Mobility-as-a-Service: The Convergence of Automotive and Mobility Industries 3030755894, 9783030755898

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Management for Professionals

Malte Ackermann

Mobility-as-a-Service The Convergence of Automotive and Mobility Industries

Management for Professionals

The Springer series Management for Professionals comprises high-level business and management books for executives. The authors are experienced business professionals and renowned professors who combine scientific background, best practice, and entrepreneurial vision to provide powerful insights into how to achieve business excellence.

More information about this series at http://www.springer.com/series/10101

Malte Ackermann

Mobility-as-a-Service The Convergence of Automotive and Mobility Industries

Malte Ackermann Nuertingen-Geislingen University Geislingen, Germany

ISSN 2192-8096 ISSN 2192-810X (electronic) Management for Professionals ISBN 978-3-030-75589-8 ISBN 978-3-030-75590-4 (eBook) https://doi.org/10.1007/978-3-030-75590-4 # The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG. The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

For Ela und Linus

Preface

This book is the outcome of my personal journey. Many of the ideas and insights stem from extensive discussions over the last years, as well as my personal tenure with Daimler and car2go. Capturing and refining these ideas has been a journey in itself. With this book, I want to explore the general societal and economic trends that lead to a new understanding and perception of mobility as well as the factors that will shape its future. The spread of digital technologies and the emergence of numerous other disruptive forces have changed the rules of the industry. The business playbook that governed the automotive industry for decades does not apply any longer. Within this book, I explore all new forms of mobility and dissect its implications on businesses. Originally, I aimed to describe the rise of mobility-as-a-service through the lens of the automotive industry. While doing so, I realized that the automotive industry and the mobility industries are increasingly converging, even indistinguishable in the eyes of the customer. This entails tremendous implications for both industries. The underlying developments that spark the convergence are here to stay and will likely only accelerate in the future. One central insight emerged from my research and shaped the outcome of this book: I truly believe that we are at the very peak of cars and new mobility services are poised to take over. A book is always a collaborative learning process and I am especially grateful to Sebastian Ulm, Lars Engelbrecht, Enrico Howe, Stefan Rostek, and Fabrice Tronche. Numerous other people gave me valuable feedback and industry insights. I would like to thank Eric Hsu for encouraging me to write a book in the first place and my editor Prashanth Mahagaonkar for believing in my idea. Special thanks goes to my mother, Irene, who tirelessly and meticulously helped me with any spelling errors or grammatical mistakes. Finally, I would like to thank my wife, Marlies, for all her contributions throughout this project. Stuttgart, Germany March 2021

Malte Ackermann

vii

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1 5

2

Why Mobility-as-a-Service? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 The Digital Era . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2 A Game of Cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Generational Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Synthesis of Chap. 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

7 7 14 21 26 30

3

Shaping the Future of Mobility . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 The Rise of Ecosystems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Sustainability: A Wicked Problem . . . . . . . . . . . . . . . . . . . . . . . 3.3 A Flood of Money . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 Synthesis of Chap. 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . .

35 35 39 48 52 58

4

Forms of MaaS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Drive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Be-Driven . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Ecosystem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Autonomous Future . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. . . . . .

65 65 73 77 81 86

5

A Business Perspective . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 A Framework for MaaS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Assessing MaaS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

. 91 . 91 . 97 . 110

6

The Way Forward . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 Strategic Imperatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

113 113 124 128

ix

1

Introduction

Mobility is the lifeline of our modern civilization. The twentieth century has been called the century of the automobile, as cars have affected greatly the way we work, communicate, and live (Sumantran et al., 2017). Modern cities and urban developments are built in a fashion that cars are the central means of personal transportation. After WWII, urban sprawl increased in many western countries, the infamous suburb became the focal point of living and led to a culture of cars. It has enabled economic development and a great quality of life in many parts of the world. As economies grew, so did the number of cars. For example, the number of vehicles in the United States increased from 68 million in 1958 to more than 270 million in 2018 (Strategy&, 2019). In recent years, this car-centered policy has come under scrutiny as societies and consumers have increasingly become aware and alerted by the negative externalities that this policy is causing. Traffic congestion and emissions are growing in developing countries and remain high in developed countries. Already, younger generations tend to drive less, for instance, in Great Britain, the peak for 17–20 year olds holding a driver’s license occurred in 1994 at 44%. Afterward this figure started to trend downward, plateauing at around 30% in 2017 (SMMT, 2019). Owning a car has never been more expensive. Cars have become increasingly complex, as advanced driver-assistance systems (ADAS), stricter emissions standards, and entertainment functionalities inflated the bill of materials. In the United States, the average car in 1950 was sold at roughly 45 percent of the average annual family income; this number rose to around 61 in 2014 (Sumantran et al., 2017). In addition, governments made operating and owning a car more costly. Still car ownership remains high, in the EU-28 the average motorization rate in 2016 was 506 cars per 1000 inhabitants, approximately one car for every two persons. However, fundamental changes are in the air. The megatrends that transform the automotive industry are often abbreviated with ACES, which is short for autonomous, connected, electrification, and shared. Autonomous stands for self-driving autonomous vehicles, whereas connected refers to the connectivity of cars. Sending out and receiving information is a prerequisite for connecting the car to its surroundings, to pedestrians and infrastructure, but also # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Ackermann, Mobility-as-a-Service, Management for Professionals, https://doi.org/10.1007/978-3-030-75590-4_1

1

2

1 Introduction 372.1

400 300 200 100

42.3

0 2018

2026

Fig. 1.1 Global mobility-as-a-service market in billion USD (Source: based on Statista Mobility Services, 2020)

enabling entertainment functionalities. Via over-the-air (OTA) updates, cars can consistently be enhanced, similar to a smartphone. Electrification indicates the electrification of the powertrain, the transition from combustion-engine powered cars toward alternatives such as battery or fuel-cell electric vehicles. Shared refers to the sharing of vehicles, the evolution from ownership to car access. Further developments change the outlook of the industry. Urbanization is still going strong and digitalization has deeply infiltrated our modern life. It has changed the way people socialize and communicate as well as the consumption of products and services. A soaring public awareness for sustainability issues affects every aspect of the industry. The accumulation of all these social and technological changes substantiates the notion that the automotive industry is ripe for disruption. Interwoven with all the aforementioned developments is the simultaneous rise of mobility-as-a-service (MaaS). MaaS is the idea that transportation does not need to be either public, like trains, or personally owned, like cars. People have the choice to consume multiple forms of mobility. They can decide on what they want and how they want it, and as far away as their nearest smartphone. Today, cars can be shared, rented, or subscribed. New vehicle types such as e-scooters or e-bikes are added to the mix. MaaS is about combining several modes of transportation in a single digital venue. MaaS is a one-stop solution that includes services like renting out a car or an e-bike, but also additional services like purchasing a ticket and routing functionality. Ultimately, MaaS denotes the transition from ownership toward flexible access and opens up an entirely new industry. Mobility has become an individualized service. Figure 1.1 displays the global MaaS market in 2018 and the outlook for 2026. Although these numbers might be disputable, other studies reaffirm the notion that MaaS is destined for tremendous growth and this is the main reason why MaaS has caught the attention of media, academia, and industry insiders. The tremendous market potential is a magnet to investors; billions of dollars have been poured into mobility ventures within the last years. Yet, most of the funding went to the new kids on the block and not to the automotive establishment. Mobility providers like Uber, DiDi, Bolt, Tier, or Voi have soaked up remarkably large sums. Incumbents like GM, BMW, or Toyota with a decades-old but proven and profitable business model seem somewhat disproportionally discounted by investors. In order to ride with the wind of change, traditional players have started to venture out into

1

Introduction

3

these new mobility segments. Today, players from very diverse backgrounds, like original equipment manufacturers (hereafter: OEMs), suppliers, start-ups, tech, software, and regulators, populate the entire MaaS landscape. In the eyes of the consumers, these new services are highly welcome. In several markets around the globe, consumers are already shifting away from ownership toward mobility access (Simoudis, 2020). Younger generations are embracing modes like ride-hailing, carsharing, or micromobility and are willing to give up their personally owned car. Digitalization has torn apart traditional industry boundaries and the entry point to most services is the smartphone. This is the principal reason why consumers are detached from any distinction between automotive and mobility industry. For them, the question is who offers the best mobility solution for their personal situation. In consequence, automotive and mobility firms are increasingly fighting over the same set of customers. As these two industries are colluding, it comes to a clash of cultures, hierarchies, organizational practices, and ultimately mind-sets. The discrepancy is best exemplified by the different speeds the automotive and mobility industry operate in. Strategically, OEMs are used to think in years, whereas mobility firms think in months. Developing a car takes quite some time, although the average automotive product cycle time has decreased significantly. In 2000, the average was 8 years and has dropped to 6 years in 2010 (Deloitte, 2019). In 2020, the average automotive product life cycle is down to around 4 years. However, developing an electric-scooter takes only a couple of months. Due to large investment rounds, mobility providers are on the fast lane. It used to take companies an average of 20 years to reach a billion-dollar valuation, the major start-ups of today are getting there in four (World Economic Forum, 2016). Start-ups that are valued at $1 billion or more are called unicorn. The e-scooter provider Bird was the fastest ever start-up to reach unicorn status in just 15 months (INC Magazine, 2020). This is simply mind-boggling and a stark contrast to the automotive industry with its strong hardware legacy and organizational structures and processes that are streamlined for the development of time-consuming physical products (Everlin Piccinini et al., 2015). Advisory firms like BCG, Bain, or the like consistently try to paint the rosy picture of how automotive firms need to branch out into the mobility segments, as the market is poised to grow and apparently incredibly attractive. Figure 1.2, taken from a study by McKinsey does exactly this, they predict that OEMs could earn up to 30% of their revenue from mobility services by 2030. Until now, that seems to be inspired by wishful thinking (McKinsey&Co, 2017). Established players from the automotive industry have a very tough time in these new mobility segments. In February of 2019, Daimler and BMW created five joint ventures in carsharing, parking, electric vehicle charging, ride-hailing, and citymapping services, pledging to invest over $1 billion into these verticals (The Verge, 2019). The initial goal was to operate with their services in nearly 90 cities until the end of 2019 and expand consistently in the years after. Just by the end of 2019, Daimler and BMW reversed out of the expansion plans and even withdrew their carsharing operations out of North America entirely. Oliver Zipse, CEO of BMW, even went as far to announce that his company is an “aircraft manufacturer

4

1 Introduction

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

2 25

30

1 19 10 73 40

Vehicle Sales

2016 AV and EV contribution

Aftermarket

2030 Mobility as a Service

Fig. 1.2 Automotive revenue share in % (Source: based on McKinsey, 2017)

and not an airline” and that the company would focus instead on its core competency: manufacturing premium cars (Financial Times, 2019). Many automotive incumbents experienced similar tales. Ford acquired Chariot, an app-based shuttle service for $65 million in 2016, just to shut the service down two and half years later (Wired, 2020). General Motors announced “Maven” in January 2016 as a “personal mobility brand”, the company’s first major mobility initiative (CNBC, 2020). Maven was shut down in the beginning of 2020, naming financial troubles. Bosch, the German supplier giant, shut down Coup – a scooter sharing operation serving Berlin, Paris, and Madrid – by the end of 2019. Bosch announced it was getting out of the mobility services entirely, citing the lack of profitability as the main issue (Electrive.com, 2019). As of 2021, it seems that traditional players from the automotive industry have lost their appetite in the new mobility playing field. Services such as ride hailing or shuttling are mainly propelled by young companies. Recent innovations, such as the burgeoning micromobility, are entirely dominated by startups. The advent of MaaS and the disruption of the automotive industry are both overlapping and part of the same developments and thus raise a very fundamental question: are we at peak car? Exhibit 1.3 shows the horse population in millions in the United States. The 1920s were peak horse just before cars became a mass product (Barclays Research, 2015). The global market for new car sales reached a new all-time high of almost 75 million in 2019, and just a century later, we might just have seen the peak of cars. This book delves into the idea of how new mobility services might usher the end of the automotive era as we know it. It explores the economic, demographic, and social changes that lie beneath the shift toward mobility-as-a-service. This book examines MaaS from a business perspective, through the lens of automotive and mobility firms (Fig. 1.3). A number of insights that result from this profound transformation will be presented in this book. Chap. 2 examines the developments that gave rise to mobility-as-a-service in the first place. The main ingredients are digitalization,

References

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30 25 20 15 22

10 5 0

25

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5 1850

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1870

1900

1910

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14 1940

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3 1960

4 2000

Fig. 1.3 US horse population in million (Source: based on Barclays Research, 2015)

urbanization, and changing generations. Chap. 3 explores the forces that will shape the future of mobility, namely the impact of digital ecosystems, the complexities and multiple layers of sustainability, and how markets flooded with capital have a lasting impact in both industries on various levels. After laying the groundwork, Chap. 4 articulates how MaaS looks today and illustrates all current forms of mobility, the ones we see in the markets today as well its autonomous future. In Chap. 5, I conceptualize MaaS as a framework, portray the implications for firms, and describe the changing value chains. Further, I characterize the major challenges for establishing and sustaining new mobility services. Based on these insights, I advance strategic imperatives in Chap. 6, on what it takes to win in such disruptive times.

