MCC Centre for Next Technological Futures

Studies on Innovation, Technologies and Regions

Volume 1

© MCC Press Kft., 2024

© Authors, 2024

All rights reserved, including the right of reproduction, public performance, radio and television broadcast, for the book as a whole and for its constituent parts.

Editor-In-Chief: Zoltán Cséfalvay

Editors: Viktor Lázár, Csikó Bálint Pálfai

ISBN 978-963-644-066-4 (print)

ISBN 978-963-644-091-6 (epub)

First published in Hungary in 2024.

Published by MCC Press Kft.

www.mccpress.hu

Managing Director: Tamás Novák

Editor: TheProBookEditor

Electronic version: György Madarász

Graphs and figures: Gábor Fodor

Cover design: Rita Papp

Electronic version v1.0

CONTENTS

Foreword

Concertation or Convergence? How the Technological Specialisation of Scaleups Is Shaping the Entrepreneurial Ecosystems of European Cities

Zoltán Cséfalvay and Zoltán Szombathy

Uncovering the Role of Proximity in European Scaleup Investments

Csaba Kristóf Johanyák

The Driving Force of Startups in Munich: A Comparative Analysis of Incubator and Accelerator Programs

Orsolya Székely

Make an Impact or Chase Profits? Characteristics of ESG and Profit-Oriented Incubators and Accelerators

Viktor Lázár

Small Matters: The Way CEE Countries Can Penetrate the US Innovation Market

Viktor Lázár

The Dynamics Between Startups and Incumbents in Service Robot Manufacturing

Zoltán Cséfalvay

The Emergence of Robotics Nations

Viktor Lázár and Máté Ujvárosi

About the Mathias Corvinus Collegium

About the Contributors

FOREWORD

As cyberpunk author William Gibson famously said, “The future is already here—it’s just not evenly distributed.”[1] This ironic statement is particularly relevant when we look at the map of innovation and entrepreneurial ecosystems in Europe, where we can easily see deep regional imbalances. But while the future of technology is nearly impossible to predict—in part due to the nature of Schumpeterian creative destruction—it could be discovered. It could be discovered by those companies, the startups, whose basic working method is trial and error. And that’s the main reason why this little book is about startups and robots.

The Centre for Next Technological Futures (CNTF, https://cntf.mcc.hu) was founded in 2021 at the Mathias Corvinus Collegium in Budapest and pursues Alexander von Humboldt’s century-old idea of a mutually beneficial combination of research and university studies. The seven essays collected here in the first volume of Studies on Innovation, Technologies and Regions are the result of this type of research by student researchers and draw on numerous sources and inspiration from the work of CNTF.

First, in recent years, CNTF’s student researchers have presented their research results at the Eurasia Business and Economics Society (EBES) conferences in Berlin, Budapest, Lisbon, Madrid and Rome. These conferences provided them with excellent opportunities to discuss research hypotheses and methods and results, and in doing so, helped to further develop the research into publications.

Second, in 2023, the CNTF became a member of the Global Trade & Innovation Policy Alliance (GTIPA), a global network of more than fifty research institutes and innovation policy think tanks that strengthened policy thinking within CNTF. Participation in the Conference on Robots and Automation (CORA) in Frankfurt, Germany also led to increased interest in studies on robotisation.

Third, the various study trips organised by the CNTF to the main startup ecosystems around the world could equally be considered a valuable and integral source of research, for example the tours to Silicon Valley, Toronto-Waterloo, Washington DC, Israel, Singapore, London, Paris, Sevilla, and Warsaw.

In the first paper, Zoltán Cséfalvay outlines the geography of the scaleups (startups that raised more than one million euros in funding) in Europe. By analysing the data of more than 12,000 scaleups retrieved from Dealroom at the level of 166 functional urban areas in Europe, he highlights two main trends. The first is the strong concentration of scaleups in a handful of leading scaleup cities, while the second trend is the strong West-East and North-South divide in the European scaleup city landscape in terms of performance of cities in relation to startups.

