et al. 2002; Carroll and Teo 1996; Schwarz and Shulman 2007; Singh et al. 1986; Wezel and van Witteloostuijn 2006). Technical and organizational innovation is a process that has ambivalent effects on the survival of organizations. The too rapid and too slow adoption of innovations is a threat to the survival of organizations. We show that the appropriate rate of adoption depends on the characteristics of the environment. A risky environment will require slower adoption rates, i.e. more technological inertia. Our results indicate that customer expectations in terms of reliability are an essential dimension to be taken into account. These expectations are reflected in the level of the cost of the failure, which in turn positively influences the intensity of the inertia strategy based on reliability. It is important to note that the cost of the failure may differ from one customer to another (Dos Santos Paulino 2006, 2011). As they have a profit objective, commercial customers will impose a high cost of failure (Dos Santos Paulino and Barbaroux 2016). For example, they spread their payments over the lifetime of the satellite, allowing them to obtain a price reduction if the satellite is defective before the end of its expected lifetime (Sennequier 2000). Space agencies impose a lower cost of failure because they have other objectives. In particular, they wish to stimulate the development of a national space industry and increase knowledge of the space environment (Dos Santos Paulino and Barbaroux 2016). The existence of these two categories of customers allows satellite

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producers to conduct both exploitation activities based on reliable technologies and exploration activities aimed at “spatializing” new technologies (March 1991; Levinthal and March 1993). For instance, exploration activities relate to the production of telecommunications satellites for commercial customers, while exploration activities relate to the production of research and development satellites for space agencies. The existence of these two customers leads space firms to develop a certain organizational ambidexterity (Wang and Rafiq 2009). Finally, this study allows us to better understand the loss of momentum in the dynamics of technological change that we observed in Chapter 2, by carrying out a meso analysis (Dos Santos Paulino 2007, 2011, 2014a; Dos Santos Paulino and Callois 2009, 2010). The factors that explain the slowdown in innovation are based on reliability, extrinsic technological uncertainty, and the cost of the failure. In this work, we collected data from a prime contractor in the space industry. The secrecy constraints in this industry have sometimes led us to use simple indicators. For future works, we would like to improve data collection in order to deepen the implications of our results. 3.2. Slow adoption of organizational innovations: a key success factor 3.2.1. Introduction Evolutionary work has contributed significantly to improving our understanding of innovation and, more specifically, organizational change. This work is essentially structured around the adaptation perspective founded by Nelson and Winter (1982). One of the results of this perspective is to show that organizational change reduces organizational mortality. This implies that organizational

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delays must be considered problematic and organizations must implement corrective actions to address them. Space organizations are experiencing delays in the way they manage projects and technology (Dos Santos Paulino 2006, 2007, 2009, 2012). For example, projects are rarely organized on the basis of simultaneous phases, with organizations preferring more traditional management based on sequential phases. In technology management, space organizations rely on a methodology developed in the 1970s. Technologies remain essentially developed according to a “technology push” strategy. Considering that slow organizational changes, and the resulting delays, are dangerous for space organizations is problematic. On the one hand, it seems that slow organizational changes contribute to the reliability of space organizations. On the other hand, in the space industry, reliability is a key success factor. This preliminary observation runs counter to the evolutionary perspective of adaptation; however, it is more consistent with the argument put forward by the selection perspective. This second evolutionary perspective argues that organizational change increases organizational mortality. The selection perspective, structured around the work of Hannan and Freeman (1984), establishes a positive link between organizational inertia and survival. In our view, the perspectives of selection and adaptation are complementary and not contradictory on the relationship between organizational change and survival. Therefore, the objective of this work will be to demonstrate that slow organizational change promotes the survival of organizations when the environment is risky. This behavior can lead to organizational delays and we call it an “organizational inertia strategy.”

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3.2.2. Organizational change: a literature review 3.2.2.1. Organizational change and survival The evolutionary paradigm that emerged in biology displays a debate between the perspective of adaptation and selection about the link between survival and change. The adaptation perspective considers that change promotes survival, while the selection perspective affirms, on the contrary, that change is a threat to survival. The importation of the evolutionary paradigm into the social sciences, such as anthropology, psychology, and linguistics, has also imported this debate. In management sciences and economics, the debate focuses on the impact of organizational change on the survival of organizations2. Based on the work of Singh et al. (1986), we are able to synthesize the main arguments for adaptation and selection perspectives. The adaptation perspective dominates the theme of organizational change and is adopted by authors based on the work of Nelson and Winter (1982). This perspective is sometimes referred to as the “rational adaptation theory” (Beugelsdijk et al. 2002), a “pro-changing perspective” (Schwarz and Shulman 2007), and “pro-innovation bias” (Meeus and Oerlemans 2000). The selection perspective is based on the work of Hannan and Freeman (1984) and is sometimes referred to as “ecological theory” (Singh et al. 1986) and “structural inertia theory” (Kelly and Amburgey 1991).

                                        2 In the field of technological change, the perspective of adaptation dominates so much that there is no real debate (Carroll and Teo 1996; Meeus and Oerlemans 2000).

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Figure 3.1. Organizational change and evolutionary perspectives [Source: adapted from Dos Santos Paulino (2009)]

Both perspectives consider it essential to take into account the changes in the environment in order to study organizational change. However, each perspective takes a different perspective on how organizations adapt to environmental change. The adaptation perspective assumes that organizations are able to act on their strengths and weaknesses in a satisfactory way. They adapt effectively to environmental changes, which implies that their strategies

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reduce their mortality. Since organizational changes are a visible aspect of these strategies, the adaptation perspective considers that organizational changes reduce organizational mortality. In other words, the adaptation perspective states that organizational inertia increases organizational mortality. In this perspective, change involves a change in routines and is often referred to as “innovation.” Organizational innovations result from the recombination of existing routines, the imitation of routines owned by other organizations and the introduction of new routines obtained in particular through research activity. The main assumption of the selection perspective is that organizations are not able to act satisfactorily on their strengths and weaknesses. This perspective retains the idea that change occurs at the population level and comes more from the elimination by the environment of existing organizations. Organizations are not able to conduct a rational adaptation through the recombination, imitation, and search for routines. As a result, organizations that attempt organizational change weaken communication routines and processes that ensure high levels of reliability and traceability. However, according to Hannan and Freeman (1984), the actors who provide the resources essential to the survival of organizations want, on the one hand, a low variance in product quality and, on the other hand, guarantee that organizations are administered in a rational manner. In other words, actors, such as banks, employees, customers, and institutions, give priority to providing resources such as capital, skills, commitment, and recognition to organizations with high levels of reliability and traceability. According to Hannan and Freeman (1984), organizations that maintain high levels of reliability and traceability are those that replicate their organizational structure. The environment selects organizations with organizational inertia and eliminates those that make frequent organizational changes. The link between inertia