References Barclays Research. (2015). Disruptive mobility: AV deployment risks and possibilities. CNBC. (2020). General motors shutting down Maven car-sharing operations. https://www.cnbc. com/2020/04/21/general-motors-shutting-down-maven-car-sharing-operations.html Deloitte. (2019). Autonomous driving–moonshot project with quantum leap from hardware to software & AI Focus. Electrive.com. (2019). Bosch discontinues Coup scooter sharing–electrive.com. https://www. electrive.com/2019/11/25/bosch-discontinues-coup-scooter-sharing/ Everlin Piccinini et al. (2015). Transforming industrial business: The impact of digital transformation on automotive organizations. Thirty Sixth International Conference on Information Systems. Financial Times. (2019). German automakers do U-turn on car-sharing push. https://www.ft.com/ content/81a5030c-1a64-11ea-97df-cc63de1d73f4 INC Magazine. (2020, August 17). 14 Months, 120 Cities, $2 Billion: There’s never been a company like bird. Is the world ready? | Inc.com. https://www.inc.com/magazine/201902/ will-yakowicz/bird-electric-scooter-travis-vanderzanden-2018-company-of-the-year.html McKinsey & Co. (2017). The automotive revolution is speeding up. Simoudis, E. (2020). Transportation transformation: How autonomous mobility will fuel new value chains. Corporate Innovators LLC. SMMT. (2019). UK automotive sustainability report 2019. Strategy&. (2019). An ecosystem approach to reducing congestion.

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Sumantran, V., Fine, C., & Gonsalvez, D. (2017). Faster, smarter, greener: The future of the car and urban mobility. MIT Press. https://ebookcentral.proquest.com/lib/gbv/detail.action? docID¼5103882 The Verge. (2019). BMW and Daimler will spend over $1 billion on the future of transportation. https://www.theverge.com/2019/2/22/18235941/daimler-bmw-mobility-joint-venture-billiondollars Wired. (2020, July 18). Ford shuts down its chariot shuttle service. https://www.wired.com/story/ ford-axes-chariot-mobility-is-hard/ World Economic Forum (2016). Digital transformation of industries. https://s2.q4cdn.com/ 470004039/files/doc_financials/2019/ar/_10-K-2019-(As-Filed).pdf

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Why Mobility-as-a-Service?

2.1

The Digital Era

Digitalization or digital transformation is the cause of large-scale and sweeping metamorphoses across multiple aspects of society and business (Raskino, 2015). Digital technologies have become immersed in people’s daily life, influencing communication, social interaction, and consumer behavior. They are fundamentally altering processes, services, and products, and established organizations have to change the way how to conduct business. Here, digitalization and digital transformation are considered synonymously and the focus is on the outcomes and implications of the digital transformation. The common denominator of the digital era is data, they even have to be regarded as a critical resource. As such, data are no natural resources like water or oil, they are produced by humans and they are capital good (OECD, 2015). They can be re-used limitlessly and enable economies of scope and scale. By its very nature, they do not inhibit intrinsic value. Data become valuable when information can be extracted, but such information is highly dependent on the context (Rogers, 2016). For organizations, it is key to master volume, variety, velocity, and quality of data. A key enabler is the smartphone, it has become so ubiquitous, small, and inexpensive that over a third of the global population owns a smartphone. This device is central to consumption and data collection, while connecting to other devices, infrastructure, and environments. Digitalization can be regarded as a general-purpose technology that is continuously reinventing itself and boosting productivity across all sectors and industries. Only three previous technological innovations are considered as general-purpose as well: the printing press, the steam engine, and the electricity generator. These technologies were revolutionary and highly disruptive to societies (Valenduc & Vendramin, 2017). The digital era is not bound to a single technology, it relies on and is driven by a set of interdependent technologies. In the following, five key technologies are briefly introduced: (1) Internet of Things (IoT) is a system of interrelated physical devices capable of gathering and sharing electronic information, which are connected with the Internet Protocol (IP). The main idea is that all # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Ackermann, Mobility-as-a-Service, Management for Professionals, https://doi.org/10.1007/978-3-030-75590-4_2

7

8

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Why Mobility-as-a-Service?

devices, independent of usage, industry, or country, can be linked, thus enabling gains in efficiency and transparency. They reduce friction and information asymmetries and enable new economic potential. These devices become “smart” and facilitate machine-to-machine (M2M) communications. IoT spans multiple technologies like machine learning, embedded systems, the process of automation, and real-time analytics (Rifkin, 2015). (2) 5G Networks, the newest generation of wireless networks enables higher speeds, up to 200 times faster than 4G, and faster data transfer. 5G coverage is a fundamental cornerstone for autonomous driving, enabling the vehicles to communicate with infrastructure and other vehicles (Herrmann et al., 2018). (3) Cloud computing is a model that provides a flexible, on-demand access to a range of services, such as storage, networking, and computing power. Only a single entry point is needed, e.g., a tablet. It increases availability, ubiquity, affordability, and capacity and thus facilitates other digital technologies, such as (4) big data analytics. Big data refers to data characterized by high variety, velocity, and volume. It often stems from multiple sources with multiple formats. Data itself might exhibit some value, but real value generation occurs with the capacity to extract information from different sources in large quantities. Data can be categorized as structured or unstructured data. Structured refers to numeric data, such as a birthday or a name. Unstructured data, which vastly surpasses structured data in terms of volume, is unorganized without a pre-determined format, e.g., a picture on Instagram. (5) Artificial intelligence (AI) refers to the ability of machines to acquire and apply knowledge within a range of cognitive tasks, such as sensing, learning, language processing, decision making, or predictions. An advanced AI is the main prerequisite for autonomous driving (Deloitte, 2019). Other potentially impactful technologies include 3-D printing, blockchain, robotics, and quantum computing (Radziwill, 2020). The combination of all these aforementioned technologies amplifies the advancement of business and society. For example, Volkswagen’s industrial cloud was designed in partnership with Amazon Web Services and will link globally more than 120 factories with 1500 suppliers and their approximately 30,000 plants (Automotive News Europe, 2020). This IoT project relies on cloud computing, big data analytics, AI, and 5G. Technology is the multiplier as digital transformation transcends a single technology. Digitalization comprises several key properties which entail extensive implications for business and society (OECD, 2019d). In the following, selected properties that have far-reaching consequences for the automotive and mobility industry are described in detail. Scale without mass: Physical products do have high fixed costs and substantial marginal costs that decline with scale, e.g., cars. Digital products and services have marginal costs close to zero and the ability to reach a global audience without accumulating a lot of mass. Mass could be employees, tangible assets, or geographical footprint. Firms, which pursue such a business model, are called asset-light (Sacolick, 2017). For instance, the messaging company WhatsApp was founded in 2009 and was able to reach 400 million active users by the end of 2013. The company was acquired by Facebook for $19 billion in 2014, with just over 70 employees (Reuters, 2020a). Jeremy Rifkin put forward the idea of a “zero

2.1 The Digital Era 0

9 500,000

1,000,000

1,500,000

2,000,000

Netflix

2,343,773

Apple Facebook

1,899,051 1,573,072 1,361,298

Alphabet Microsoft eBay

877,393 812,030 766,034

PayPal Amazon

2,500,000

351,531

Fig. 2.1 Revenue per employee in 2019 (Source: annual reports and public statements, 2020)

marginal cost society”, implying that digitalization leads to an era of nearly free goods and services, called global collaborative commons. The replication of YouTube videos is virtually zero and content is often free of charge, e.g., tutorials on learning how to program with Python or on a yoga practice. For example, with Airbnb, the cost of adding another room is near zero, while traditional players face substantial cost by adding another physical accommodation, e.g., a hotel room. The long-term effects on society and economy are currently unclear and fiercely debated among economists. Scaling without mass can be extremely profitable; Fig. 2.1 shows the revenue per employee of selected tech companies in 2019 in USD. Just for comparison, Netflix with its asset-light business model boasts a much higher revenue per employee than Amazon, which relies heavily on manual labor. A new scope: The digital environment has led to a new understanding of economies of scope. Traditionally, large firms could achieve lower marginal costs through the production of complementary goods and services (Teece, 1980). In this context, economies of scope imply that producing one good reduces the production cost of another related good. Conglomerates like General Electric or Philipps could share common costs such as legal, accounting, or finance while venturing out in unrelated business segments. In the digital era, economies of scope might be understood as the creation of complex products that combine many functions and features, which were separated before. A smartphone combines telephony, navigation, music, videos, and many more functions, meaning that industry boundaries become blurred (McKinsey&Co, 2019b). This functional expansion allows digital firms to realize economies of scope across products, industries, and even entire firms. Apple builds computers and mobile phones but does also tap into several traditionally unrelated industries such as books, music, videos, games, podcasts, cloud computing, and payment. Speed: All social and economic activities are accelerated through digitalization. Ideas spread more quickly, time-to-market shrinks, and information is ubiquitous (Nadkarni & Prügl, 2020). Traditionally competitive advantage originated in sheer firm size, market-entry barriers, or image and brand. Today, competitive advantage is increasingly derived from a first-mover or fast-follower practice. Organizations of all industries try to mimic agile principles, relying on rapid and iterative learning on

10

2

Why Mobility-as-a-Service?

markets and customers (Mishra & Ranjan, 2019). The promise of move fast and break things becomes de rigueur (Taplin, 2017). As markets and investors are flushed with capital (see Sect. 3.4), firms aim to achieve scale before profits, which is aided by near zero marginal costs of digital goods (OECD, 2019d). Examples for this type of behavior can be found in any industry, for instance, in the financial sector fin-techs (which are start-ups in the financial sector) like Revolut in the UK or N26 in Germany. Companies like these have rather little mass, minimal marginal costs, and aim to expand rapidly before other competitors can occupy market space. One way to quantify how digitalization is speeding up the rate of change is by measuring the entry and exit of firms in the S&P 500 index. In 1958, firms listed in the S&P 500 remained in the index for an average of 61 years. By 2011, the average stay had plummeted to 18 years (Harvard Business Review, 2017). Transformation of space: Software, data, and digital tools are intangible assets which can be utilized from principally anywhere. This decouples digital products from traditional borders and challenges values of territoriality and sovereignty. In theory, digital services such as video streaming could be served from one country to the entire world via the internet. The decoupling also creates opportunities for jurisdictional arbitrage. In Europe, Google was employing the infamous Double Irish, Dutch sandwich tax scheme for many years (Reuters, 2020b). Two separate Irish entities (one incepted in the Islands of Bermuda) and two Dutch holdings allowed Google to shift their intellectual property freely throughout Europe and consequently pay minimal tax in the US and overseas. This transformation of space suggests that countries which traditionally acted as borders of competition have lost their relevance. Moreover, this development adds significant competitive pressure. A company like Spotify with its value proposition (streaming of music and other contents) is basically forced to act and think globally right from its inception as competitors would simply eat up the business model if it does not reach a sizeable mass. This phenomenon is labeled “born globals” (Cavusgil & Knight, 2009). Traditionally firms would serve years in their domestic markets before going abroad. Firms that conduct business in the digital era are not rewarded with that luxury; they have to quickly envision an international agenda. This opens up opportunities, but also accelerates the speed of business itself. End-to-end empowerment: The internet lays ground for an “end-to-end” principle. Individual users can communicate with many others, reducing information asymmetries and sidestepping hierarchical structures of processing information, for example, as in a newspaper (OECD, 2019a). The functional decentralization leads to the empowerment of users and networks. Individuals can become their own publishers (e.g., via Twitter) or entrepreneurs can directly source potential suppliers globally. Consumers can innovate, construct, and design their own communities and networks. This digital era might lead to new, flexible forms of organizations, constructed from an array of quasi-independent individuals and enterprises. Especially start-ups are increasingly constituted by a rather small core team and rely on contractors, freelancers, and consultants. The disintegration and recombination of value could further reduce the distinction between categories such as consumer and