Csaba Kristóf Johanyák investigates the impacts of locally available venture capital (VC) funds on startups at city level in Europe. Particularly, he focuses on the role of these VCs at different funding rounds. By applying the gravity model and using a dataset of nearly 3,500 VC fund transactions between 2012 and 2021 retrieved from Dealroom, he finds that geographic distance plays an important role in investment decisions, especially in the early development stages of startups. However, in later stages, as the distance between the investor and the investment increases, the amount of funding received by the startups decreases.

Orsolya Székely delves into a relatively less analysed topic of the supporting institutions in startup ecosystems, such as incubators and accelerators, in the case of Munich. The semi-structured interviews carried out with seven accelerators indicate that, while the selection processes and the programs provided are slightly similar in all accelerators investigated, there are significant differences among them with regard to strategic, sectoral, and territorial focus.

In contrast, Viktor Lázár analyses those accelerators that revolve around environmental, social, and governance (ESG) goals. By conducting more than twenty semi-structured interviews, he distinguishes between accelerators aimed primarily at profit and those supporting ESG startups. He concludes that accelerators focusing on ESG startups can attract more non-corporate funding and have more extensive programs and less strict selection processes than their accelerator counterparts that only chase profit.

In another study, Viktor Lázár examines the technological specialisation of the Central and Eastern European countries (Czechia, Hungary, Poland, Slovakia, and Slovenia) and compares this with the subnational innovation indices in the United States at state level. In this way, he identifies those US states with which cooperation of the CEE countries would be mutually beneficial in order to enhance the innovation potential, in particular at the level of startups.

Viktor Lázár and Máté Ujvárosi raise the question of what factors influence the adoption of industrial robots, in particular the development of robot density (measured as the number of industrial robots deployed per ten thousand employees in manufacturing) in 17 European countries between 2011 and 2019. To analyse the dynamics of robot density in time and the explanatory measures, they employ a fixed-effects model (FEM). As a result, they highlight that there are three factors of prominent importance, such as the labour cost in manufacturing, the business R&D expenditure, and the specialisation in industries in which industrial robots are deployed in a large-scale (e.g., automotive sector).

Zoltán Cséfalvay distinguishes two distinct stories in robotisation, that of the industrial robots and that of the robots in service sector. While industrial robots are deployed in large-scale but in very few industries, the service robots are currently deployed in small-scale but in widening application areas. His in-depth analysis of the service robot manufacturers based on IFR data between 2010 and 2021 reveals particular dynamics in which not only the large manufacturers but also the small robotics startups play a crucial role. Yet, both the service robot manufacturers are concentrated only in a handful of countries, while the robotics startups are in very few ecosystems.

The Centre for Next Technological Futures would like to thank the Mathias Corvinus Collegium for the support and excellent facilities provided for the research, the results of which are included in this book.

Prof. Zoltán Cséfalvay

Head of the Centre for Next Technological Futures

Mathias Corvinus Collegium, Budapest

CONCERTATION OR CONVERGENCE? HOW THE TECHNOLOGICAL SPECIALISATION OF SCALEUPS IS SHAPING THE ENTREPRENEURIAL ECOSYSTEMS OF EUROPEAN CITIES[2]