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and survival leads the selection perspective to conclude that organizational changes increase mortality. 3.2.2.2. Complementarity of evolutionary perspectives Rather than considering evolutionary perspectives as contradictory, we prefer, like other evolutionary authors, to see them as complementary (Beugelsdijk et al. 2002; Carroll and Teo 1996; Schwarz and Shulman 2007; Singh et al. 1986; Wezel and van Witteloostuijn 2006). The relevance of each perspective would depend, according to these authors, on factors such as the magnitude of the changes being considered, the size of the organization and the centrality of the changes. The selection perspective would better describe the impact of radical organizational changes on large organizations and in the heart of organizational structures. The adaptation perspective would better describe the impact of incremental organizational changes on smaller organizations and on the periphery of organizational structures. The work of Carroll and Teo (1996) allows us to consider the complementarity through the level of risk in the environment. These authors argue that organizational theories cannot assume that change takes place without friction, costs, and dangers. Moreover, the higher the risk associated with change, the more organizational change compromises the survival. By building on the work of Carroll and Teo (1996), we consider that the selection perspective is more relevant to describe organizational change in a risky environment, while the adaptation perspective is more appropriate in a less risky environment. 3.2.2.3. Organizational change in HROs The work on High Reliability Organizations, also known as HROs, allows us to look further into the complementarity between the two evolutionary perspectives.

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HROs produce and operate high-tech systems such as space shuttles, nuclear power plants, and air traffic control systems (Bourrier 1996; LaPorte 1996; Weick 1990; Weick and Roberts 1993). These technological systems are risky or operate in risky environments (Roberts 1990). When HROs make mistakes, the consequences are often dramatic, as shown by the explosion of the space shuttle Challenger, the Chernobyl disaster, and the Tenerife air crash (1977). In a risky environment that promotes the survival of organizations with high levels of reliability, LaPorte and Consolini (1991) show that HROs implement a rational adaptation strategy. The basis of this strategy is to replicate everything that guarantees high levels of reliability. Once the reliability target is met, any change will degrade rather than improve reliability (LaPorte 1996). Roberts (1990) notes, for example, that the captain of aircraft carrier Theodore Roosevelt insists that a rule should never be broken unless compliance with it jeopardizes safety. Similarly, Bourrier (1996, p. 109) observes that, in the management of a nuclear power plant, last-minute ideas, including those that appear to be good, should be avoided because they can totally compromise a safety exercise. Work on HROs shows that organizations adapt rationally to a risky environment by seeking to maintain high levels of reliability. The survival of organizations depends on limiting organizational changes. In our view, this argument holds, on the one hand, the idea that organizations act effectively on their strengths and weaknesses and, on the other hand, it recognizes that change is dangerous to survival. The work on HROs provides a relevant combination of the two evolutionary perspectives (Dos Santos Paulino 2009, 2012). However, it is difficult to go any further with HRO literature because it does not provide an appropriate analytical model for our study. Indeed, this literature has a

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research program that does not aim to study the adoption of organizational innovations. 3.2.3. Modeling the organizational inertia strategy In our opinion, to achieve high levels of reliability, organizations must perform organizational innovations through recombination, imitation, and research. In other words, they must act as advocated by adaptation theory. It is only when organizations have reached the desired levels of reliability that they set themselves the goal of maintaining these high levels. According to Hannan and Freeman (1984), this requires reproducing the structure of organizations. Distinguishing between achieving and maintaining high levels of reliability makes it possible to introduce a temporal complementarity between the adaptation and selection perspective. By combining the different theoretical contributions mentioned earlier, we are able to propose a model that describes how organizational inertia ensures survival in a risky environment. Our model consists of five steps that we present Figure 3.2. Our model is based on the following reasoning: (a) a risky environment imposes high levels of reliability, (b) this first leads organizations to make organizational innovations in order to achieve the required levels of reliability, and (c) organizations will then maintain these high levels of reliability through rigorous organizational replication (i.e. replication of rules, methods, procedures, and processes). It is at this point that organizational inertia begins, which has two consequences: (d) on the one hand, it promotes the survival of organizations and (e) on the other hand, it generates organizational delays.

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Figure 3.2. Organizational inertia and survival in a risky environment

We define organizational inertia as a rational adaptation strategy aimed at a slow adoption of organizational innovations to maintain high levels of reliability. This strategy can be observed in a risky environment. The organizational inertia strategy involves recombining existing routines, imitating routines owned by other organizations, and, to a lesser extent, introducing new routines obtained through research activity. It should be noted that these three actions are carried out with caution and are common to innovation strategies. Although organizational inertia is not immobility, it can lead to organizational delays. 3.2.4. Methodology We will demonstrate the validity of our model through a case study on organizations belonging to the space industry. Our units of analysis are the large space organizations, such as space agencies and spacecraft manufacturers that produce

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and operate launchers, satellites, and space probes. These organizations perform their activities in risky environments. This case study is based on illustrations from space organizations and covers the period from 1999 to 2009. We chose to stop our study at the end of the 2000s because, at that time, there were many discontinuities in the space industry, grouped under the name “New Space” (Dos Santos Paulino and Le Hir 2016). We will utilize a variety of sources, such as academic documents, articles in the specialized and general press, and other useful documents (e.g. reports, popularization documents). We will also use the results of two interviews with space firms. Finally, we will complete our sources with observation data collected from a European space prime contractor. Observations and interviews were conducted between 2003 and 2006. 3.2.5. Results 3.2.5.1. Risky environment Our model detailing organizational inertia is based primarily on the fact that space organizations face a risky environment. Risk management indicates that risk has two dimensions: loss and uncertainty (Chapman and Ward 2003; Cooper and Chapman 1987; Roberts 1990; March and Shapira 1987). The case of the Venera space program is an interesting illustration of the uncertainty surrounding space activities (Brunier 2006; Dos Santos Paulino 2012). At the beginning of space exploration, it was accepted that Venus was a tropical paradise because of its proximity to the Sun (van Vogt 1969). The Venera program, led by the Russians during the 1960s and 1970s, helped to challenge this consensus. Without being able to determine precisely the atmospheric