2.1 The Digital Era

11

business, work, and leisure. The pandemic of 2020 and 2021 will certainly accelerate these developments. Rise of ecosystems: The empowered end-user, the transformation of space, and low transaction costs promote the surge of platforms and ecosystems. Such ecosystems span combinations of services, operating systems, and applications across industry boundaries (OECD, 2019b). Ecosystems are further explored in Sect. 3.1. Creation and delivery of value: Digitalization has changed the way value is created and delivered. Several themes within this domain can be identified. X-as-aservice, which stands for anything-as-a-service, implies that in principle all services and business activities can be provided via cloud-based computing (Andriole, 2012). Also known as everything-as-a-service, it displays the vast potential that any product can become a service. Firms like Atlassian started to get rid of sales departments; customers would simply order the products online for themselves. The most common examples are software-, infrastructure-, or platforms-as-a-service, but even the most tangible products are turned into services. For instance, Rolls-Royce from the UK is leasing out its engines on airplanes. Simply selling products seems to be of the past, as firms realized that turning products into services could yield greater profits. The Sharing Economy is based on the assumption that peers are sharing goods and services, often via commercial platforms (Codagnone et al., 2019). Several companies have started out with pure peer-to-peer functionality, but quite often commercial users have replaced many peers, prominent showcases for this development are Airbnb and Uber. Airbnb started with home-sharing offerings, but today the actual peer-to-peer offering represents only a fraction of the entire business, which is dominated by commercial owners (The New York Times, 2020). End of Ownership implies the replacement of product ownership with subscription of services and goods (Perzanowski & Schultz, 2016). Movies used to be bought or rented, today entire movie catalogs can be streamed via dedicated platforms. This changes the relationship between consumer and products fundamentally, as physical products are sought to exhibit higher value appreciation. This means it is more difficult to demonstrate value in digital products to consumers. Individuals who previously owned a music record in the form of a vinyl or CD now simply stream the service, yet they will value the individual vinyl much higher than the streaming of one particular song. Aggregation involves the aggregation of information, services, and goods via digital channels. Google news aggregates information from thousands of magazines and publishers in many languages. There have been several high-profile jurisdictional controversies surrounding the service, as publishers feel that Google infringes on their intellectual property (CNN, 2020). The Google news case highlights two major developments. First, anything which can be potentially aggregated will be aggregated. Second, aggregation implies that only very few players will come out on top and today ownership of value is marked by blurred lines. Specific tasks or even entire jobs which are of temporary nature make up the Gig Economy (Ruyter & Brown, 2019). Instead of (full-time) employees, businesses are hiring freelancers, self-employed, or independent contractors. Payment is usually based on completed assignments, tasks, or sales

12 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

2

Why Mobility-as-a-Service? 80%

40%

5%

8%

1977: 100

1981: 50.000

15%

2008: 1-10 Mio.

Today: 100 Mio.

Tomorrow: 200-300 Mio.

Fig. 2.2 Software in cars: share of total value added and lines of code (Source: adapted from Wallmüller, 2017)

enabled via digital platforms. On the upside, these flexible and customizable arrangements can benefit consumers, workers, and firms. On the downside, it might lead to an erosion of employees’ rights as the economic relationship between workers and business is purely based on demand and supply. All aforementioned themes are highly interlinked and on an aggregated level, they profoundly redefine our understanding and perception of value creation. Digitalization has taken on the automotive and mobility industry by storm, virtually all strategic directions, projects, and processes revolve around software and data. Added value is increasingly derived through software. Today the total value added in modern cars is at around 40% and can be expected to increase to be at around 80% in the years to come, see Fig. 2.2 (Wallmüller, 2017). While the exact figure might be up for discussion, it is undisputed that competitive advantage and innovation in the industry will largely rely on software in the future. Various trends such as connectivity, autonomous vehicles, direct sales of OEMs to customers, the emergence of subscription models, personalization and customization, sharing, digital financing, and leasing all contribute to this very notion. Certainly, software is not entirely new to cars, yet from the introduction of the first line of code that was used for electronic spark timing in 1977 for General Motors’s Tornado model, the automotive industry has come a long way in the last five decades (IEEE Spectrum, 2009). Modern cars are already a complex combination between software and hardware. Various sources estimate that modern cars comprehend around 100 million lines of code, see Fig. 2.3 (McCandless, 2014). By comparison, the operating system Android contains around 12 million while a Boing 787 flies around with 14 million lines of code in it. As such, lines of code are a rather suboptimal measurement for the capability and efficiency of a system, put simply, often more can be done with less. Vehicles today come with increasingly sophisticated technologies, including advanced driver-assistance systems (known as ADAS). These electronic systems support drivers in driving and parking through sensors and cameras to detect driver errors and obstacles. The fact that a modern car contains around 100 million lines of code does not entirely speak for its complexity, it can be attributed to two main reasons. First,

2.1 The Digital Era

13

Lines of code in million Hubble Space Telescope Android Boing 787 Windows XP - 2001 Facebook Software in a modern car Mouse - total DNA basepair in genome

2 12 14 40 62 100 120

Fig. 2.3 Lines of code in million (Source: based on McCandless, 2014) 379 261 147 96

55

81

156 103

107

131

291 233 147

159

57

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Fig. 2.4 Recalls quota for the United States from 2005 to 2019, in percent (Source: based on CAM, 2020)

complex and highly decentralized value and supply chains that often span several different companies lead to an increase in complexity. In the past, OEMs have often disregarded software as strategic assets and therefore heavily outsourced software development; only around 2–5% of software is developed in-house (The Economist, 2020). Second, the software development process is not state-of-the-art in the automotive industry (McKinsey&Co, 2019a). The rise of agile methods such as Scrum and Kanban has certainly resonated within the industry, but the development of cars is often confined by methods such as the V-cycle (Blyler, 2013). The V-cycle splits the software development process into two phases: verification and validation. Each phase consists of several steps and each step of the development process entails a corresponding testing process. Pictured graphically the model resembles a V with the left side representing the gathering of requirements and specifications and the right side representing the integration of parts and their validation. This model-based approach is well established and allows for clarity in complex situations, yet it lacks agility and responsiveness to customer needs. In that vein, most established OEMs and suppliers are – in terms of organizational structures, processes, and approaches—still very hardware-minded. As cars become software products and life-cycles become shorter, all companies within the industry are facing this mounting pressure. The step surge of recalls of cars sums up this challenge best, Fig. 2.4 shows the recalls quota (in percent) for the United States from 2005 to 2019 (Center of Automotive Management, 2020). The quota is calculated by the proportion of

14

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Why Mobility-as-a-Service?

new car sales to recalls, for instance in 2011 the number is 103%, meaning that slightly more recalls for cars were issued than sold. The number of recalls has grown strongly, even as the entire US car market has seen record numbers of new sales within the last years (Goodcarbadcar, 2020). The increasing number of recalls underscores the intricacy in building modern cars and the need for reinvention as well as the susceptibility for disruption by industry outsiders. Volkswagen, for example, wants to reinvent itself as a software company and has publically announced to develop at least 60% of its software in-house (Reuters, 2019). This seems as a steep climb, as the company estimates that as of 2019 around 10% of software are developed in-house. The trend toward a greater software emphasis can be observed to varying degrees across all OEMs and suppliers. Mobility companies do not have to deal with the immersive complexity of a car, which allows for greater agility and adaptability. Start-ups such as Tier or unu motors rather resemble software than manufacturing or hardware companies.

2.2

A Game of Cities

We live in times of urbanization. By the end of the nineteenth century, the world was mostly rural, slightly more than a century later the world was mostly urban (United Nations, 2018). In regards to economic power, population, and creative output, urbanized agglomerations stand for localized peaks of human activity. Historically, cities have been the powerhouses of economic development and the focal points of social and cultural exchange (Dastbaz et al., 2018). Urban areas are the major contributors to the global gross domestic product (GDP) which is the total monetary value of all finished goods and services (Sumantran et al., 2017). In the EU, over two-thirds of the population live in urban areas and create up to 85% of Europe’s GDP. The simplest and most forward reason why humans are drawn to urban agglomerations is the creation of jobs. Cities serve as catalysts for social activities, creativity, and innovation. The expansion of urban agglomerations enables further economic growth and reduces poverty in many developing parts of the world. Urbanization is being regarded as one of the biggest growth engines of this century (McKinsey&Co, 2011). The world population has grown at an astonishing pace in the last decades. In 1928, the world population was around 2 billion people, less than a millennium later the world population is around 8 billion. Figure 2.5 displays the global population divided into rural and urban (United Nations, 2018). The year 2010 represents a tipping point for humanity, because for the first time in history more than half of the world population was living in urban areas. The United Nations estimates the global rural population in 2020 around 3.4 billion and the urban population to be 4.4 billion. They forecast that the rural population will decrease to around 3.1 billion by 2050, while the urban population is then expected to be around 6.7 billion. The epitome of urbanization is the emergence of superstar cities like San Francisco, New York, London, Munich, Stockholm, Shenzhen, Tokyo, or Sydney.

2.2 A Game of Cities

15

Popluation in Bilion

Rural

Urban

10 000 000 8 000 000 6 000 000 4 000 000 2 000 000 0

Fig. 2.5 Global rural and urban population (Source: based on United Nations, 2018)

In terms of income, education, housing prices, values, age, or wealth, they differ greatly from domestic rural areas. In the EU and the United States, people in the urban areas tend to be younger and better educated. For example, in 2015, in the age groups 30 to 34, around 28% of the EU-28’s rural population had tertiary education, compared to around 41% in urban areas (Eurostat, 2019). The urban-rural education disparity is particularly pronounced in China, with its astonishing economic growth, where the disparity has even grown (Zhang, 2017). This puts the rural population into a “poverty trap”, since education investment and school quality are inferior. Even with migration to urban areas, the Chinese household registration system (called hukou system) limits the access to better education in cities, which further cements the rural-urban divide. Innovative jobs tend to be clustered in very few areas, which in turn attract the most innovative people. In the twentieth century, Western economies were largely driven by manufacturing jobs, which relied on cheap labor input to reach economies of scale and scope. Consequently, jobs and income equality were rather equally distributed. Innovative or tech jobs tend to cluster in cities with universities, research institutions, and even competitors. The United States makes up for around 4% of the world’s population while generating approximately 24% of the global economic output. Around half of this entire output stems from just 23 metropolitan areas. To put things into perspective, New York has a GDP of $1.7 trillion (2019), which equals the economic output of all rural areas in the United States combined. According to a study by the Brookings Institute (2019), within the innovation sector, which is composed of the 13 highest-tech sectors such as software, semiconductor’s or pharmaceuticals, nine out of ten jobs created between 2005 and 2017 were based solely in Seattle, Boston, San Diego, San Francisco, and Silicon Valley (Robert D. Atkinson et al., 2019). In the knowledge economy, we live in, starting up a company tends to be centered in certain places, too. According to StartupBlink, the best ten cities to start a company in 2020 are: (1) San Francisco Bay, (2) New York, (3) London, (4) Boston Area, (5) Los Angeles Area, (6) Beijing, (7) Tel Aviv Area, (8) Berlin, (9) Moscow, and (10) Shanghai (Startupblink, 2020). These cities are the political and economic centers of their home countries. With regard to the language spoken, education, or even values, these urban hubs are converging and interlinked due to the advancement of digital technologies. This concentration of companies,

16

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Why Mobility-as-a-Service?

Table 2.1 Top ten cities in terms of GDP and GDP growth by 2035 (Source: based on WEF, 2019) 2035 GDP in Tr. $US City 1 New York 2 Tokyo 3 Los Angeles 4 London 5 Shanghai 6 Beijing 7 Paris 8 Chicago 9 Guangzhou 10 Shenzhen

Country U.S Japan U.S. U.K China China France U.S. China China

in $T. 2.5 1.9 1.5 1.3 1.3 1.1 1.1 1 0.9 0.9

Annual growth GDP in % until 2035 City Country 1 Bengaluru India 2 Dhaka Bangladesh 3 Mumbai India 4 Delhi India 5 Shenzhen China 6 Jakarta Indonesia 7 Manila Philippines 8 Tianjin China 9 Shanghai China 10 Chongqing China

in % 8.5 7.6 6.6 5.3 5.2 5.2 5.2 5.1 5 4.9

talent, and loose monetary policies have led to sky-high housing cost in these agglomerations, yet people are still drawn to urban areas. The metabolism of cities contains economically and socially favorable outcomes but also several negative externalities. Urban centers create extraordinary amounts of waste, exemplified by the case of Germany, where 320,000 coffee-to-go paper cups are consumed daily in cities (Gassmann et al., 2019). Urban areas carry a large burden and face several challenges due to decades of car-centered policy. Car parking and roads take up valuable space, for instance in Stockholm, Sweden, approximately 50% of the city’s space is allocated to roads and car parking (EY, 2020). The case of Basel, Switzerland, further indicates the inefficient space utilization, as it has 69,000 private and 31,000 public parking spaces in a city with only 57,000 registered cars (Gassmann et al., 2019). Noise and air quality are among the chief negative externalities of car-centered urban mobility. Air pollution levels in major cities in Europe and North America often exceed recommended safety levels. In China, the situation seems even dire, in multiple agglomerations the pollution is a serious concern for public health (Song et al., 2017). In the last years, Chinese administrations have made multiple commitments to tackle this problem and have achieved promising results. For example, the annual deaths related to air pollution in China peaked in 2013 and dropped below 1990 levels by 2017 (Yin et al., 2020). Mega-cities, typically with a population of more than ten million inhabitants, are on the rise. There were 43 cities with 5 to ten million inhabitants in 2014 and their numbers will increase to 63 cities (Dastbaz et al., 2018). This surge is primarily taking place in developing nations, and underscores the tectonic shifts in global power balance. The McKinsey Global Institute estimates that in 2025, 440 of the world’s 600 most economically significant cities will be in developing countries. Table 2.1 estimates the ten most economically powerful and growing cities in the world by 2035 according to the World Economic Forum (World Economic Forum, 2019). Out of the top ten measured by GDP, the split between Western and Asian seems even, as the one half is located (three US, one UK, one France) in the Western