Zoltán Cséfalvay and Zoltán Szombathy

1. INTRODUCTION: STARTUPS, SCALEUPS AND THE ECOSYSTEMS FOR INNOVATION AND ENTREPRENEURS

While startups are crucial for innovation and have become one of the main drivers of current industrial revolution, relatively little is known about their territorial distribution. Various startup ecosystem rankings (e.g., Startup Genome, 2021; StartupBlink, 2021; Dealroom and Sifted, 2021; Erasmus Centre for Entrepreneurship, 2021) indicate that startups are extremely concentrated in few hubs, and consequently they focus on the largest and most advanced startup ecosystems. The Startup Genome, for instance, publishes its annual report for over 150 cities worldwide, ranks them by metrics such as startups’ performance, funding, connectedness, market reach, and access to knowledge and talent, and analyses the evolution of ecosystems in a lifecycle model that includes four phases, i.e., activation, globalisation, attraction and integration (Startup Genome, 2020, 59–61). The basic idea is the old evidence of regional sciences, and this is greatly supported by Silicon Valley’s success story (Saxenian, 1994; Castells, 2000) that the regional concentration of startups, top universities, risk capital, entrepreneurial expertise, and supportive institutions together tends to be developed first as a self-sustaining and later, after a critical mass, as a self-reinforcing system that is able to attract investments and talents from all over the world (Engel, 2014). Therefore, on the one hand, the competition among the startup ecosystems is about to scale and grow to the point where the ecosystem becomes self-sustaining, and on the other hand, beyond this point, it is also about to attract the main resources around the world, primarily the talent and capital.

Nevertheless, the 2021 report places only five European cities in the global top thirty startup ecosystems, such as London, Paris, Amsterdam, Stockholm and Berlin, while three more cities, Munich, Bern-Geneva and Dublin, are ranked in the ten runners-up ecosystems (Startup Genome, 2021, 19). These are followed by a larger group of top one hundred emerging ecosystems, in which thirty-five European cities are ranked, among others Copenhagen, Barcelona, Madrid, Zurich, Helsinki, Oslo, Manchester, Brussels, Prague and Warsaw, just to mention a few (Startup Genome, 2021, 32–34). Taking these figures and following the logic of the lifecycle model, in Europe only few startup ecosystems have reached the point where development becomes self-reinforcing and begins increasingly attracting the global resources, while the majority of the European cities are trapped in the activation or globalisation phase, in short, their main goal is to sustain themselves.

In contrast, anecdotal startup success stories in remote locations suggests that in the Digital Age startups can be launched almost anywhere as long as the place is connected to the Internet. A quick glimpse into the frequency with which the terms digital technology and startups are mentioned in the literature—taken from Google Books Ngram Viewer which records the frequencies of phrases in a great bulk of books—shows that they have been going hand in hand and their effects are mutual (see Fig. 1). As digital technologies lower the market entry barriers, opening up more opportunities for startups and vice versa, these startups continue to drive the development of digital technologies. In addition, digital technologies make it easier to combine the different business and technology fields, which is the very essence of innovation, and the startups could largely benefit from this.

More than half a century ago, Schumpeter praised the entrepreneur as the engine of development who comes up with new products, new methods of production or new forms of industrial organisation (Schumpeter, 1942, 82), but he also made it clear what new means in most cases, since “innovation combines factors in a new way, or that it consists in carrying out new combinations” (Schumpeter, 1939, 62). Similarly, Brynjolfsson and McAfee (2014, 78) recognise that “the true work of innovation is not coming up with something big and new, but instead recombining things that already exists.” As evidence, they cite some well-known examples such as Google’s self-driving car, Waze, Web, Facebook and Instagram that combine existing technologies into a new product, and they conclude that “digital innovation is recombinant in its purest form” (Brynjolfsson and McAfee, 2014, 81). Ridley (2020, 250) formulates this insight in a more general way, stating that every innovation is recombinant, and “every technology is a combination of other technologies, every idea is combination of other ideas.” Haskel and Westlake (2022) also underline that in our age when investment in intangible assets becomes increasingly important, one crucial property of intangibles, the synergy, has a tremendous impact on innovation. Because ideas and other ideas go well together, especially in technology, intangibles are often particularly valuable when properly combined with other intangibles and human capital. Finally, Lee (2018), from the perspective of an arms race for supremacy in artificial intelligence, emphasises that we are now leaving the age of discoveries and entering the age of implementation, and therefore this technology is constantly being combined with ever-broader new business and societal realms. Whether it is about new combination (Schumpeter), recombining existing technologies (Brynjolfsson and McAfee), recombinant innovation (Ridley), synergy as a basic property of intangibles (Haskel and Westlake), or entering the age of implementation (Lee), these shifts in innovation pave the way for startups that are typically involved in the innovation process when knowledge and human capital are the assets to be leveraged.