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conditions of Venus, the failures of the first six Venus probes demonstrated that Venus was not a tropical paradise. It was thanks to Venera 7 that it was proven that Venus was a very hostile planet, with an atmospheric pressure 90 times higher than that existing on Earth, a temperature exceeding 490°C and sulfuric acid rains. Exploration of the planet Mars also involves a high degree of uncertainty. For example, a JPL3 employee calculated that only 45% of Mars landings were successful (Santini 2008). Since most failures remain unexplained, some space journalists are mischievously referring to the existence of a monster on Mars that feeds on spacecraft (Dinerman 2004). In space activities, losses are generally very high because the cost of a program is rarely less than several hundred million euros and can reach several billions (Ritchie et al. 1998, p. 836). As a result, even minor errors can have a considerable cost. The case of the Mars Surveyor 98 program, which cost US$327 million, deserves attention (Dos Santos Paulino 2009). This program consisted of the Mars Polar Lander, which was lost due to an unknown event, and the Mars Climate Orbiter, which was lost due to a measurement unit problem. NASA had written specifications with the metric system, while a subcontractor had used the imperial system (JPL-NASA 1997). These illustrations show that space organizations produce systems for a risky environment. Ritchie and Lallour (1998) summarize this situation quite well. They indicate that insurance theory states that losses are a negative function of their frequency. According to them, this rule applies to the majority of human activities but not to space activities in which failures are frequent and cause high losses.

                                        3 Jet Propulsion Laboratory: NASA’s space research center.

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3.2.5.2. High levels of reliability required Space organizations have adapted rationally to a risky environment by placing reliability at the center of their organizational choices (see Figure 3.2) (Dos Santos Paulino 2009, 2012). This strategic direction was seen as the most effective way to ensure survival. Space organizations have reduced risks by implementing many organizational innovations resulting from intense recombination, research, and imitation activities. At the beginning of the conquest of space, space organizations were pioneers in making organizational choices to achieve high levels of reliability. They have developed what the HRO research calls a strong reliability culture, which begins with a strong commitment to reliability (LaPorte 1996; Roberts 1990, p. 173; Roberts et al. 1994). Space organizations have been pioneers in the development of quality control methods and quality assurance (Bach et al. 2002). For example, space organizations have contributed to the first quality control standards such as the MIL-STD standard. This standard has been widely adopted by space organizations. Space organizations have also adopted quality assurance more quickly than other organizations. In a 1994 study in France, Ravix (2000) reported that 96% of aerospace organizations had adopted quality assurance, compared to only 28.8% for other organizations. According to LaPorte (1996), a strong reliability culture is also achieved through the implementation of organizational redundancies. When assembling spacecraft, people carry out the assembly itself and others record all the actions carried out in a document (D’Armagnac 2004). The purpose of writing this document, called the “Acceptance Data Package,” is to be able to check whether everything has been done correctly. Another dimension of the reliability culture is that technical skills determine authority and legitimacy in

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organizations (LaPorte 1996). In space organizations, are a few recruitments of profiles without technical (Michot 2004). Technical skills are so dominant engineers even perform tasks normally assigned to managers, such as marketing and communication Santos Paulino and Tahri 2014).

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there skills that sales (Dos

Beyond the creation of a reliability culture, other organizational innovations have also contributed to achieving the high levels of reliability required by the space environment. They can be observed in technology management, project management, and supply chain management. Again, space organizations were often pioneers in the 1960s and 1970s in the implementation of innovations in these fields (Bach et al. 2002; Dos Santos Paulino 2009, 2012). NASA has helped develop the TRL4 scale to assess the maturity of a technology, including whether it is sufficiently reliable to be implemented operationally (Banke 2017). Space organizations have also been pioneers in the implementation of PERT5 and PPP6 methods to improve project management in terms of time, cost, and quality control. With regard to supply chain management, space organizations have also been among the first to develop sophisticated partnership relationships (Alcouffe 2001; Bès and Alcouffe 1997). This has been very useful in creating innovative, complex, and very expensive products. For example, space organizations established co-design relationships very early on.                                         4 Technology Readiness Level. 5 In a simplified way, the Program Evaluation and Review Technique method is based on the construction of a dependency graph to manage projects. 6 Phased Project Planning assists in the development of new products by identifying key phases associated with a main deliverable. In space activities, the usual phases are: mission analysis, feasibility study, preliminary design, detailed design, production, assembly, and testing.

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3.2.5.3. Organizational replication When space organizations have reached the desired high levels of reliability, another step begins, characterized by the start of the organizational inertia strategy (see Figure 3.2). Organizations will now seek to replicate their organizational structure (i.e. rules, methods, procedures, processes, and routines; Dos Santos Paulino 2009, 2012). To understand this choice, we can mention two illustrations from Ariane launchers. Ariane 4 was one of the most reliable launchers of its generation and has only experienced three launch failures. Investigations revealed that the failure occurred in 1990 was caused by an unplanned intervention on the launcher. A cloth had been left in the water supply of one of the four Viking engines, which led to the explosion of the launcher (Ritchie and Lallour 1998). In 2017, Ariane 5 experienced a less dramatic failure that also relates to the introduction of novelty. A customer had made an unusual request that Arianespace implemented with a new procedure that excluded double testing. The lack of double testing allowed a human error in setting the launcher’s trajectory to be overlooked, which almost led to launch failure (Barensky 2018; Cabirol 2018). These examples echo the work on HROs, which emphasizes that a rule should never be broken unless compliance with it jeopardizes safety (Roberts 1990, p. 169). The dangers associated with not replicating validated rules are reduced by rigorously verifying compliance between what is done and what has been validated in the past (Vaughan 1990). Compliance is achieved through highly formalized production processes based, for example, on specification documents and the Acceptance Data Package (D’Armagnac 2004). Specification documents are written at the beginning of the development of the space program and describe the technical characteristics of each system and how these systems will be assembled. The specification documents for a new program must be in accordance with the documents