2.2 A Game of Cities

17

hemisphere and the other (one Japan, four China) in Asia. A different picture emerges, when we look at the annual growth rates, as all of the top ten countries are located in Asia. It seems pretty reasonable to assume that the economic growth engine of the world in the upcoming years will be located in the emerging countries in Asia. To continue, in the years to come the most populous cities will be predominately found in emerging countries, most notably in Asia and Africa. The World Economic Forum expects the four largest cities in 2035 to be: (1) Jakarta in Indonesia with 38 million, (2) Tokyo in Japan with 37.8 million, (3) Chongqing in China with 32.2, and (4) Dhaka in Bangladesh with 32.2 million inhabitants. These megacities will become economic powerhouses but require holistic mobility concepts to accommodate these stunning population sizes. Accelerating urbanization leads to an overloading of infrastructure. A prominent proxy for overloaded infrastructure is the level of congestion. For example, in Paris, the average person spends around 65 hours in traffic per year. The TomTom City Index provides a good estimate on how megacities and its inhabitants suffer from congestions (TOMTOM, 2020). Table 2.2 shows the most congested cities on a global level. A congestion level of 53% in Jakarta implies that a 30-minute trip will take 53% more time than it would throughout Jakarta’s uncongested baseline conditions. Thus, during rush hour in Jakarta, a 30 minutes trip will result in 45.9 minutes average travel time (0.53*30 + 30 min). According to Table 2.2, the stress of congestion hits cities in developing countries the hardest. The predominance of developing countries accentuates their rapid urban expansion as the infrastructure is not up to par with the growth. Among the 40 cities with the highest congestion levels, only three are located in Western Europe (Dublin at no. 17, Athens at no. 30, Edinburg at no. 33) and two are from North America (L.A. at no.31 and Vancouver at no. 40). Herrmann et al. (2018) specifically address the implications of congestion for the United States. They calculate that in 2020 an estimated 14.4 billion liters of fuel is wasted due to congestion. The price the society has to pay is equally impressive, they estimated that the aggregated congestion-related delays amount to $192 billion per year. For the individual car commuter, this means an additional $1100 per year. Furthermore, they show that all these figures have risen dramatically over the course of the last three decades. The costs of congestion in Europe are equally high, estimated at around $150 billion annually, roughly equivalent to 1% of the EU’s GDP (European Commission, 2020). Congestion is a prime example for the so-called tragedy of the commons, the challenge of common resources (Gassmann et al., 2019). In the case of commonly owned goods, individuals are acting independently to their own self-interest but at the expense of other individuals. This can lead to overconsumption of the general resource. In many cities, residents favor their private vehicle over public transportation since they enjoy increased flexibility, freedom, and time-savings. Consequently, more individuals are using private vehicles, but the city and its residents suffer from higher congestion levels and environmental pollution. Several measures and incentive programs to counteract the tragedy of the commons have been introduced, yet most could only lessen the

Rank 1 2 3 4 5 6 7 8 9 10

City Bengaluru Manila Bogota Mumbai Pune Moscow region Lima New Delhi Istanbul Jakarta

Country India Philippines Colombia India India Russia Peru India Turkey Indonesia

Congestion 0.71 0.71 0.68 0.65 0.59 0.59 0.57 0.56 0.55 0.53

Rank 11 12 13 14 15 16 17 18 19 20

Table 2.2 Most congested cities in the world in 2020 (Source: based on TomTom, 2020) City Bangkok Kyiv Mexico City Bucharest Recife S. Petersburg Dublin Odessa Lodz Rio de Janeiro

Country Thailand Ukraine Mexico Romania Brazil Russia Ireland Ukraine Poland Brazil

Congestion 0.53 0.53 0.52 0.52 0.5 0.49 0.48 0.47 0.47 0.46

18 2 Why Mobility-as-a-Service?

2.2 A Game of Cities

19

burden, not solve it entirely. A prominent and simple example to offset these behaviors was the introduction of the high occupancy vehicle (HOV) in California in the 1970s (Kwon & Varaiya, 2008). Most commonly, one restricted traffic lane is reserved for the exclusive use of vehicles with at least two passengers in order to incentive trip sharing. Addressing the challenges of urbanization necessitates investment in public transportation and infrastructure, yet several Western countries face a lack thereof. The United States in particular faces striking underinvestment in roads, tunnels, and bridges. Approximately 33 percent of roads are in mediocre or poor condition and around 24 percent of bridges are structurally deficient or functionally obsolete (Strategy&, 2019). Around 25 percent of rail transit infrastructure and 40 percent of buses are in marginal or poor condition. An estimated $836 billion are needed to meet the backlog of required investments for a functioning infrastructure. The same goes for the European Union, investments in infrastructure have been declining since 2009 and remain lower than before the financial crisis in 2008 (Zachariadis, 2018). China, on the contrary, has invested comparably more, as the national government regards investments in infrastructure as prerequisites for economic growth (Guo & Shi, 2018). Apart from rapid urbanization and industrialization, an incentive system motivates local officials to pursue infrastructure investments. The industrialized cities of China possess much better roads and public transport than comparable economic powerhouses in the West. For instance, the envy of the world, the Silicon Valley, has surprisingly poor public transportation and suffers badly from congestion, given its incredible wealth (Government Technology, 2020). A prominent and apparent magic bullet to tackle underinvested infrastructure, mitigate negative externalities of urban developments, and stimulate growth is by making them “smart”. The concept of a smart city has enjoyed tremendous attraction in media and scientific literature. It is a rather fuzzy concept, and “smart” has often been circumscribed with alternative adjectives such as “digital”, “connected”, or “intelligent” (Albino et al., 2015). Cities are increasingly becoming better at automating routine functions like connecting traffic systems, but truly smart implies to understand, evaluate, and monitor urban areas in a way that improves the quality of life for its citizens in real time (Batty et al., 2012). A prerequisite for a smart city is the availability of data in order to integrate and connect intelligent functions. Put simply, a smart city hails the promise of technology-enabled infrastructure. The goal is to integrate digital technologies into the context of urban development and achieve a holistic and real-time data-driven view of cities. It is enabled by the Internet of Things, linking once stand-alone objects through digitalization with other objects and technologies, facilitating communication between the objects. Batty et al. propose that smart cities span across six key areas (Batty et al., 2012). Figure 2.6 exhibits these areas and the underlying major themes. The concept of smart cities aims at transforming all six key areas: economy, people, governance, mobility, environment, and living into “smart” (Lassen et al., 2020). Smart environment focuses on managing the input and output of resources in a sustainable fashion. Objectives include minimizing emissions, persevering, and strengthening green areas for people and animals alike. A smart economy aims at

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Why Mobility-as-a-Service?

ECONOMY Competitiveness

PEOPLE - Social and Human Capital

GOVERNANCE Participation

• Flexibility of markets • Innovation & entrepreneurship • Willingness for change • Network embeddedness

• Level of qualification • Flexibility & creativity • Social and ethnic inclusiveness • Participation in city life

• Involvement of people • Transparency of governance • Level of public services

MOBILITY - Transport

ENVIRONMENT Natural Resources

LIVING - Quality of Life

• Sustainable transportation • Availability & accessibility • Safety • Inclusive transportation

• Sustainable resource management • Energy efficiency • Controlling pollution

• Social cohesion • Security • Culture • Healthcare • Attractiveness of city

Fig. 2.6 The smart city concept (Source: based on Batty et al., 2012)

increasing the competitiveness of cities, as in today’s knowledge economy competition is often based on human capital and not machinery. This goes hand in hand with the idea to attract talent, the notion of smart people. The objective is to empower people to develop their true potential in order to increase the social capital of a city (Gassmann et al., 2019). Smart governance aims for transparency in decisionmaking processes but also the enablement of citizens to participate in the city’s discourse. Living smart includes varied themes such as security, access to cultural amenities, affordable healthcare, and overall attractiveness of the city with the overarching purpose of strengthening the quality of life for its citizens (Calvo, 2020). Smart mobility compromises a variety of solutions and concepts, such as interconnected traffic and parking systems and real-time congestion forecasts. It should include and enable all social classes to participate in mobility solutions. The concepts of smart mobility and mobility-as-a-service are nearly congruent and share several commonalities. Differences are rather based on their heritage, smart mobility is deeply embedded in academia, most notably transportation, while MaaS originates in business. Especially in Western countries, the notion of smart and digitally enabled cities is a departure from traditional urban development, as it challenges developers and policy makers on several levels. A smart city often comes across as a bit of a potpourri of trendy buzzwords, yet it merely mirrors the interwoven complexities of modern urbanization mixed with digitalization. Several smart city concepts are under-way in Europe and North America; the aim is mainly to transform or upgrade existing cities. In other parts of the world, several smart city concepts are built from scratch. The most ambitious project, called NEOM, can be found in Saudi Arabia (Attia et al., 2019). The project will cover an area of 26,500 km2 and stretch over 460 km along the coast of the Red Sea, with investments totaling up to $500 billion. The NEOM area will function independently from the Saudi-Arabian jurisdiction, rather eyeing a Western-inspired lifestyle. Its aims to open by 2025 and to host one million residents by 2030 (The National, 2020). Multiple other large investments in smart cities are on the way, from Chile, Mexico, Nigeria, Abu Dhabi, India, and China, the examples are manifold and the development will stretch well into the

2.3 Generational Change

21

2030s and 2040s. Why are governments building all these smart or future cities? The answer is rather trivial: smart cities are seen as vehicles to stipulate economic growth, attract talent, and investment. Role models for these investments are the aforementioned superstar cities like San Francisco, London, Stockholm, or Tokyo, which have largely decoupled themselves from their respective host country. Emerging countries want to mirror this achievement by building smart cities from scratch. As of 2021, none of the aforementioned projects have been able to reach the economic output of cities that have evolved over decades or even centuries, but in the years to come the incumbent cities will likely face new competitors which do not have to suffer from several legacy problems such as outdated public infrastructure or an inefficient car-centered design.

2.3

Generational Change

A generation can be defined as “a cohort-group whose length approximates the span of a phase of life and whose boundaries are fixed by peer personality” (Howe & Strauss 1992, p. 60). The peer personality refers to a person in the same generation, which is mostly determined by age, common beliefs, and perceived membership in that common generation. Members of a generational cohort often share similar habits, lifestyles, and values which distinguish them from other generations. In Western societies, currently five different cohorts are recognized: (1) Silent, (2) Baby Boomers, (3) Generation X, (4) Millennials, and (5) Generation Z. Across the world, various definitions and labels can be found for these generations, but in principle, the formative context, traits, and behaviors among these generations are almost borderless. Especially, the younger generations are increasingly similar around the globe (McCrindle & Wolfinger, 2011). The silent generation was born from 1925 to 1945, and was labeled “silent”, because few of its members held high-profile positions at the time of its naming (Rainer & Rainer, 2011). Baby boomers were born between 1946 and 1963 and grew up and matured during relatively stable and prosperous times. Their name is due to the increasing birth rates at the time and they have been the largest cohort for decades in most Western countries. The sheer size caught the attention of media, businesses, and scholars. That is why this generation is the most researched, marketed, and debated generation to date. Gen. X, born from mid-1960s to late 1970s, is smaller than the boomers. Millennials who were born between 1980 and 1995 gained tremendous media attention within the last years, as they allegedly differ heavily from previous generations. Gen. Z, born from 1996 to 2010, is also called the first true digital generation (KPMG, 2017b). Table 2.3 displays intra-generational differences in several dimensions. Across generations, different formative experiences shape aspiration, values, and perception of society. Millennials and Gen. Z do contradict previous generations in terms of marketing and customer behavior. Both of these generations value unique experiences and often put them before the consumption of products. Baby boomers, on the contrary, put a bigger emphasis on status symbols like cars. The values of

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Table 2.3 Different generations (Source: own illustration, various sources, 2020) Years Formative experiences

Aspiration Traits and behavior Way of consumption Signature products

Silent Gen. 1925–45 WW2 Great depression Home ownership Conformism

B. Boomer 1946–63 Postwar Cold War Youth culture Job security

Affordability

Idealism Revolution Collectivism Ideology

Homes Television

Cars and music

Gen. X 1964–79 End of Cold War Rise of technology Work-life balance Materialism Competition Individualism Status PC Luxury articles