Figure 1: Frequencies of the terms digital technology, startups and entrepreneurial ecosystem in the literature, 1970–2019 (frequencies in percentages)

Source: Data retrieved January 31, 2022, from Google Books Ngram Viewer. Design by authors.

The approach that startups are embedded in an entrepreneurial ecosystem of different actors, on the other hand, has appeared in the literature only in the last decade. Today’s buzzword of entrepreneurial ecosystem (Brown and Mason, 2017; Brown and Mawson, 2019) was conceived by Moore (1993) primarily as a metaphor rather than as a research and policy concept. Yet, it aims at directly placing the entrepreneur at centre stage (Ács et al., 2017; Audretsch et al., 2019) and one of the focal points of the approach is to analyse and promote that environment which supports and enables the establishment of new firms and businesses (Isenberg 2010; Spigel, 2017; Meyer and Williamson, 2020). The major drawback is that there is a long list of such actors, i.e., universities, research institutes, technology centres, incubators, accelerators, business organisations, banks, venture capital funds, angel investors, governmental innovation agencies, non-profit organisations, but skilled labour and talents are also a prerequisite, as well as cultural factors could play a major role, including success stories and societal norms (Isenberg and Onyemah, 2016).

From this broad spectrum, there are numerous references in the literature to publicly funded and operated R&D and innovation agencies such as the DARPA in the US, the TEKES in Finland or the EIT in the EU, and many others, not least because they are underpinned by the idea of public-private partnership in innovation (Block, 2008; Mazzucato, 2013). Yet, all these face the same challenge of how the public sector should encourage new technological breakthroughs by subsidising such young companies and startups that have not yet been born. Kay (2011, 9) claims bluntly that “if an industry is to advance, much—perhaps all—innovation will come from businesses that don’t yet exist.” Another broadly studied area is the pivotal role of universities in the development of innovation ecosystems (Heaton, Siegel, and Teece, 2019), and in particular the role of knowledge transfer and spillover through university spinoffs (Scott, 2004; Graham, 2014). Finally, although they are becoming increasingly important for the development of entrepreneurial ecosystem, there are only few systematic case studies about those incubators and accelerators that aim at nurturing the talents and startups (Hackett and Dilts, 2004; Brown et al., 2019).

Despite this abundance of literature, we come across a fundamental contradiction, when we use the metaphor ecosystem for innovative technology regions with a vibrant entrepreneurial culture and critical mass of startups. While stability, resilience and organic development are the main features associated with ecosystem, the innovative entrepreneurs, and particularly the startups, are closer to the disruptive character of innovation in terms of the Schumpeterian creative destruction. There is ample evidence that large companies usually develop their products incrementally and continuously with systematically organised R&D work, while the startups are more likely to be responsible for breakthroughs and disruptive innovations (Baumol et al., 2007; Christensen, 2013; Tirole, 2017). Albeit this Schumpeterian dynamics exist between the incumbent companies with old technologies and the frontier firms with new technologies (Phelps, 2013; Aghion, et al., 2021), between disruptive innovation via startups and incremental innovation driven by incumbent companies it hardly fits into the metaphor of ecosystem (Fransman, 2018; Meyer and Williamson, 2020).

In this backdrop, our study focuses primarily on startups as a fundamental element of the entrepreneurial ecosystem centred around cities (Florida et al., 2020). In doing so, we apply the approach of Graham (2012) and use the term in line with our main source of information, the Dealroom, and accordingly in this study the startup is defined “as a company designed to grow fast” (Wijngaarde, 2021). The main advantage of using this definition is to avoid arbitrary thresholds for various measures such as age, technology, funding structure, market value or employment. For a deeper analysis, however, the problem arises that due to the specific nature of startup development and venture capital investments, only a tiny fraction of startups are responsible for the vast bulk of innovation and funding they have raised, while the overwhelming majority of startups tend to receive very minor funds or no funding at all. There is a skewed distribution of startups that somewhat follows the power law when we look at the relationship between the level of funding and the number of startups.