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written during previous programs. Similarly, the Acceptance Data Package of the current mission is used to verify that the development and assembly steps performed are consistent with those performed in previous programs. Tasks related to the replication of the organizational structure are essential in spatial construction. The head of the SME department we met gave us an indication of their weight. He told us that the time spent recording what is done can represent 50% of total working time when the customer is in the space sector, while it represents only 20% of working time when the customer is in the shipbuilding sector. 3.2.5.4. Organizational delays Organizations that seek to rigorously replicate their structure will achieve fewer organizational innovations than others. This behavior will lead them to gradually show organizational delays compared to the organizations that replicate less rigorously their structure (see Figure 3.2; Dos Santos Paulino 2006, 2007, 2009, 2012). In the 1960s and 1970s, space organizations made several innovations that enabled them to be pioneers in technology management, project management, and supply chain management. The benefits of these innovations have prompted organizations in other industries to take inspiration from space. It should be noted, however, that it is not just a simple copy and paste that has been done by nonspace organizations. Organizational innovations in space have often been improved. In project management, organizations in other industries have improved PPP by introducing the possibility of having simultaneous phases in projects and significant feedback between phases. In supply chain management, non-space large organizations have drawn inspiration from the sophisticated partnership relationships that already existed

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in the space industry to strengthen their relationships with suppliers. However, instead of limiting these relationships to tiers-one suppliers who are also large companies, it was decided to also involve tier-two and tier-three suppliers that can be SMEs. Similarly, the TRL initially strongly inspired technology management in other industries. However, improvements have been made, such as including the “market pull” strategy in the TRL, which is originally based only on a “technology push” strategy. In non-space industries, the management of technology has gradually become a management of innovation in which organizations rely, for instance, on multifunctional teams (e.g. R&D, marketing, supply chain, law, etc.; Dodgson et al. 2008; Trott 2012). These improvements in technology, supply chain, and project management have been accompanied by the adoption of information systems increasingly based on Information and Communication Technologies (ICT). These organizational changes have increased the performance of organizations. They have contributed to: (1) reducing the costs of developing and producing new products, (2) broadening the base of available expertise, (3) sharing risks, and (4) reducing time to market. Despite these positive influences on performance, space organizations have slowly adopted the organizational innovations made by nonspace organizations. Having reached the high levels of reliability required, space organizations have preferred to replicate their organizational structures with diligence rather than take the risk of reducing reliability. This strategy of inertia led to the emergence of organizational delays. Space projects are, for instance, rarely based on simultaneous phases and there are few feedbacks between phases (Alcouffe 2001; Potteck 1999). In supply chain management, spacecraft manufacturers bring together SMEs in the manufacturing phases and rarely in the design phases (Haas et al. 2001; Potteck 1999). It is considered that SMEs do not have sufficient expertise to be included in

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sophisticated partnerships, such as co-design. The person in charge of the design office of a subcontractor we met confirmed this observation. He told us that his customers in the space industry did not entrust them with design tasks, unlike their other customers in the aeronautics and automotive industries. For example, during the Airbus A380 aircraft project, 30% of the design costs were borne by the SME partners (ENHANCE 1999). The situation is comparable in technology management, which has remained largely based on the TRL method in space organizations. The understanding of the needs of users remains partially addressed because the influence of the market pull strategy is marginal among space organizations. In addition, project teams are mainly composed of engineers selected for their technical legitimacy (Dos Santos Paulino and Tahri 2014). These organizational delays lead to delays in ICT adoption. In the 2000s, our observations in a space prime contractor allowed us to note a lower use of digital collaborative work tools, very useful in the design phases (Dos Santos Paulino 2006). For example, space organizations did not have virtual reality tools and planned to adopt CATIA CAD software version 5 in 2008. In the aeronautics industry, Airbus had already been using this software since 2002. With regard to exchanges with partners, the telecommunications networks of space organizations spilled over a little to SMEs, compared to what was already being done in aeronautics (Dos Santos Paulino 2006). 3.2.5.5. Organizational inertia favors survival Organizational delays are one of the consequences of replicating the organizational structure. The other consequence is to favor the survival of organizations in an environment where high levels of reliability are required (see Figure 3.2). This consequence is at the heart of the argument proposed by the literature on HROs (Weick and Roberts 1993) and the selection perspective (Hannan and Freeman 1984).

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To illustrate this, we can again mention the case of the Ariane launcher, commercialized by Arianespace. This company has built its competitive advantage in marketing the most reliable launcher on the market. Indeed, the average success rate of launchers was 92.6% in 2007, while Ariane 4 had a rate of 98.5%. This superior reliability has enabled Arianespace to dominate the market for launches open to competition, including against American companies (Dos Santos Paulino 2012; Pradels 2016). Thus, since the 1980s, the Ariane launcher has been responsible for about 50% of commercial satellite launches. The high reliability of the Russian Soyuz launcher and spacecraft also explains their success and longevity. They were designed in the 1960s for manned spaceflights and they remain in service today. Many of the competing products that have been designed after the Soyuz, such as the US space shuttle, already ceased their operations because of lower reliability. Similarly, space organizations, such as satellite operators, base their success on their ability to provide telecommunications services with minimal service interruptions compared to terrestrial solutions. Spacecraft manufacturers’ offers are chosen for their reliability/price ratio, regardless of the fact that they have organizational delays (Blanc 2010; Pradels 2016). 3.2.6. Discussion and conclusion The objective of this study was to demonstrate that organizational inertia favors organizational survival when the environment is risky. To do this, we constructed an original model that describes a five-step reasoning, as schematized in Figure 3.2 (Dos Santos Paulino 2006, 2007, 2009, 2012). We recall this reasoning here: (a) a risky environment imposes high levels

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of reliability, (b) this first leads organizations to make organizational innovations in order to achieve the required levels of reliability, (c) organizations will then maintain these high levels of reliability through rigorous organizational replication (i.e. rules, methods, processes). This strategy of organizational inertia, (d) on the one hand, favors the survival of organizations and (e) on the other hand, generates organizational delays. By organizational inertia, we mean a rational adaptation strategy aimed at a slow adoption of organizational innovations in order to maintain the high levels of reliability required in a risky environment. The strategy of organizational inertia is not a form of immobility but involves carefully implementing innovations. By carrying out a case study on the space sector, our main result is the confirmation of our model. We demonstrate that the slow adoption of organizational innovations is a key success factor in a risky environment because it ensures the high levels of reliability required. This result implies that some organizational delays should be seen as a manifestation of the mastery of a key success factor and not as a weakness that should be reduced by accelerating organizational change. Making an incorrect diagnosis following the observation of organizational delays can lead to corrective measures that would weaken the ability of organizations to maintain high levels of reliability. These corrective measures would be dangerous because they would prevent organizations from offering an attractive offer and would lead to their death. Our model is based on a temporal complementarity between the perspective of adaptation (Nelson and Winter 1982) and that of selection (Hannan and Freeman 1984). We consider that organizational change is both a source of survival and mortality because innovation is inseparable from creative destruction. In a risky environment, organizations starting their business must, in our opinion,