Millennials 1980–94 Globalization Internet

Freedom and flexibility Globalism Selforientation Experience Travel and festivals

Gen. Z 1995–2010 Social media Digital natives Security and stability Realism Undefined Uniqueness Ethical products

millennials and generations Z are surprisingly similar across the globe, whereas previous generations differed much more on a country level. They demand transparency, are less loyal to brands, trust reviews and their peers more than conventional advertisement (Sarkar, 2019). In doing so, they are disrupting entire industries. For instance, their preference for healthy and organic foods put traditional food companies, like Kraft or Kellogg’s, under pressure to change. The inter-generational differences toward mobility are striking. Millennials and Gen. Z tend to drive less and use other transport modes at higher rates than previous generations (Polzin et al., 2014). They even own fewer driver’s licenses and thus vehicles. Generations like baby boomers associate cars with status and are willing to spend a higher proportion of their disposable income for their private car (Thigpen & Handy, 2018). Due to a higher environmental awareness and frequent usage of information and communication technologies (ICTs), younger generations exhibit a higher preference for mobility services, such as carsharing. Vij et al. (2020) survey 3985 Australian citizens and find that utilization of new mobility services is strongly correlated with age, young individuals are most likely to use MaaS solutions. Becker et al. (2017) even find that members of free-floating carsharing are younger on average than the members of station-based carsharing (Becker et al., 2017). Clewlow and Mishra (2017) survey residents in seven major US cities on ridehailing and report that 36% aged 18 to 29 have used the service, compared with only 4% of those aged 65 and older. Unsurprisingly, the most recent additions to the mobility mix, e-scooter, and bike-sharing services are predominately adopted by younger generations (Buck et al., 2013). In contrast, baby boomers display a high car dependence, with frequent and intense utilization (Colli, 2020). They travel more than their previous generations (silent and before) and everyday trips are longer. For boomers, the car remains important for trips related to leisure, shopping, and social activities with little willingness to change this behavior. Younger generations exhibit

2.3 Generational Change

23 Gen. X

Baby Boomer

Millenials

Gen. Z

6 4 2

2018

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2012

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2004

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1990

1988

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Fig. 2.7 Yearly GDP growth in OECD countries per cohort group by age 18 (Source: own calculations, based on World Bank, 2020)

more adaptability and openness toward new services, favoring simplicity, convenience, and affordability in regards to their mobility preference. Across the globe, the younger generations share further similarities: Their marriages take place later in life and childbearing is delayed or outright neglected. They are better educated than previous generations, yet they have a harder time in the present job market. Younger generations are reaching adulthood in times of lower economic growth than previous generations. Figure 2.7 illustrates the average GDP growth per year in OECD countries of each cohort group by age 18 (The World Bank, 2020). For example, baby boomers were born between 1946 and 1963 and reached age 18 in the years 1964 to 1981. The growth curve is subject to strong fluctuations, often times recessions, yet the aggregated average growth for each cohort highlights the intra-generational differences. For baby boomers, the aggregated average growth amounts to 4.18%, for generation X to 2.90%, and for millennials to 1.98%, resulting in fewer economic opportunities for younger generations. In OECD countries, millennials are increasingly being squeezed out of the middle class (OECD, 2019c). For example, the median wages grew by an average of 0.3% per year between 2007 and 2017, compared to more than three times that between the mid-1980s and mid-1990s. The lower growth rates within OECD countries have led to striking discrepancies in terms of inter-generational wealth. Figure 2.8 highlights how wealth in the United States is fundamentally skewed toward older generations, the silent and baby boomers hold considerable more assets than any other generation. In simplified terms, the FED measures wealth by assets (e.g., homes, cash saving, and stocks) minus debts (e.g., mortgages or student loans). In 2019, the silent generation held around 24%, baby boomers 57%, Gen. X 16%, and millennials only accounted for roughly 3% of wealth in the United States (Federal Reserve, 2020). Not just collectively, but even compared to the same age in the life of the generations, the picture seems similar dire for younger ones. Examining the data by the median cohort age of 35, baby boomers collectively owned 21 percent of wealth in the United States by the time their generation hit a median age of 35 in

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Why Mobility-as-a-Service?

100 90 80 70 60 50 40 30 20 10 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020

0

Silent and Earlier

Baby Boomer

GenX

Millennial

Fig. 2.8 Wealth distribution in the United States in % (Source: own calculations, based on Federal Reserve, 2020)

1990 (Washington Post, 2020). On average generation X reached 35 in 2008, owning 9 percent of US wealth. Millennials will hit 35 by 2023 (on average), but in 2020, they only owned around 3.2 percent of wealth and it is very unlikely that this percentage will drastically change within these few years. This is mostly due to the almost gravitational pull of compound interest. In principle, if people start with less capital they will have much more difficulties to build wealth over time. The unequal distribution of wealth among generations, although to varying degrees, can be found in Europe as well (Chen et al., 2018). There is very limited or no comprehensive data on the intergenerational wealth distribution in China or India available, but a few indicators are pointing toward a different direction. Chinese millennials and Gen. Z are in great demand for luxurious products and have often higher incomes to spend than previous generations (KPMG, 2017a). Apart from the inter-generational wealth distribution, the disparity of wealth is becoming greater, implying that much wealth is concentrated at the top of the pyramid. The Gini index, which measures income or wealth inequality, has been on the rise for most OECD countries in the last three decades (Bain & Company, 2018). In the United States, between 2001 and 2013, the wealth owned by the top 1% increased from 32% to 36% (Killewald et al., 2017). Even in Nordic countries like Sweden or Denmark, countries renowned for their system of redistribution, inequality has risen. Inequality in China is equally pronounced, but to a different degree than in Western countries. Inequality is divided between rich coastal and poor inland regions (Shi, 2013). The one-child policy has even amplified economic inequality among generations, as poor families are more constrained than rich ones (Yu et al., 2020). This means that poor families have more children but can invest much less in human capital per child. Given that advancement in China depends heavily on extracurricular activity, it widens the intergenerational gap. In his seminal book “Capital in the Twenty-First Century”, Thomas Piketty shows that wealth inequality has followed a U-shaped trajectory across most

2.3 Generational Change

25

developed countries since 1990 (Piketty & Goldhammer, 2014). He attributes the increase in inequality mainly to the fact that the rate of return on capital is larger than the economic growth rate. In other words, income on assets grew faster than income on labor. Although his work has been widely criticized from numerous instances, his findings and reasoning on inequality are generally agreed upon (Milanovic, 2014). The practice of quantitative easing has further bolstered the inequality among generations, since it leads to strong distributive effects. Those who own assets, such as homes and shares, benefit strongly from increasing asset prices. Consequently, saving for retirement is more challenging for current generations than for previous generations, as most stable investments (e.g., life insurance) are returning close to zero. To put things into perspective, current investments in governments bonds, accounting for inflation, are effectively losing money (Hausken & Ncube, 2013). Younger generations do benefit from digitalization and its promise of ubiquitous information and entertainment, but they also suffer in greater terms from the corrosion of the medium-skilled job. This phenomenon is called job polarization and refers to the increasing concentration of employment within the lowest- and highest-paying segments (Autor, 2010). Several authors have demonstrated that high-skilled (e.g., data scientist or asset manager) and low-skilled (e.g., cleaner or fast food worker) jobs are on the rise throughout North America and Europe. Employment in the middle of the income distribution (e.g., routine office job or manufacturing) is in decline (Goos et al., 2009). This hollowing out of the middle is caused by the disappearance of occupancies that are based on routine tasks. Several hypotheses on why these jobs are disappearing have been put forward, most notably globalization, automation, and digitalization (Jaimovich & Siu, 2020). This implies that activities that are defined by well-set procedures are simply replaced by machines, software, or outsourcing. Jobs in call centers, for example, especially in English-speaking countries, have been moved in staggering numbers to Asian countries. In the Philippines alone, call centers employ over one million people (Paxson, 2018). But even more complex tasks, like legal document preparations or case searching which previously required paralegals and lawyers, are now performed by software (John Markoff, 2011). Younger generations are much more confined with this phenomenon and millennials and generation Z will, to a certain degree, find themselves either at the top or at the bottom of the income distribution. Apart from the financial perspective, global demographics are undergoing a significant change. The workforce in developed economies is aging rapidly (Magnus, 2012). Figure 2.9 depicts the US and EU28 labor force average growth by decade. In the 2020s, growth will slow to around 0.4% per year in the United States and shrink by
0.5% in the EU28 (Bain & Company, 2018). This represents a major demographic shift and ends the abundance of labor that has fueled the economic growth, especially in the United States, since the 19,070 s. Due to longevity and better healthcare, many will work well into their 60s and beyond, but this trend will not offset the negative effects of aging populations. In EU28, the aging of the workforce is even stronger. This pattern is observable in some Asian countries as well and it is particularly pronounced in Japan. The most radical demographic transition of all countries takes

26

2 US

Why Mobility-as-a-Service?

EU 28

3.00% 2.60% 2.50% 1.80%

2.00% 1.50% 1.50%

1.30% 1.20% 0.90% 0.80% 0.90% 0.70% 0.50% 0.50% 0.60% 0.40% 0.30% 0.50% 0.40%

1.10% 1.00% 0.50% 0.00% -0.50% -1.00%

1950s 1960s 19070s 1980s 1990s 2000s 2010s 2020s 2030s 2040s 2050s -0.10% -0.50% -0.50% -0.70% -0.60%

Fig. 2.9 Average growth rate of labor force per decade in the United States and EU28 (Source: based on Bain & Co, 2018)

place in South Korea, which in 2019 had the world’s lowest fertility rate of 1.1 (BBC, 2020). This rate has been declining drastically since the 1950s, when the country grew a fertility rate of 5.6. This means that the South Korean population is aging faster than any other country on earth. Even China faces a similar problem, its population will peak in 2029 and then enter an unstoppable decline (Time, 2019). The country will enter an era of negative population growth, which is largely due to the legacy of the one-child policy. Introduced in 1980 it aimed to soften the growth out of fear for an economically unbearable population. This policy was scrapped in 2016, but it seems that a decline is unavoidable. The trend of an aging population in developing and emerging countries is amplified by the middle income trap, which implies that these economies tend to stagnate as incomes reach the median levels and thus this newly emerging (middle) class begins having fewer babies (Aiyar et al., 2013). Especially Chinese women are prioritizing careers and stable home life over raising children as the costs of living and education soar. This behavioral pattern can be found in many countries, independent of their respective culture. Only Africa will be the last remaining resort of strong population growth. As the total size of the labor force stagnates or declines in many markets, economic growth could slow and demand for mobility solutions will change.

2.4

Synthesis of Chap. 2

So why now mobility-as-a-service? The main reasons are digitalization, urbanization, and intergenerational changes. Digitalization is a general-purpose technology that transcends a single technology. It has fundamentally changed the relationship between firms and consumers. The smartphone has become ubiquitous and deeply

2.4 Synthesis of Chap. 2

27

embedded into the social fabric of society. It is the primary gateway to any information, communication, and consequently mobility, too. Virtually all new mobility solutions require the smartphone, which combines mapping, routing, and navigation functionality, enabling new business models. The way value is created and delivered has changed and expanded. Products are turned into services in all industries. Product sharing has benefited consumers and firms alike. The end of ownership, the gig economy, and aggregation are contributing to the rise of new mobility solutions. Firms are now embedded into dynamic networks of stakeholders based upon reciprocal communication; customers are no longer just passive targets (Rogers, 2016). Online marketing is based on real-time data and elaborated usage of statistical methods whereas offline marketing addresses audiences at random. Think of a TV advertisement for a new SUV which is the same to anybody compared to an online campaign via social media which targets the desired customer groups and enables feedback and data evaluation. Automotive firms were used to sell in bulk to their dealers, now digitalization brings direct contact and the need to derive value from data. The transformation of space has diminished or even broken down industry barriers. Competition went from being symmetric to asymmetric; it is no longer a global zero-sum game between 10 and 12 major OEMs. Competitors looked like binary opposites, whether it be from Munich (BMW) to Stuttgart (Daimler) or Detroit (General Motors) to Tokyo (Toyota). Firms that competed with each other, did in a way looked like one another as they faced similar opportunities and challenges. Now start-ups and tech companies are disrupting the industry from all over the place and do have different values, mindsets, and organizational set-ups. Most new mobility segments, like ridehailing or micromobility, are dominated by new entrants. Incumbent firms have had a hard time to realize new mobility ventures. New entrants into the mobility market are able to scale without mass and with great speed, this puts incumbents under pressure to change their organizational settings to embrace an iterative learning mindset. In a digital environment, the entry point of a consumer is via smartphone or browser, thus occupying this touch point along the customer journey is a high priority challenge. Cars are increasingly software products, contribute value through connectivity services and require strategic interlinkages to mobility solutions. For instance, free-floating carsharing firms rely on dedicated in-vehicle software to operate. The rapid expansion of urban agglomerations in the developed world and even more so in emerging and developing countries has fostered economic growth and lifted billions of people out of poverty. Many megacities, especially throughout Asia and Africa, are emerging. The urban metabolism inhibits economically and socially favorable outcomes but also severe negative externalities, most notably overloaded infrastructure, congestion, pollution, waste, and noise. As e-commerce continues to grow, the additional numbers of delivery vehicles further increase the stress on cities. These negative externalities require solutions and new interconnected mobility services might mitigate these. Just congestion alone is absurdly costly to communities. Mobility-as-a-service indulges the notion of reducing negative externalities by applying a holistic view on mobility. MaaS aims to maximize utilization across all transport options, thereby reducing the overall number of