By analysing the growth stages of young companies, Flamholtz and Randle (2016) distinguish the “organisational scaleups” that are those startups that have already received considerable funding, developed a marketable product and a feasible business model, and hence, are able to scale quickly. According to the literature (Flamholtz and Randle, 2016), the various startup advocacy groups, such as Startup Europe Partnership and European Startup Network, and the ecosystem ranking institutions (Erasmus Centre for Entrepreneurship, 2021; Dealroom and Sifted, 2021; Early Metrics, 2021) numerous measures have to be fulfilled in order for a startup to qualify as a scaleup, such as annual growth, number of employees, or annual turnover. However, they are common when applying the threshold that scaleup are those startups that have already raised at least 1 million dollars or euros in funding.

While Ries (2011, 27) famously defined startups as “a human institution designed to create a new product or service under conditions of extreme uncertainty,” scaleups have already passed the stage of extreme uncertainty. To quote another often-cited definition of Blank and Dorf (2020, xvii) which describes startup as “a temporary organisation designed to search for a repeatable and scalable business model,” the scaleups have already found their business model and have marketable products. In short, scaleups are successful startups that are economically relevant and have growth prospects and as such can make a significant contribution to the entrepreneurial ecosystem of a city or a region. For this reason, in this study we only focus on the scaleups, for which we use the threshold that scaleups are those startups that have received funding of more than 1 million euros.

With this brief overview of startups, scaleups and entrepreneurial ecosystem in mind, this study has two objectives.

• The first is to analyse the European scaleup landscape in terms of cities’ performances with regard to scaleups, and to look in detail at the territorial distribution of scaleups across the European cities. As the various startup ecosystem rankings and analyses suggest, this landscape is expected to be dominated by a few large scaleup cities. Our analysis, however, aims to include every European city with considerable scaleup performances in order to provide a deeper insight into the geographic pattern of scaleup cities.

• The second objective of the study is to investigate how the technological specialisation affects this landscape. Does it reinforce the concentration tendency, or does it rather balance it out? For the answer, we are focusing on the scaleups that are active in those technologies that are not surrounded by the current hype, as is the case, for instance, with artificial intelligence or blockchain technology. Therefore, by analysing the impact of technological specialisation on the European scaleup landscape, this study investigates the scaleups in Industry 4.0 technologies. More than this, Industry 4.0 technologies are becoming increasingly important for Europe as the continent has clear global competitive advantages and opportunities in these technology fields.

2. INDUSTRY 4.0—AN ELUSIVE CONCEPT

The idea of Industry 4.0 jump-started since the early years of the last decade, while the dominant concept of the previous era, lean manufacturing, has gradually started to lose its relevance. Again, a simple look at the frequencies with which the related terms are mentioned in the literature—taken from Google Books Ngram Viewer—clearly shows this rapid change (see Fig. 2).

This shift in narratives about manufacturing concepts can be attributed in part to changes in the development and adoption of digital technologies as, since the millennium, they have gradually moved from the domain of information, communications and services to the core of all industries, manufacturing (Propris de and Bailey, 2020). However, today Industry 4.0 is more of a vision and an idea, a challenge and an opportunity and less of everyday practice in production (Buhr, 2017; Atkinson, 2019), and this fact opens the way for innovation and startups. On the other hand, since Industry 4.0 is more about the future than the current reality, it is not surprising that there is not yet a universally accepted definition of what this term stands for, and there are many ambiguities in its interpretation (Alcácer and Cruz-Machado, 2019; Chiarello et al., 2018; Castellani et al., 2022; Xu et al., 2018).