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first make organizational innovations to achieve the high levels of reliability required and then implement organizational replication to maintain these levels. Our study shows that organizations that adopt organizational inertia strategies may, in the past, have been pioneers in many organizational innovations. This observation makes it possible to understand more precisely the slowdown of the dynamics of technical change that we observed in the previous chapter. This slowdown also had an organizational dimension, which can be explained in particular by the difference between achieving and maintaining high levels of reliability in a risky environment. We, therefore, show that the trade-off between exploration and exploitation put forward by March (1991) can be sequential. This study also complements our work on the strategy of technological inertia (Dos Santos Paulino 2014a). When the environment is risky, organizational and technological delays can be the sign of the mastery of a key success factor, namely, high reliability. Firms that adopt organizational and technical inertia strategies fall into the category of laggard adopters suggested by the industry lifecycle theory. In addition, both these strategies are based on several similar characteristics such as: (1) a risky environment, (2) high reliability requirements, and (3) replication. We consider that organizations from other industries may also implement strategies of organizational inertia. We think first of all of the organizations facing risky environments in which HROs will be found. For example, organizations manufacturing and operating nuclear power plants, air traffic control systems, aircraft, and aircraft carriers. We also consider that our model can be used to better understand the organizational change in organizations facing less risky environments. We are thinking here, for example, of the aeronautic and the automotive industries.

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Some organizational delays should also contribute to the survival of organizations by helping to maintain high levels of quality. The main limitation of the proposed model is that it does not take into account the dangers of an organizational inertia strategy. Fostering organizational replication to maintain high levels of reliability can be dangerous when the performance gains from organizational innovations are large enough to make up for the decline in reliability. This situation can occur, for example, when the industry is subject to major discontinuities. In the next chapter, we examine in more detail the strategy of firms in this context.  

 

 

4 Technological Discontinuities and Strategic Diagnosis

In the previous chapter, we showed that satellite manufacturers adopted inertia strategies that enabled them to maintain the high levels of reliability required by customers. These strategies ensure the survival of firms through organizational and technological replication (Dos Santos Paulino 2006, 2007, 2009, 2012, 2014a; Dos Santos Paulino and Callois 2009). The relevance of inertia strategies prevails as long as firms do not observe discontinuities that disrupt the structure of the industry. Examples include discontinuous innovations that would compensate for the decline in reliability with a major increase in performance or discontinuous innovations that would change the performance criteria required by customers. As of the 2010s, the space industry is experiencing many discontinuities that practitioners group under the name “New Space” (Dos Santos Paulino and Le Hir 2016; Pérez et al. 2017). Satellites are attracting new customers with different expectations in terms of reliability, price, and performance. For example, Silicon Valley companies, such as the giants Amazon, Google, and Facebook, are questioning the possibility of acquiring thousands of satellites to provide Internet access in the white areas of developed and

Innovation Trends in the Space Industry, First Edition. Victor Dos Santos Paulino. © ISTE Ltd 2020. Published by ISTE Ltd and John Wiley & Sons, Inc.

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developing countries. On the supply side, new producers are entering the industry by claiming to be able to develop relatively cheap satellites by changing performance criteria, including reliability requirements. The technological discontinuity that attracts the attention of new entrants is the advent of small satellites. The miniaturization of satellites seems to have the potential to transform the structure of the space industry. The emergence of small satellites leads existing firms to have to determine whether this discontinuous innovation is a substitute that would threaten the traditional large satellites they have been selling for many years. In this chapter, we propose an original model to help existing firms improve their external strategic diagnosis (Dos Santos Paulino and Le Hir 2016; Pérez et al. 2017). To do this, we refer to the theory of disruptive innovations introduced by Christensen in 1997 and deepen the concept of potential disruptive innovation proposed by Danneels (2004). We show that small satellites are potential disruptive innovations, but do not pose a significant threat to existing firms that manufacture large traditional satellites. 4.1. Disruptive innovations and threat analysis 4.1.1. Introduction The satellite industry has experienced many discontinuities since the early 2010s (Dos Santos Paulino and Le Hir 2016; Pérez et al. 2017). The technological discontinuity that focuses attention is the appearance of small satellites. The miniaturization of satellites seems to have the potential to offer new opportunities to the space industry. This situation leads existing firms to question whether small satellites are a substitute that would threaten traditional large satellites. In this study, we propose to

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determine whether this discontinuous innovation poses a threat to existing firms. To do this, we refer to the theory of disruptive innovations introduced by Christensen in 1997 (Dos Santos Paulino and Le Hir 2016). The usual reasoning behind this theory is that a disruptive innovation is proposed by new producers and that it introduces new performance criteria that are not valued by the existing mainstream customers. When it is introduced, disruptive innovation is ignored by existing firms because it does not interest the mainstream customers. Innovation is only purchased by a small group of customers who have a low budget. Over time, the performance of the disruptive innovation increases and will attract mainstream customers. Christensen (1997) argues that by then it is too late for existing firms to invest in disruptive innovation. New entrants will gradually dominate the market controlled until now by existing firms. According to Christensen (1997), when new entrants introduce new technology, existing firms face the “innovator’s dilemma” which is expressed as follows: existing firms must cannibalize their existing products and invest in the new technology that is, for the moment, rejected by their customers. Although the theory of disruptive innovations is very useful in understanding how new technologies can pose a threat to existing firms, it has two limitations (Dos Santos Paulino and Le Hir 2016; Pérez et al. 2017). On the one hand, the concept of disruptive innovation cannot be used by existing firms to diagnose the level of threat associated with a new technology (Danneels 2004). Indeed, firms discover that a new technology is a disruptive innovation when it is too late to act. On the other hand, there is confusion about what exactly a disruptive innovation is (Schmidt and Druehl 2008). In this study, we address these limitations by further developing the concept of potential disruptive innovation

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suggested by Danneels (2004). Without defining this concept precisely, Danneels (2004) proposes to follow this avenue of research to address several limitations of the theory of disruptive innovations. In this work, we precisely define the potential disruptive innovations by constructing a model. This allows us to determine, before it is too late for existing firms, whether new technologies pose a threat. We apply our model to the case of small satellites and show that although they are potential disruptive innovations, small satellites do not pose a significant threat to existing firms. 4.1.2. The theory of disruptive innovations The concept of disruptive innovation was coined by Christensen. His work has enjoyed strong support among researchers and practitioners, which has helped to create the theory of disruptive innovations (Christensen and Bower 1996; Christensen 2006; Danneels 2004; Govindarajan and Kopalle 2006a, 2006b; Markides 2006; Paap and Katz 2004; Schmidt and Druehl 2008; Yu and Hang 2010). 4.1.2.1. Synthesis of traditional reasoning In Figure 4.1, we show the particular dynamics that can be observed when new entrants use new technology to enter the industry. New entrants can offer disruptive technology in a new, smaller market segment where margins and cost structure are low. In this new segment, new entrants attract low-end customers whose needs are not met by existing firms selling existing technologies to mainstream customers (see point A in Figure 4.1).