28

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transportation modes, such as cars or buses. In certain parts of the world, the car will remain the backbone of private transportation, especially in North America where urban areas are much more spread and public transportation infrastructure is underdeveloped. The American culture of “suburbia” is designed for cars and will largely remain that way. Europe and China show a mixed picture. In several European countries, such as Denmark, the Netherlands, or Austria rather restrictive land utilization plans limit the urban sprawl. Cities are equipped with public transportation, both in terms of efficiency and frequency and further possibilities for walking and cycling (Bain & Company, 2018). Rural areas often solely rely on private transportation. In China, the sheer size of the population leads to much higher densities within the urbanized areas, especially in the coastal regions. Across the globe, several superstar cities have emerged and their gravity might surpass the relevance of countries. Silicon Valley competes with Boston, London, or Shanghai and not with England or China. As superstar cities become largely disenfranchised to the host country, they become more alike, due to advancement in technologies and cultural assimilation. In terms of spoken language, education, political, and cultural values, daily life in a start-up firm in Tel Aviv, Berlin, or London, tends to be pretty similar. The economic superstar cities in the United States, namely Boston, Seattle, San Diego, San Francisco, and the Silicon Valley, concentrate prosperity and attract talent. This phenomenon is observable, although to a lesser degree, in the European Union as well. Superstar cities will most likely evolve into true first smart cities, a concept that incorporates and expands the idea of MaaS. It is driven by the notion that information and communication technologies can lessen the burden of the tragedy of the commons. In newly designed cities, there will be little to no room for private car transportation as we know it. Not cars, other forms of transportation, ranging from public to micromobility will be key in these areas. Planned cities like NEOM aim to be futuristic by utilizing the most advanced technologies available while solely relying on renewable energy. By the end of 2019, 153 cities around the globe published an official smart-city strategy with many more on the way (Roland Berger, 2019). With all these bold claims, a certain level of skepticism seems appropriate, since several smart city concepts are on the way for several years, yet none of them have lived up to their lofty expectations. The idea of a smart city reaffirms the notion of a widening gap between rural and urban developments. Truly smart cities that enable “smartness” across all dimensions hold significant leverage over other urban developments. These urban agglomerations will look and feel vastly different to neighboring and distant communities. New mobility concepts are mainly explored and concentrated in these types of cities as they are only economically feasible in urbanized areas with a certain population density. Several mobility solutions, for example, station-based carsharing, have been laid out numerous times in rural areas, yet most of the endeavors proved to be unsuccessful. Rural areas, especially in the developed world, are designed for transportation by private cars and will continue to do so. Millennials and generation Z contradict previous generations in terms of marketing and general customer experience. Both of these generations value unique experiences and often put them before the consumption of products. They aspire

2.4 Synthesis of Chap. 2

29

different things than previous generations and their status symbols are often tech goodies, like a smartphone, tablet, or smartwatch. They appear to be less car crazed than baby boomers. There is great ambiguity about the general impact of digitalization on jobs, wages, and working conditions. While the net impact is highly disputed, most studies agree upon the great need of upskilling and continuous learning. We can already see a certain polarization of jobs, a clustering of within the lowest- and highest-paying segments. As job switching becomes common and general lifestyle patterns become blurred, this might lead to a reluctance by consumers to settle for only one way of transportation, like a private car. Younger consumers prefer much more flexibility but society also expects them to be more flexible. The high adoption of micromobility by younger generations and the rise of subscription models in the automotive industry mirror this development. To continue, a rising awareness for environmental aspects, individualized lifestyles, and the adoption of technology leads to a different perception of mobility by younger generations. The inter-generational discrepancy regarding their individual wealth and real income is remarkable and a challenge for society and businesses. Some of this gap will close eventually as wealth is being passed on to heirs, but the distribution of inheritance is often unequal. This discrepancy of wealth already affects the consumer behavior. Kurz, Li, and Vine find that baby boomer and generation X members in the United States spend $600 to $700 a year more on cars than millennials (Kurz et al., 2018). However, once this data is plotted against wealth and other economic factors, the generational cohort fixed-effects are not significant. In other words, younger generations do not seem to exhibit a different preference for cars than older generations, they simply have less money to spend on cars. Some research suggest that even though younger generations do show lower car affiliation, as the population ages they have become increasingly car dependent. Newbold and Scoot show in a study on Canada that millennials are just as likely to acquire a driver’s license as previous generations although delayed (Newbold & Scott, 2017). Klein and Smart (2017) find that millennials in the United States own about 13% fewer cars than older generations. When controlling for economic factors and age, they find that millennials who still live with their families exhibit low car ownership. Millennials, who live on their own, actually do not seem to have a lesser preference for car ownership. Consequently, they can attribute the lower overall car ownership of millennials to lower income, less wealth, and decreased employment opportunities. It might be even the case that the rise of the sharing economy is not simply a matter of preference; it might mainly be due to less wealthier generations. An example of how the generational wealth gap already affects the automotive industry maybe this: in 1999, the average age of a German customer for the Porsche Boxster model was 41.8 years; this number rose to 50.7 years in 2016 (Interviews, 2020). Younger generations in Western societies simply cannot afford the lifestyle of previous generations, like buying brand new cars. In China, where the younger generation seems not to be considerably disadvantaged regarding intergenerational wealth, the average age in 2017 for a Porsche customer was 36, considerably younger than in any Western markets.

30

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The aging workforce implies less potential demand in Western countries, but might also lead to an intensified competition for talent. Governments will face major challenges such as exploding healthcare costs, commitments to pensions, and high debts. This might leave little room for investments into much needed infrastructure for holistic mobility approaches. Increasing life expectancy coupled with aging population leads to a greater proportion of people unable to drive. Elderly will require large-scale and inclusive mobility services for trips to doctors, shopping, and family visits. Solely relying on the private car as a means of transportation will not be sufficient. This could magnify the need for shared and new mobility. Achieving a truly smart city across the key areas economy, people, governance, mobility, environment, and living necessitates enormous investment. Smart city concepts in the West might have to function on a shoestring budget. This could simply result in favoring new modes such as the e-bike and scooters over the private car, by dedicating inner city lanes exclusively to these new modes. The developments initiated by the overarching themes of digitalization, urbanization, and generational differences will only intensify in the years to come, thus stimulating the departure from the private car-consumed transportation toward new mobility services.

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OECD. (2015). Data-driven innovation: Big data for growth and well-being. OECD Publishing. https://doi.org/10.1787/9789264229358-en. OECD. (2019a). Going digital: shaping policies, improving lives. OECD Publishing. https://doi. org/10.1787/9789264312012-en. OECD. (2019b). An introduction to online platforms and their role in the digital transformation. OECD Publishing. https://doi.org/10.1787/53e5f593-en. OECD. (2019c). Under pressure: The squeezed middle class. OECD Publishing. OECD. (2019d). Vectors of digital transformation. OECD Publishing(273). Paxson, P. (2018). Mass communications and media studies: An introduction. Bloomsbury Publishing USA. Perzanowski, A., & Schultz, J. (2016). The end of ownership: Personal property in the digital economy. MIT Press. Piketty, T., & Goldhammer, A. (2014). Capital in the twenty-first century. The Belknap Press of Harvard University Press. Polzin, S. E., Chu, X., & Godfrey, J. (2014). The impact of millennials’ travel behavior on future personal vehicle travel. Energy Strategy Reviews, 5, 59–65. Radziwill, N. M. (2020). Connected, intelligent, automated: The definitive guide to digital transformation and quality 4.0 (First edition). Quality Press. Rainer, T. S., & Rainer, J. W. (2011). The millennials: Connecting to America’s largest generation. B & H Pub. Group. Raskino, M. (2015). Digital to the core. Bibliomotion. http://search.ebscohost.com/login.aspx? direct¼true&scope¼site&db¼nlebk&AN¼1088030. Reuters. (2019). Volkswagen aims to boost in-house software development to 60% by 2025. https:// www.reuters.com/article/us-volkswagen-software/volkswagen-aims-to-boost-in-house-soft ware-development-to-60-by-2025-idUSKCN1TJ0K3 Reuters. (2020a, August 21). Facebook’s WhatsApp acquisition now has price tag of $22 billion– Reuters. https://www.reuters.com/article/us-facebook-whatsapp-idUSKCN0HV1Q820141006 Reuters. (2020b, August 21). Google to end ‘Double Irish, Dutch sandwich’ tax scheme–Reuters. https://www.reuters.com/article/us-google-taxes-netherlands/google-to-end-double-irish-dutchtax-scheme-filing-idUSKBN1YZ10Z Rifkin, J. (2015). The zero marginal cost society: The internet of things, the collaborative commons, and the eclipse of capitilism. Palgrave Macmillan. Rogers, D. L. (2016). The digital transformation playbook: Rethink your business for the digital age. Columbia Business School Publishing. Columbia Business School Publishing. http://gbv. eblib.com/patron/FullRecord.aspx?p¼4398619 Roland Berger. (2019). The smart city breakaway. Sacolick, I. (2017). Driving digital: The leader’s guide to business transformation through technology. AMACOM. https://ebookcentral.proquest.com/lib/gbv/detail.action?docID¼4787345. Sarkar, S. (2019). Customer-driven disruption: Five strategies to stay ahead of the curve. BerrettKoehler Publishers. Shi, L. (2013). Rising inequality in China: Challenges to a harmonious society. Cambridge University Press. doi:https://doi.org/10.1017/CBO9781139035057. Song, C., Wu, L., Xie, Y., He, J., Chen, X., Wang, T., Lin, Y., Jin, T., Wang, A., Liu, Y., Dai, Q., Liu, B., Wang, Y., & Mao, H. (2017). Air pollution in China: Status and spatiotemporal variations. Environmental Pollution, 227, 334–347. https://doi.org/10.1016/j.envpol.2017.04. 075. Startupblink. (2020). Global ecosystem report 2020. Strategy&. (2019). An ecosystem approach to reducing congestion. Sumantran, V., Fine, C., & Gonsalvez, D. (2017). Faster, smarter, greener: The future of the car and urban mobility. MIT Press. https://ebookcentral.proquest.com/lib/gbv/detail.action? docID¼5103882. Taplin, J. T. (2017). Move fast and break things: How Facebook, Google, and Amazon cornered culture and undermined democracy. Pan Macmillan UK.

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Teece, D. J. (1980). Economies of scope and the scope of the enterprise. Journal of Economic Behavior & Organization, 1(3), 223–247. The Economist. (2020). Who will rule the Teslaverse? https://www.economist.com/business/2020/ 09/17/who-will-rule-the-teslaverse The National. (2020). Saudi Arabia’s Neom appoints Aecom to design city’s transport and utilities infrastructure. https://www.thenational.ae/business/technology/saudi-arabia-s-neom-appointsaecom-to-design-city-s-transport-and-utilities-infrastructure-1.1078227 The New York Times. (2020). The future of Airbnb. https://www.nytimes.com/2020/09/24/travel/ airbnb-pandemic.html The World Bank. (2020, August 12). GDP growth (annual %)–European Union, United States, China | Data. https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG?end¼2019& locations¼EU-US-CN&start¼2000 Thigpen, C., & Handy, S. (2018). Driver's licensing delay: A retrospective case study of the impact of attitudes, parental and social influences, and intergenerational differences. Transportation Research Part A: Policy and Practice, 111, 24–40. Time. (2019). China’s aging population is a major threat to its future. https://time.com/5523805/ china-aging-population-working-age/ TOMTOM. (2020). Traffic congestion ranking. https://www.tomtom.com/en_gb/traffic-index/ ranking/ United Nations. (2018). World urbanization prospects. https://population.un.org/wup/Download/ Valenduc, G., & Vendramin, P. (2017). Digitalisation, between disruption and evolution. Transfer: European Review of Labour and Research, 23(2), 121–134. https://doi.org/10.1177/ 1024258917701379. Vij, A., Ryan, S., Sampson, S., & Harris, S. (2020). Consumer preferences for Mobility-as-aService (MaaS) in Australia. Transportation Research Part C: Emerging Technologies, 117, 102699. https://doi.org/10.1016/j.trc.2020.102699. Wallmüller, E. (2017). Praxiswissen Digitale Transformation: Den Wandel verstehen, Lösungen entwickeln. Wertschöpfung steigern: Hanser. Washington Post. (2020, August 9). The staggering millennial wealth deficit, in one chart. https:// www.washingtonpost.com/business/2019/12/03/precariousness-modern-young-adulthood-onechart/ Yin, P., Brauer, M., Cohen, A. J., Wang, H., Li, J., Burnett, R. T., Stanaway, J. D., Causey, K., Larson, S., & Godwin, W. (2020). The effect of air pollution on deaths, disease burden, and life expectancy across China and its provinces, 1990–2017: an analysis for the Global Burden of Disease Study 2017. The Lancet Planetary Health, 4(9), e386–e398. Yu, Y., Fan, Y., & Yi, J. (2020). The one-child policy amplifies economic inequality across generations in China. Zhang, H. (2017). Opportunity or new poverty trap: Rural-urban education disparity and internal migration in China. China Economic Review, 44, 112–124. https://doi.org/10.1016/j.chieco. 2017.03.011.