The basic point seems to be clear: Industry 4.0 is about the merging of digital technologies and industrial production. The main motive behind the introduction of this term is also clear—in highly industrialised countries, it is to safeguard competitive advantages in manufacturing at a time when digital technologies are increasingly being deployed in industrial production and invading manufacturing. This is the context in which former German Chancellor, Angela Merkel used this term, saying that we are dealing with “the fusion of the online world and the world of industrial production” and that this emerging trend calls for quick action otherwise “the leaders in the digital domain will take the lead in industrial production” (Zaske, 2015). Since then, the buzzword Industry 4.0 has become extremely popular and spread across Europe with a number of initiatives and national strategies (Fernández-Macías et al., 2018).

Figure 2: Frequencies of terms for the different concepts of manufacturing in the literature, 2010–2019 (frequencies in percentages)

Source: Data retrieved January 31, 2022, from Google Books Ngram Viewer. Design by authors.

However, the ambiguities surrounding Industry 4.0 arose as the term was extended to the Fourth Industrial Revolution (Schwab, 2016) or even equated with it (Roland Berger Strategy Consultants, 2014) on the one hand and was limited to the smart factory on the other. For the latter, Smit et al. (2016, 20) describes Industry 4.0 as a “model of the smart factory of the future where computer-driven systems monitor physical processes, create a virtual copy of the physical world,” and “devices autonomously communicating with each other.” The International Federation of Robotics (IFR) also uses the concept of smart factory to define the Industry 4.0 as the digitisation of production, the “linking the real-life factory with virtual reality” (IFR, 2017, 21). McKinsey Digital (2015, 7) follows this line and interprets Industry 4.0 as “digitisation of the manufacturing sector, with embedded sensors in virtually all product components and manufacturing equipment, ubiquitous cyber-physical systems, and analysis of all relevant data.”

It comes therefore as no surprise that scholars already claimed the lack of a clear definition in the mid-2010s, but they now arrive at the same conclusion. As concerns the mid-2010s, Bauer et al. (2014) pointed out that due to the clustering and interconnecting of technologies, a clear assignment of particular technology fields to Industry 4.0 was not possible, while Herman et al. (2015), after analysing more than a hundred definitions, came to the judgement that a generally accepted definition did not exist. Today, Alcácer and Cruz-Machado (2019) and Chiarello et al. (2018), using more extensive data sources for the analysis, again assert the lack of a commonly recognised definition, and, as Balland and Boschma (2021, 1653) summarise the situation, “in the extensive literature on I4, there exists no consensus on what I4T stands for.”

Notwithstanding, Industry 4.0 is not a panacea and not a “ready-to-wear” production model, but the blend and integration of many new digital technologies in manufacturing—some already used by companies, some tried and tested—such as collaborative robots, 3D printing, Big Data analysis, simulation tests, the internet of things, augmented reality, cloud services and cybersecurity. In addition, the application of Industry 4.0 needs a creative combination of these technologies that is unique to each individual company and plant. Since Industry 4.0 is tailor-made at the company level, this new model of industrial production can only be successful if an ecosystem of cooperative partners from universities, R&D institutions, startups, corporate producers and digital service providers develops around the companies.

3. DATA AND METHODOLOGY

Our main source of information is the Dealroom.co, a leading global platform for intelligence on startups that provides comprehensive data on venture-backed startups in every country throughout the world with a very detailed breakdown by location, industry, technology, funding, founders, investors and market value. However, due to the skewed distribution of startups with very minor or no funding for the vast majority, in this study we focused only on the scaleups, which means on startups that raised more than 1 million euros in funding. By applying this threshold, in 2021 we retrieved from the Dealroom database a total of 13,851 scaleups with headquarter location in Europe.

To examine the territorial distribution of scaleups, we applied the EU-OECD classification of Functional Urban Areas (FUAs) (Dijkstra et al., 2019; OECD, 2020). A FUA consists of a city (core) and its commuter zone and, as such, encompasses the economic and functional expansion of the city. One main advantage that the FUA classification offers in the regional analysis of scaleups is the availability of corresponding economic data, such as population, employment and GDP.