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Figure 4.1. Disruptive and continuous technology [Source: adapted from Christensen et al. (2015)]. For a color version of the figures in this chapter, see www.iste.co.uk/dossantos/innovation.zip

Existing firms often fail to properly diagnose the disruptive technology and the new market segment. Disruptive technology is not considered as a threat because it does not meet the performance expectations of mainstream customers. Similarly, the new segment is not an opportunity for existing firms because margins or volumes are insufficient. These firms prefer to serve with existing technologies the mainstream customers who have expectations above those of low-end customers (see Figure 4.1). The survival of disruptive technology in a minor segment allows it to be improved until it meets the performance expectations of the mainstream customers (see point B in Figure 4.1). At that time, the existing firms observe very

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strong competitive pressure from new entrants. The mainstream customers now consider disruptive technology as a substitute for existing technology. The size of the existing market shrinks in favor of the new market segment. Existing firms generally have difficulty offering an attractive offer in the new segment and fail to enter it because they are unable to catch up with the learning curve. Existing firms have difficulty mastering disruptive technology and understanding customer expectations. Finally, existing firms abandon and return to their initial market, which is now a niche market where there are only high-end customers (see point C in Figure 4.1). When a new technology is introduced by new entrants, existing firms must determine whether this new immature technology is a disruptive technology that will become a substitute for the existing technology. If so, they must invest in new technology as soon as possible and cannibalize existing technology to ensure their long-term survival. This difficult choice for existing firms is called by Christensen (1997) “the innovator’s dilemma.” 4.1.2.2. Predictive value of the concept of disruptive innovation Disruptive innovations have the potential to destroy existing markets and create new ones. As such, they are a manifestation of the broader concept of creative destruction observed in economic evolution (Schumpeter 1911). Disruptive innovations do not disrupt the structure of the industry immediately after their introduction but after a diffusion process that takes time (Schmidt and Druehl 2008). When introducing them, it is very difficult to distinguish between, on the one hand, an innovation with low performance that will fail and, on the other hand, an innovation that introduces new performance criteria that are temporarily rejected by customers but that will turn out to be a disruptive innovation (Tellis 2006). During the introduction, the existing firms in both cases observe

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innovations that do not provide them with any strategic advantage. The fact that the nature of disruptive innovation changes between the time it is introduced and the time when its diffusion is already well advanced is a major obstacle to using this concept to make predictions (Danneels 2004). Indeed, existing firms will only know that an innovation with poor performance will become a disruptive innovation when it is too late to act. Existing firms need to know as soon as possible whether an innovation with poor performance will ever be a threat. This key managerial objective is currently inaccessible because the scholars identify disruptive innovations through characteristics that can be observed when diffusion is already well advanced (Danneels 2004). In order to address this limitation, Danneels (2004) suggests that the concept of potential disruptive innovation be further developed. In this study, we agree with this suggestion and propose to identify a potential disruptive innovation through the initial characteristics of a disruptive innovation. By initial characteristics, we mean the characteristics of the disruptive innovation when it is introduced to the market and before its diffusion is significant. 4.1.2.3. Confusion about the concept of disruptive innovation The concept of disruptive innovation has become very popular because it dynamically takes into account several aspects of the industry’s structure, such as markets, customers, products (including substitutes), and new entrants. Unfortunately, the concept of disruptive innovation is often misunderstood. Many practitioners and scholars believe that they are observing a disruptive innovation when this is not the case. There is considerable confusion about what a disruptive innovation really is (Schmidt and Druehl 2008; Yu and Hang 2010). The confusion arises from the fact that the term “disruption” is assimilated to that of “discontinuity.”

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Therefore, all discontinuous innovations are considered as disruptive innovations. To understand that this is not the case, we must mention Christensen’s distinction between disruptive innovations and sustaining innovations. Sustaining innovation improves product performance mainly on a cumulative basis. This type of innovation involves improvements in a product already established on the market. Sustaining innovation creates little uncertainty for existing firms because they quickly know if the innovation is capable of replacing existing technology, for example, the launch of USB 2 technology, which logically replaced USB 1. The disruptive innovation leads to a high degree of uncertainty for existing firms because, when it is launched, there are doubts about its ability to improve and diffuse in the market. One example is the launch of the “low cost” air transport service when customers had only access to the service of legacy airlines. The confusion around the concept of disruptive innovation stems from the fact that a sustaining innovation can improve the performance of existing products in a moderate as well as in a significant way. (Govindarajan and Kopalle 2006b; Schmidt and Druehl 2008; Yu and Hang 2010). In other words, in Christensen’s reasoning, a sustaining innovation is a cumulative innovation that can be both continuous and discontinuous1. A disruptive innovation is a particular case of non-cumulative discontinuous innovation that has already diffused significantly in the market. Disruptive innovation degrades some existing performance criteria while introducing new performance criteria. In addition, it must also have already diffused significantly before a disruption in the structure of the industry can be observed. In order to clear up the confusion around the concept of disruptive innovation, the authors of disruptive innovation theory consider that it is necessary to identify several                                         1 See Chapter 1 for a definition of these concepts.