3

Shaping the Future of Mobility

3.1

The Rise of Ecosystems

The term ecosystem itself appears to be the latest corporate buzzword. It has been referred to countries (China’s ecosystem), a bundle of services and products, and even wellbeing (a happiness ecosystem). According to BCG, the term ecosystem occurs 13 times more frequently in annual reports now than it did a decade ago (BCG, 2019). Apart from this semantic overstress, it is fundamentally a way to organize economic activity. Also called platform or multi-sided platform, ecosystems can bring together people, enterprises, and markets through digital technologies (McKinsey&Co, 2019a). In general, ecosystems provide new ways of organizing the trade-off between flexibility and commitment (Fuller et al., 2019). In traditional organizational settings, firms can focus on flexible decisions, e.g., a pilot project, or commit themselves to a rigid strategic path. In an ecosystem, firms can focus on building platforms, like Airbnb, but remain flexible about the services they will deliver, since other companies will provide these services. Current resources can be combined and reconfigured without having to commit to a certain combination in-house. The essential characteristics of ecosystems are the following. They comprehend groups of companies, involving multiple entities. For instance, Alphabet Inc. encompasses Google Maps, YouTube, Android, and Waymo. They act as gateways, reducing friction with customers’ encounters when switching between related services (Skilton, 2016). Ecosystems utilize network effects that provide a strategic scale advantage. Integration of data across services and products ensures a seamless and superior customer experience (McKinsey&Co, 2019a). For example, Google’s sheets, data storage, and e-mail program interconnect flawlessly. Organizational structures can be described along a spectrum of fluidity, see Fig. 3.1 (Fuller et al., 2019). It illustrates the kinds of relationships firms can have between their products and consumers. The most stable phenotype is entirely vertically integrated along its products and services, all key activities remain within the organization. A static supply chain combines vertical integration with the outsourcing of certain activities to different suppliers. The most fluid form is a # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Ackermann, Mobility-as-a-Service, Management for Professionals, https://doi.org/10.1007/978-3-030-75590-4_3

35

36

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Shaping the Future of Mobility Most fluid

Most stable

Consumer Product or company Company

Vertically integrated

Static Supply chain

Ecosytem

Open market

Fig. 3.1 A spectrum of organizational structures (Source: based on Fuller et al., 2019)

complete open, uncoordinated market in which customers combine products according to their individual and changing demands. In contrast to the open market model, ecosystems comprise a set of multilateral relationships (BCG, 2019). This implies that a contract between A and B (e.g., phone manufacturer and app developer) can be undermined by the failure of contract between A and C (phone manufacturer and telecom provider). Moreover, contributions to the ecosystem tend to be customized by the participants, yet they are mutually compatible. For example, app developers for Apple iOS need to ensure compatibility. In contrast to vertically integrated supply chains, ecosystems are not fully hierarchically controlled. Some sort of mechanism ensures coordination, which can be standards, rules, or processes (Westerman et al., 2015). Digital ecosystems mostly safeguard access and interaction by a set of application programming interfaces, called APIs. Successful ecosystems follow the principles put forward by Eric Raymond in his seminal paper “The Cathedral and the Bazaar”. Based on the development of the Linux kernel, Raymond observed that two distinct types of processes advance the overall software (Raymond, 1999). With the cathedral, software releases are centralized and restricted to an exclusive group of software developers. The bazaar describes decentralized open-source development, in principle anyone can contribute. The rationale behind the bazaar mode is that the more widely available the source code is, the more rapidly all forms of bugs and inconsistencies are discovered. An ecosystem applies the benefits of centralized cathedral building and the flexibility of the bazaar. The development of YouTube itself is highly centralized, while content creation is decentralized. To take this analogy even further, while an ecosystem is this beautiful, complex, and towering cathedral, a traditional business model resembles rather the building process of a residential house.

3.1 The Rise of Ecosystems

37

User interface Applications Operating system

In-house

Software

Cloud platform Connectivity & data

Actuators, Sensors & Power Components Vehicle

Supplier

Hardware

Electronics

OEMs In-house

Tesla

Supplier

In-vehicle technology

Fig. 3.2 Technology sourcing, Tesla vs. OEMs (Source: own illustration based on interviews, 2020)

In the automotive industry, OEMs approximate the “static supply chain” phenotype, some development takes place in-house, but large chunks of the car production span across multiple supplier levels around the globe. Interestingly, the infamous disruptor named Tesla pursues a higher vertical integration in software as well as hardware development than all established OEM and major suppliers (Perkins & Murmann, 2018). Tesla decouples software and hardware development. In software development, a rather tight vertical integration is chosen, in order to maximize control over their own software ecosystem. Tesla deliberately chose to lock out Android Auto and Apple Car Play (Harvard Business Review, 2020b). They embrace their own homebrew Linux system and even shy away from other global Linux alliances, such as the Automotive Grade Linux, which features notably members like Ford, Toyota, and Bosch (Automotive Grade Linux, 2020). The main risk of this stand-alone software strategy is to ensure a critical mass so that their own ecosystem can strive in the long run. On the upside, if a certain reach is achieved, Tesla will entirely own a software platform and can reap its benefits. Connected services are considered to be one of the main value drivers of automotive companies of the future. As for a popular comparison, Apple boasted around $50 billion of sales in 2019 via its app store alone and it is estimated that this generates around $15 billion in revenue for the company (CNBC, 2020a). On hardware, Tesla applies a model that resembles the static supply chain phenotype, critical activities are performed in-house, and comparably few parts are outsourced (Various, 2020). The depth of value added is much greater than with traditional OEMs. Figure 3.2 compares insourced and outsourced in-vehicle technology of Tesla and traditional OEMs. While the graph is oversimplified and does not apply to all OEMs in the same manner, Tesla does exhibit a higher vertical integration for several reasons: Tesla did pursue a highly centralized strategy from the start, while the technological architecture of OEMs is intertwined in complex supplier networks, which evolved over decades. With Tesla, vehicle configuration is simplified with limited variances,

38

3 Must have

Shaping the Future of Mobility

Interested

56

54

23

30

APPLE CAR PLAY

ANDROID AUTO

Fig. 3.3 Interest in Apple car and Android auto (Source: based on Strategy Analytics, 2017)

whereas OEMs have far-reaching product portfolios with several interlinkages between OEMs and suppliers. For example, in 2010 Daimler, Renault and Nissan formed an alliance in order to utilize economics of scale, cut costs on small engines, and collaborated on pick-up trucks (Reuters, 2020b). Such partnerships allow for great variability, savings on inputs and risk-reduction but might not be best suited for establishing an end-to-end ecosystem. To continue, most OEMs tend to purchase infotainment and vehicle control systems from suppliers, while these competencies remain entirely in-house with Tesla. Moreover, the production of EVs requires less parts, a conventional powertrain may have as many as 2000 moving parts, while electric powertrains may have as few as 20 (Forbes, 2019b). A Tesla model 3 only has one central control unit, whereas most modern cars have several control units due to a high-degree of outsourcing. The mechanism of ecosystems has led to the emergence of big tech companies, most notably the FAAMG companies, an acronym for Facebook, Amazon, Apple, Microsoft, and Google (now Alphabet) (Smyrnaios, 2018). These companies dominate their respective markets through far-reaching ecosystems and in terms of market capitalization belong to the biggest companies in the world. Most tech giants seem to venture into the mobility market, best summed up by the testimonial of Facebook COO Sheryl Sandberg who stated in 2017: “We’re the only company in Silicon Valley that’s not building a car” (Emma Thomasson, 2017). Via Waymo Google is at the forefront of autonomous driving and Apple has long been rumored to build a car of their own, the so-called “Project Titan” (The Guardian, 2015). Amazon has invested millions in the elective vehicle startup Rivian and acquired Zoox, a selfdriving start-up (Forbes, 2020). To continue, Google and Apple are championing their mobile platforms (Android and iOS) to become the de-facto software ecosystem in cars. A survey from 2017 underlines the potential pull-effect of these systems, as customers are already used to the service and want to extend their usage into the car. Figure 3.3 shows the potential for these services as out of 1503 vehicle owners in the United States who were interviewed; 23% (Apple Car Play) and 30% (Android Auto) consider these services a must-have (Strategy Analytics, 2017). This represents a pivotal challenge for OEMs, as they need to decide if and how to include these already widely established ecosystems. General Motors, for instance, has basically surrendered to this challenge and has decided to use Android Auto in all of their brands as of 2021 onwards (The Verge, 2019). Other manufacturers are

3.2 Sustainability: A Wicked Problem

39

much more wary, some fear to become the Foxconn of the automotive industry, referring to the notorious manufacturer of Apple’s iPhone. Ecosystem companies profit from the so-called “data flywheel” effect (Stevens & Strauss, 2018). As digital ecosystems grow, they accumulate more data, which then fuels improvements in services, thus stimulating further growth, leading ultimately to market domination and thus increased profitability. The capacity of digital giants to attract talent in areas of short supply, e.g., machine learning, reinforces the virtuous circle even more. An ecosystem can evolve into a super-app. Super-apps are a hub for diverse functions that are spread across different apps in other ecosystems (Lee, 2019). The most prominent example, WeChat of China, has taken on the functionality of PayPal, Uber, Expedia, Instagram, Skype, Amazon, and many more. As of 2021, there are no super-apps in Europe or North America. It is an open secret, that several companies, such as Uber or Facebook, strive to become the first western super app (Financial Times, 2019). Another likely competitor is certainly Google Maps, as the service has added several new features in the last years, from hotel booking, restaurants, travel, and mobility (e.g., adding Uber and Lime in selected markets) (Forbes, 2019a). The only real digital competition rivaling the US-American Tech Giants stems from China. Three major players are dominating the market: Baidu, Alibaba Group, and Tencent Holdings, often abbreviated as BAT (Zhu, 2019). All three corporations are active in a myriad of industries, such as e-commerce, money transfer, advertising, gaming, and entertainment. The formula for success is a huge Chinese market, digital enthusiasm of customers, state protection and affiliation, skillful workers, no digital legacy (e.g., leapfrogging from cash to smartphone pay), and an overall growing economy (Fannin, 2019). These Chinese giants also venture out into the world of mobility. For instance, Baidu is the leading map provider in China, comparable to Google Maps, and several businesses run on its service. Baidu champions an open-source approach to autonomous driving, an initiative called Apollo, aiming to establish an ecosystem for autonomous driving (TechCrunch, 2019).