For analysing the European scaleup landscape, we used Tableau software to match the 13,851 scaleups retrieved from the Dealroom database to their respective FUAs. For measuring cities’ performance at FUA level in terms of scaleups, we applied three variables such as the number of scaleups, the total funding of scaleups and the number of scaleups with a market valuation more than 200 million euros. Based on these variables, we performed a cluster analysis to filter FUAs with significant performance in terms of scaleups; in particular, we applied the k-means algorithm, and this resulted in a total of 166 FUAs (consisting of 12,472 scaleups), which were arranged in 6 clusters (see Tab. 1):

• The Local scaleup cities are not only cities with the lowest number of scaleups and very minor total funding of scaleups (the median total funding is just below 100 M EUR), but the scaleups in this group have considerable difficulties to scale and grow, as here not a single scaleup is worth more than 200 M EUR.

• The Regional scaleup cities also have a very small number of scaleups and are just slightly better in scaling, since the overwhelming majority of the cities in this cluster have at least one scaleup worth more than 200 M EUR. However, the median total funding remains low, close to 200 M EUR, and thus these cities offer a location for scaleups for a smaller region.

• The main feature of the Emerging scaleup cities is that they are able to attract considerable financial resources, as the median total funding of scaleups is around 500 M EUR, although the number of scaleups is relatively modest and the median value is just over forty.

• The Top European emerging scaleup cities excel because in this group the median value of total funding exceeds the 1bn EUR threshold and their scaling performance—measured by the number of scaleups valued more than 200 M EUR—is significantly better than in the lower-ranked scaleup cities.

• The Top European scaleup cities are those that have the leading position in the European scaleup landscape, the median of the number of scaleups exceeds 250, and the median total funding is almost 5bn Euro.

• Finally, the Global scaleup cities play an important role not only at a European but also at a global level due to very high number of scaleups and total funding (in this group, the median number of scaleups is over 1,000 and the median total funding exceeds 21bn EUR).

Table 1: The distribution of scaleup city clusters according to their main performance variables, 2021

Number of scaleups

Total funding of scaleups

(M EUR)

Number of scaleups
with market value
> 200 M EUR

Global scaleup cities (4 FUAs)

4,958

112,742

190

Top European scaleup cities (11 FUAs)

2,697

53,870

106

Top European emerging scaleup cities (24 FUAs)

1,985

35,117

68

Emerging scaleup cities (35 FUAs)

1,452

18,565

32

Regional scaleup cities (36 FUAs)

433

7,502

36

Local scaleup cities (56 FUAs)

947

7,560

0

Europe total (166 FUAs)

12,472

235,356

432

Source: Authors’ own calculation based on Dealroom data retrieved June 2, 2021, from https://dealroom.co and EU-OECD FUA classification. Design by authors.

In order to analyse the scaleups in Industry 4.0, we extended our research to the relevant technologies applied, and although the spectrum is relatively wide, a limited number of digital technologies can be considered as common denominators. According to Chiarello et al. (2018), Industry 4.0 includes and combines many hundreds of technologies that can be aggregated into more than thirty different technology fields and eight clusters of such technologies. Balland and Boschma (2021, 1654) distinguish ten technologies that belong to Industry 4.0, although some of them fall outside the realm of the merging of digital technologies and manufacturing, such as quantum computers and system integration. In contrast, Castellani et al. (2022) focuses only on the three decisive technologies of the Industry 4.0 they have the highest potential impact on manufacturing processes, such as advanced industrial robots (AIRs), additive manufacturing (AM) and industrial internet of things (IoT). Fernández-Macías et al. (2018) identifies five “game changing technologies” with potentially disruptive character in manufacturing over a time horizon of a decade to come, namely, advanced industrial robots (AIRs), additive manufacturing (AM), industrial internet of things (IoT), electric vehicles (EV) and industrial biotechnologies (IB). In order to improve Europe’s competitive advantages, the European Commission (2021) has launched the “Advanced Technologies for Industry” (ATI) project, which focuses on a total of sixteen technologies, including advanced manufacturing, artificial intelligence, augmented reality, big data, cloud technologies, internet of things, robotics and IT for security/cybersecurity, which are also often referred to as Industry 4.0 technologies.