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categories of disruptive innovation. We have identified two approaches in this work; on the one hand, those that analyze changes in customers performance criteria and budgets (Govindarajan and Kopalle 2006b); and on the other hand, those that study the diffusion process and the novelty of the market where innovation is introduced (Christensen and Raynor 2003; Schmidt and Druehl 2008). These two approaches have some complementarities that enrich understanding; however, they remain too disconnected to sufficiently remove the confusion surrounding the concept of disruptive innovation (Schmidt and Druehl 2008). We propose to merge them in order to gain clarity and above all to propose a model that makes it possible to estimate the threat before the diffusion of innovation is already too advanced to take action (Dos Santos Paulino and Le Hir 2016). 4.1.3. Model 4.1.3.1. Types of potential disruptive innovations and threat Drawing on the work of Schmidt and Druehl (2008) and Danneels (2004), we identify three types of potential disruptive innovation that we show in Figure 4.2 (Dos Santos Paulino and Le Hir 2016; Pérez et al. 2017). Type 1 has a very low price compared to the existing product, due to a decrease in performance in some criteria. There are few changes in the bundle of performance criteria, in the sense that few criteria are dropped or added. These limited changes allow producers to introduce Type 1 innovation into the existing market and to first attract the segment of customers who have a low budget but similar performance criteria to those of major customers (Figure 4.2). Low-end customers consider innovation as a second choice but still buy it because they cannot afford the existing product. If performance improvement is possible, the innovation will meet the expectations of the mainstream customers who will then start buying it. In this case, we will observe a massive

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shift from the mainstream customers of the existing product to innovation, which poses a strong threat to existing firms (Figure 4.2). In other words, Type 1 innovation is a substitute for the existing product because the two have the same performance criteria and target market. Dell’s introduction of personal computers, when Compaq and Hewlett-Packard’s personal computers dominated the market, is an illustration of the threat of Type 1 potential disruptive innovation. Indeed, the two existing firms disappeared from this market due to competition from Dell computers. Type 2 and Type 3 potential disruptive innovations have a much lower price and a much higher price, respectively, than the existing product. These price changes result from significant changes in the performance of existing criteria and in the bundle of criteria. In particular, producers are introducing new key performance criteria and abandoning some existing criteria. Type 2 potential disruptive innovation primarily attracts new low-end customers who are present in a new fringe market and who have similar performance criteria to those of the mainstream customers (Figure 4.2). As with Type 1 innovation, these new customers consider Type 2 innovation as a second choice but still buy it because they cannot afford the existing product. However, unlike Type 1 innovation, Type 2 innovation is a second choice because it shows significant changes in existing performance criteria and in the bundle of criteria. Type 3 potential disruptive innovation first attracts new high-end customers in a new market that is detached from the existing market and has different performance criteria from those of the mainstream customers (Figure 4.2). In this case, Type 3 innovation is the first choice of new customers in this new detached market.

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Even if performance improvement is possible for Types 2 and 3 of potential disruptive innovations, they will still partially meet the needs of the mainstream customers in the existing market. These two innovations present bundles of performance criteria that are too different from the bundle desired by the mainstream customers. As a result, only a small number of the mainstream customers will move from the existing market to the new fringe market and new market where Type 2 and Type 3 innovations are offered, respectively (Figure 4.2). Types 2 and 3 of potential disruptive innovations are imperfect substitutes for the existing product. They are characterized by a bundle of performance criteria and markets that are very different from those of the existing product. In other words, these innovations represent a lower threat than Type 1 innovation. The low-cost air transport service is a good illustration of Type 2 potential disruptive innovation because it is an imperfect substitute for the air transport service offered by legacy airlines such as Air France and British Airways (Schmidt and Druehl 2008; Yu and Hang 2010). The low-cost market and the legacy market coexist, even if some customers of the legacy market have moved to the low-cost market. This evolution is also observed in the case of minicomputers that have survived in their historical market, even if personal computers are an illustration of a Type 2 innovation that has diffused. Mobile lines are an illustration of a Type 3 potential disruptive innovation. They were more expensive than fixed lines, and mobility is a decisive new performance criterion. Each of the services also survived in their respective markets because a small number of customers terminated their fixed lines when a mobile line was adopted (Dos Santos Paulino and Le Hir 2016; Schmidt and Druehl 2008; Yu and Hang 2010).

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Figure 4.2. Types of potential disruptive innovations. For a color version of the figures in this chapter, see www.iste.co.uk/dossantos/innovation.zip

4.1.3.2. Analysis grid The generalization of the above analysis leads to proposing nine characteristics to identify potential disruptive innovations. When introduced, the three potential disruptive innovations have a lower performance than the existing product in the criteria valued by mainstream customers (1). They also introduce new performance criteria that are not valued by mainstream customers (2). Type 1 and Type 2 innovations are cheaper to produce and offered at a much lower price than the existing product (3), while Type 3 innovation is much more expensive than the existing product (4). When introduced, none of these innovations are purchased by mainstream customers (5). Type 1 innovation is purchased by existing customers (6), while Type 2 and

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Type 3 innovations are purchased by new customers in new markets (7). Customers of Type 1 and Type 2 innovations have low budget compared to the mainstream customers (8), while customers of Type 3 innovation have a high budget (9). In Table 4.1, we summarize the nine characteristics of the three types of potential disruptive innovations through two dimensions: technology and demand. We also indicate the level of threat induced by each type of innovation.

Demand

Technology

Characteristics Reduction in performance for criteria valued by mainstream customers (1) New performance criteria not valued by mainstream customers (2) Less expensive than the existing product (3) More expensive than the existing product (4) Not purchased by mainstream customers (5) Purchased by existing customers (6) Purchased by new customers in new markets (7) Purchased by low-end customers (8) Purchased by high-end customers (9) Threat to existing firms

Potential disruptive innovations Type 1: Type 2: Type 3: existing new new market fringe detached market market X

X

X

X

X

X

X

X

n/a

n/a

n/a

X

X

X

X

X

n/a

n/a

n/a

X

X

X

X

n/a

n/a

n/a

X

High

Lower

Lower

Table 4.1. Characteristics of potential disruptive innovations

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When considering a recently introduced innovation in light of the nine characteristics of potential disruptive innovations, it is possible to determine whether this innovation will pose a threat to existing firms if it is diffused. Our reasoning is as follows: if the innovation introduced has the characteristics of Type 1 potential disruptive innovation, the threat will be high in the event of diffusion; on the other hand, the threat will be lower if the innovation introduced has the characteristics of Type 1 and Type 2 potential disruptive innovations. 4.1.4. Methodology In this study, we implement a descriptive methodology applied to the case of the world satellite industry. We use several secondary sources, both qualitative and quantitative. Qualitative sources were used to conduct a content analysis and are structured into three categories: (1) websites of space organizations (e.g. SEI and Futron Corporation); (2) press articles (Space Review, Spaceflight, and other specialized journalistic sources); and (3) open databases (Encyclopedia Astronautica). Quantitative sources are open archival data, accessible on the Internet (i.e. Claude Lafleur, Gunter Krebs, Findthedata, and EO Portal). These archival data are reliable sources of information used by scholars in their research (Barbaroux and Dos Santos Paulino 2013; Dos Santos Paulino and Le Hir 2016; Zelnio 2007) and by practitioners to conduct strategic intelligence (ASDEurospace 2015). Using these quantitative sources, we built an original database that we then used to perform descriptive statistics. Our original database shows a selection of fundamental criteria describing satellites, including weight, lifetime, power consumption, and type of customer. We have sought to collect these characteristics for all 2,870 satellites launched between 1990 and 2014. When