Against this background, we have used the applied technologies as defining characteristics to identify the scaleups in Industry 4.0, and we retrieved from Dealroom database the scaleups located in Europe in the following four predefined categories of technologies: 3D technologies, augmented reality, internet of things, and autonomous and sensor technologies. For other important technologies that play a crucial role in Industry 4.0 but are also used widely in non-industrial applications, such as software as a service, we have limited the dataset to the scaleups operating with a business model manufacturing. Therefore, in the case of artificial intelligence and big data technologies, only scaleups that also use the manufacturing business model were included in the analysis. Similarly, the term robotics or robots is widely used in non-industrial contexts, especially as software robots, which are actually computer programs that work autonomously to perform virtual tasks such as “bots,” “chatbots,” “web crawlers” and Robotic Process Automation. In order to filter out the scaleups that deal with robots in physical form, we have limited our sample to scaleups in the robotics industry with the manufacturing business model.

By applying these thresholds and matching the scaleups that meet these criteria with the Functional Urban Areas of the 166 scaleup cities, we have retrieved a total of 1,328 Industry 4.0 scaleups with a total funding of 17.3 bn euros. These correspond to 10.5 percent of the total number and 7.3 percent of the total funding of scaleups in European scaleup cities.

4. EUROPE’S SCALEUP GEOGRAPHY IN LIGHT OF KEY PERFORMANCE INDICATORS[3]

As it is expected, the scaleup city landscape of Europe is dominated by the large Western European capitals that are at the same time also location for the Global and the Top European scaleup cities (see Fig. 3).

Combined with almost 5,000 scaleups, the Global scaleup cities —London, Paris, Berlin and Stockholm—concentrate 40 percent of scaleups of the European scaleup cities. The Top European scaleup cities, i.e., Barcelona, Copenhagen, Dublin, Helsinki, Madrid, Amsterdam, Munich, Cambridge, Manchester, Oxford, and Zurich, together with more than 2,500 scaleups, host a further 20 percent of European scaleups. As opposed to this, the 56 local scaleup cities, in which there are in total fewer than 1,000 scaleups, are responsible for only 7 percent of the scaleups in Europe.

It is striking in this territorial landscape that the fifteen scaleup cities of Central and Eastern Europe—i.e., Prague, Budapest, Tallinn, Vilnius, Gdansk, Poznan, Wroclaw, Cracow, Warsaw, Bucharest, Bratislava, Ljubljana, Riga, Sofia, and Zagreb—offer a total of only 443 scaleups a location which is equal to 3.5 percent of all European scaleups. Just for comparison, it is above the corresponding values of Dublin (339 scaleups) but below those of Stockholm (489). In a similar vein, major capitals in Southern Europe—Rome, Athens and Lisbon—have put together fewer than 130 scaleups, which is in line with the values of Lausanne or Edinburgh.

Figure 3: The number of scaleups across the European scaleup cities, 2021

Source: Authors’ own calculation based on Dealroom data retrieved June 2, 2021, from https://dealroom.co and EU-OECD FUA classification. Design by authors.

When it comes to the total funding of scaleups, however, the North-South and West-East divide is more pronounced (see Fig. 4). In the southern part of Europe, the scaleups only have a relatively high level of total funding in Barcelona (5bn EUR), Madrid (3.1bn EUR) and Milan (2bn EUR), while the funding raised by the scaleups in Rome, Athens and Lisbon together amounts to less than 1bn EUR. Nevertheless, these values are orders of magnitude below the values of London (57bn EUR), Paris (22bn EUR), Berlin (20bn EUR) and Stockholm (13bn EUR) and also below Amsterdam (7bn EUR) or Munich (7bn EUR). As the West-East divide, in Central and Eastern Europe the scaleups have notable total funding only in Bucharest (1.9bn EUR) and Tallinn (1bn EUR), while they receive less than 500 M EUR in the major capitals, such as in Warsaw, Prague, and Budapest, and less than 100 M EUR in Riga, Bratislava, and Ljubljana.