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observations were missing, we always ensured that we worked with a representative sample with a 95% confidence level. The time period analyzed starts in 1990 because the end of the Cold War started a new phase for the space industry, which gave rise to many discontinuities (Dos Santos Paulino and Barbaroux 2016). For instance, in the early 1990s, NASA and other space agencies encouraged the implementation of space programs developed according to the new paradigm of “faster, better, and cheaper.” These changes have fostered the development of smaller satellites that could be a threat for the traditional satellites made by existing satellite manufacturers. We will proceed in two steps to estimate the threat. First, we will characterize small satellites in light of the structure of the industry and the innovator’s dilemma. In a second step, we use our analysis grid (Figure 4.1) to determine whether this technological discontinuity is a potential disruptive innovation and to estimate the level of threat to existing firms. 4.1.5. Results 4.1.5.1. Discontinuities and innovator’s dilemma Satellites are high-tech goods produced by an industry that presents high barriers to entry for new entrants. These barriers are explained not only by high requirements in terms of capital and reliability but also by the existence of complex regulations. Satellite manufacturers need a significant financial base to acquire expensive facilities (e.g. test rooms) and to cope with the spread of customer payments of up to a decade. Satellites are high-tech products, so manufacturers must also invest significant amounts in R&D. In addition, it

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generally takes 2—7 years to design satellites that are often manufactured individually and through the control of a complex supply chain. Satellite design requires continuous and in-depth discussions with the customer, which often changes specifications after the project has started. Since satellites are used in a risky environment, manufacturers have had to commit significant resources over many years to achieve and maintain the required high levels of reliability (Dos Santos Paulino 2009, 2012, 2014a; Dos Santos Paulino and Callois 2009, 2010). Finally, there are many regulations that hinder the construction and sale of satellites because they are dual-use products, both civil and military. Examples include export controls and favoring domestic producers in institutional tenders. Existing firms manufacture satellites priced between US$100 million and US$400 million, to which the customer must add the launch price, which corresponds to two-thirds of the price of the satellite itself (Blanc 2010). Although there is significant variability, satellites generally weigh several tons and have a lifetime of more than 10 years and an electrical power of about 4,000 W. The main applications of satellites are Earth observation and telecommunications, where performance criteria are image resolution, visibility, and bandwidth, respectively. Satellites with these features are traditional satellites because they have characterized the industry until 2013. From this date onward, there has been a technological discontinuity marked by numerous launches of small satellites. In particular, Figure 4.3 shows that from 2013 onward, the number of satellites weighing less than 500 kg launched each year exceeds the number of satellites weighing more than 500 kg. The evolution of the total number of launches allows a better understanding of the extent of the discontinuity. The previous peak for all types of spacecraft launched was 180 launches in 1965 (i.e. satellites and space probes). It can be seen in Figure 4.3 that 253 satellites were launched in 2014

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and that the majority of these satellites are satellites weighing less than 500 kg.

Figure 4.3. Satellites launched by weight [Source: adapted from Dos Santos Paulino and Le Hir (2016)]

Small satellites are characterized by variable masses. This implies a lack of consensus among practitioners to define exactly what a “small satellite” is. Several weight ranges are proposed and, in light of Figure 4.3, we define a small satellite as a satellite weighing less than 500 kg. This technological discontinuity coincides with many other discontinuities that have initiated a new dynamic in space activities that practitioners have named “New Space.” Among these other discontinuities, there is a sudden increase in the number of new entrants. For example, in Table 4.2, we see the entry of 12 new satellite manufacturers from 1990 onward, including 10 only after 2005.

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Period of time 1990— 2005 2005— 2014

Number of entries 2 10

New entrants Satrec Initiative and Deimos Space Geo-optics, Gomspace, Clyde Space, Skybox Imaging, NovaNano, Planet Labs, Dauria Aerospace, Tyvak NanoSatellite Systems, PlanetiQ, and OmniEarth

Average size Employees: 84.1 Turnover: €33.21 m

Table 4.2. New entrants

Although the majority of new entrants are SMEs and small satellites have not yet proven their worth, existing firms are facing the innovator’s dilemma in the 2010s (Dos Santos Paulino and Le Hir 2016). Should they ignore small satellites or invest in this new technology and, as a result, reduce the resources allocated to traditional satellites? There are several arguments supporting each strategy. In the 2010s, small satellites are above all technological and commercial promises for existing firms. In addition, there are significant barriers to entry into the industry that SMEs may have difficulty overcoming. Despite this, the misadventures observed in other sectors by companies, such as Kodak, Compaq, and Hewlett-Packard, suggest that a serious threat could exist. Indeed, these companies have had to leave their markets following the successful introduction of disruptive innovations by new, often small, entrants. 4.1.5.2. Threat to existing firms In Table 4.1, we have defined the potential disruptive innovations through nine characteristics describing both the technology and the demand. We use this breakdown to conduct our analysis.

Technological Discontinuities and Strategic Diagnosis

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4.1.5.2.1. Characteristics of the technology

Performance criteria (average)

A potential disruptive innovation exhibits, in the criteria valued by the mainstream customers, a lower performance than existing products (characteristic 1, Table 4.1). This characteristic is validated for small satellites since we observe in Table 4.3 that they have an average life span 2.6 times shorter than traditional satellites. Small satellites are also less reliable than traditional satellites. Electrical power is another performance indicator since the higher the power of a satellite, the greater its capacity to receive and transmit data. We observe in Table 4.3 that small satellites have an average electrical power 14 times less than traditional satellites.

Life time (years) Electrical power (watts) Weight (kg)

Small satellites (