3.2

Sustainability: A Wicked Problem

When industrialization took off in the nineteenth century, the world experienced exponentially growing economies and populations, which caused severe stress on the environment and scarce resources. When economies grow, so does resource usage and emissions. For every 1% increase in global GDP, resource intensity has grown by 0.4% and CO2 emissions by approximately 0.5% (GeSi, 2020). On a global level CO2 emissions have grown by 50% since 1990, and by 35% since 2000 (OECD, 2020). To put things into perspective, the levels of CO2 emissions in 1800 were estimated to be around 280 parts per million (ppm), as of 2020 the global average has been around 400 ppm. Willeit et al. (2019) estimate that over the course of the past three million years the current CO2 concentration in the atmosphere is unprecedented for planet Earth. To put it bluntly, the advancement of technologies that tackle the human impact is not up to par (Cavagnaro & Curiel, 2012). As it

40

3

Fig. 3.4 Three pillars of sustainability (Source: own illustration)

Shaping the Future of Mobility

Economy • Growth • Profit • R&D

Social

Enviroment

• Standard of living • Equality • Diversity

• Natural Resource Use • Prevention • Preservation

stands, the current global demand for energy and natural resources seems unsustainable (World Economic Forum, 2016). Numerous organizations such as the World Meteorological Organization believe that the effects caused by rising emissions cannot be reversed easily and will be a challenge for generations. The ongoing industrialization of emerging economies and a growing world population drive the global hunger for resources. The global middle class is growing consistently, consequently the carbon-footprint per person increases (Homi Kharas, 2017). The amount of materials extracted globally doubled between 1980 and 2010, which can be mainly attributed to the rise of constructions and the advancement of industrial materials. Given the current demand, it is expected to double again by 2060, as highincome countries continue to maintain high levels of material consumption per capita. Waste from manufacturing and non-biodegradable waste from end-of-life usage have led to contamination, water pollution, and the emergence of unregulated landfills. For example, between January and June 2017 alone, Europe, the United States, and Japan exported 3.1 million tons of plastic waste to developing countries, mostly in Asia (Capgemini Research Institute, 2020). These excesses caused by humanity call for sustainability. A common definition for sustainability is based on a statement of the UN World Commission on Environment and Development, Our Common Future (1987): “Sustainable development is development that meets the needs of the present generation without compromising the ability of future generations to meet their own needs” (Managi, 2015). From a business point of view, sustainability is a multiplex concept. The so-called triplebottom line concept refers to three pillars: environmental, economic, and social (Keeley & Benton-Short, 2019). These pillars span a wide array of interrelated topics, see Fig. 3.4. Sustainability has been described as a “wicked problem” due to two principal influences (Borland et al., 2019). A wicked problem does not possess a definitive formulation with a single solution, as requirements are often incomplete, changing, or even contradictory (Incropera, 2016). It has innumerable causes and is almost

3.2 Sustainability: A Wicked Problem

41

impossible to fathom as a whole. The first influence is the complex relationship between the three pillars, meaning that altering one pillar has enormous impacts on the others. The second influence refers to the diverse set of stakeholders within the three pillars. The economic pillar compromises for instance customers, managers, employees, union representatives, and investors. The social pillar includes cities, communities, gender-equality, or human rights while the environmental one potentially spans resource use or even ecosystems, such as woodlands or marine (Ajami & Goddard, 2018). The presence of these oftentimes-opposing stakeholders requires firms to consistently reaffirm their definition and understanding of sustainability. The greater the disagreement among stakeholders, the more wicked the problem. Sustainability as a concept is dynamic, subjective, ever evolving, and highly ambiguous. Wicked problems do not have a specified end-point, just as sustainability does not have a pre-determined time-horizon. The goal of firms should be to balance and manage the interaction of these pillars in a sustainable manner. The pillars are sometimes complementary, but they are often juxtaposed in a functional way. Therefore, theory and practice of corporate sustainability are often side-ways and can lead to partial or imperfect results (Gatrell et al., 2016). In order to become truly sustainable, firms need to employ the concepts of a circular economy. Mimicking nature, the concept of circularity regards the economy as a continuous cycle without waste. Materials and energy are confined in closed-loop cycles and sequentially re-enter the cycle thereby decoupling economic growth for finite resource consumption (PWC, 2019). Our natural environment does not waste materials, all organic material re-enters the biological ecosystem. The circular economy as an alternative economic model seeks to minimize overall material consumption, with particular attention to materials with precarious waste streams such as plastics or electric goods (OECD, 2020). The intent is to maximize the value of products and materials within the circulation; for example, by lengthening the use duration of smartphones. The prevention of waste and the reduction of hazardous components are at the core of the concept. Waste of manufacturers is supposed to become inputs for other manufactures. Ideally, the circular economy creates “win-win” situations between realizing economic growth while alleviating potential resource depletion and environmental degradation (Naustdalslid, 2014). Transportation plays a pivotal role in any sustainability efforts. In 2016, transportation accounted for a quarter of the world’s global CO2 emissions, with road transportation alone accounting for 18% (Capgemini Research Institute, 2020). In the EU, transport is responsible for nearly 30% of the EU’s total CO2 emissions, Fig. 3.5 breaks down the emission by transport mode as of 2016 (European Parliament, 2020). Road transportation accounts for the majority of CO2 emissions, with over 60% of these emissions originating from cars. Even though these data are from 2016, the numbers have not changed much in the years after. In North America and Europe, emissions from transportation have been more or less stable over the course of the last years, albeit at a high level (Agora, GIZ, WEF, 2020). As Asia, Africa, Central, and South America are economically progressing, the still comparably low per capita transport emissions are growing strongly. Extrapolating the current trajectory, transport emissions could more than double

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Shaping the Future of Mobility

Fig. 3.5 Emissions by transport mode in the EU—2016 (Source: based on European Parliament, 2020)

by 2050. Motorized vehicles emit several different greenhouse gases, which contribute to climate change. The primary greenhouse gas is carbon dioxide (CO2). In addition, cars produce methane (CH4) and nitrous oxide (N2O) from the tailpipe and hydrofluorocarbon emissions from air conditioners. The emissions of these gases are small in volume compared to CO2. Nevertheless, these emissions can have a higher global warming potential than even CO2 (US EPA, 2016). The impact of vehicles varies greatly, the level of emissions is correlated to the weight, construction, fuel consumption, and type of fuel. Despite all the negative press in recent year, the automotive industry has made considerable progress in sustainability within the last decades. For instance, since 1990, NOx emissions have been reduced by around 90% in Europe (McKinsey&Co, 2019b). Between 2000 and 2015, OEMs in the EU were ahead of the carbon footprint reduction targets set by regulators. As exhibit 3.6 highlights, they realized CO2 emissions of close to 120 g/km against a target of 130 g/km for new passenger cars in the EU (European Environment Agency, 2020). However, after 2015, carbon emissions started to rise, mainly due to a growing demand for SUVs. The same pattern was observable in the United States (EPA, 2020). With the current demand for larger vehicles by consumers, OEMs are not on a trajectory to meet the emissions standards in the United States and Europe (Fig. 3.6). With increasing pressure from several stakeholders, the CO2 emission targets will only be met with an increased focus on alternative powertrains such as electric or hydrogen fuel cells. Several countries have already announced zero-emission policies. The Netherlands require zero local emissions of new vehicles by 2030 (City of Amsterdam, 2020). The biggest car market in the world, China, has also been pushing heavily toward limiting emissions. The sales of electric vehicles have been heavily subsidized, but these incentives will phase out in 2022 (Reuters, 2020a). China has been known for heavily incentivizing infant or nascent industries and once they reach a certain plateau, subsidies start to phase out (Haley & Haley,

3.2 Sustainability: A Wicked Problem

43

190

Average CO2 emissions from new passenger cars in EU

170

Target CO2 emission new passenger cars in EU

150 130

130

Linear (Average CO2 emissions from new passenger cars in EU)

110 95

90

80.8

70 50

59.4 2000

2005

2010

2015

2020

2025

2030

REQUIRED ZERO EMISSION PERCENTAGE

Fig. 3.6 Emission timeline for new passenger cars in EU (Source: based on European Environment Agency, 2020) 100 80 60

Norway France Netherlands California (USA) India

40 20 0 2015

2020

2025

2030

2035

2040

Fig. 3.7 Zero local emission percentage of total OEM sales (Source: own illustration based on public statements, 2020)

2013). Once financial incentives run out, other forms of stimulus or legislation will ensure the further rise of alternative powertrains in China. In terms of total sales on alternative engines, the Chinese market towers over all other global markets. Consequently, the market volume of alternative powertrains in China will heavily influence all other global markets. Exhibit 3.7 illustrates the compulsory zero local emission percentage of total OEM sales in selected countries. From a strategic perspective, these heterogeneous timelines present an enormous challenge for automotive organizations, as some countries have set imminent zero local emission goals, such as Norway. Other jurisdictions, such as France and India, have set a zero-emission percentage for 2040 (as of late 2020). California (United States) initially settled in 2018 zero emissions for 2040, but in September of 2020 accelerated their efforts and settled for 2035. As these timelines are subject to constant change, they do carry enormous strategical weight for established automotive organizations. It necessitates a highly adaptive and ambidextrous approach, as heterogeneous market demands exist contemporaneously (Fig. 3.7).

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As the world is moving toward less emissions and a smaller ecological footprint, there is a fierce debate on how to achieve this goal in the automotive context. There are various approaches on the measurement and assessment on how to achieve sustainability. Regarding the powertrain, media outlets tend to favor EVs based on their zero local emissions. Yet, concentrating exclusively on tailpipe emissions, implies ignoring other significant sources of GHG emissions, such as vehicle production, origin of electricity, recycling, and sourcing of materials. Therefore, only a full lifecycle measurement can truly determine the environmental impact. Especially, the source of electricity heavily influences this ecological equation, as energy can be derived from a diverse set of sources, which are often not being bound to particular countries, even regions. In 2019, the United States relied for 37% on petroleum, 32% natural gas, 8% nuclear electric power, 11% coal, and only 11% renewable energy for its primary energy consumption (U.S. Energyy Information Administration, 2020). On an aggregated level, this looks surprisingly similar in the EU. In 2018, the energy mix in the EU27 stems from 36% petroleum, 21% natural gas, 21% coals (fossil fuels), 13% nuclear energy, and 15% renewable energy (Eurostat, 2020). Within the United States, some states have a much better environmental footprint than others, as the methods of electricity production vary. In Europe, the picture becomes even more blurred, due to a vast mix of electricity generation methods and diverging national policies. For instance, France derives around 75% of its electricity from nuclear energy, as the country has a long-standing policy toward independent energy security (World Nuclear Association, 2020). In Austria and Luxembourg, approximately 60% of the electricity production originates from hydro power plants, while in Denmark 46% of the electricity production emanates from wind energy. In addition, even so the EU announced to become “the world’s first climate-neutral continent by 2050”, it is still a long way to go (European Commission, 2020). Looking further east, China is the world’s largest greenhouse gas emitter, but it has led the world in the construction of solar and wind facilities within the last years while continuing to build new coal plants (Oxford Institute for Energy Studies, 2020). Several contradictions exist which defy an easy analysis. In general, it is assumed that China is increasing its environmental efforts toward clean energy, but this transition might take longer than anticipated. In Southeast Asia, for instance, the demand for energy is expected to double, at the latest by 2040. In 2020, the contribution of renewable energy was around 15% and the lion’s share has been met by the utilization of fossil fuels (International Energy Agency, 2020). Despite falling costs and South-East Asia’s great natural potential for renewable energy, a focused strategy for transition is still lacking. These large differences in eco-friendly energy generation between countries and regions are supposed to illustrate the fact that any environmental analysis must include energy generation. If not, emission may simply move from the tailpipe to the smokestack. Ergo, the environmental impact of automobiles needs to be assessed on a lifecycle basis. Miotti et al. (2016) compare the CO2 emissions of three different vehicles over the course of a hypothetical lifespan of 270,000 in the US Midwest (Miotti et al., 2016) (Table 3.1).

3.2 Sustainability: A Wicked Problem

45

Table 3.1 Lifecycle emission analysis of selected vehicles (Source: based on Miotti et al., 2016)

Powertrain Production emissions (kg CO2 equivalent) Use emission—270,000 km (kg CO2 eq.) End of life emissions (kg CO2 eq.) Lifecycle emissions 270,000 km (kg CO2 eq.) Lifecycle emissions per km (g CO2 eq./km)

Tesla Model S P100D battery-electric

BMW 7 Series 750 xDrive combustion engine

12,204

8190

Mitsubishi Mirage combustion engine 4752

48,600

95,310

46,980

311

351

159

61,115

103,851

51,891

226

385

192

On a lifecycle emission per km basis, the Mitsubishi Mirage does exhibit lower CO2 emissions than the Tesla Model S, illustrating that EVs are not more environmentally friendly per se; it is not a simple black and white situation as often depicted in the media. There are numerous different studies with vastly different outcomes, some reaffirming the notion of Miotti et al., while others conclude that EVs are much more environmentally friendly than traditional internal combustion engines. In a recent study conducted by the Technical University of Eindhoven on behalf of the Green Party in the German Bundestag (August 2020), the authors find that EVs do emit vastly less CO2 emissions over the lifecycle than assumed in other studies (Hoekstra & Steinbruch, 2020). That study concludes that this is mainly related to an exaggeration of the emissions during battery production, an underestimation of battery lifetime, and the assumption that the origin of electricity will not be in the transition toward renewable sources. Most recent studies reaffirm the notion that EVs do have a smaller environmental impact than conventional combustion engines. Moreover, EVs production and battery capacity are progressively advancing, which will result in even more favorable environmental assessment on EVs. Due to several circumstances, such as well-established manufacturing and service stations, lacking infrastructure, charging time, lifecycle of cars, and price issues, the 2020s will see a steep rise in the adoption of EVs, but this decade will see also a mix of different powertrains on the streets. Exhibit 3.8 displays the main alternatives, internal combustion engine (ICE), electric battery (BEV), and fuel cell (FCEV) (Martyr & Plint, 2012). The combustion engines can further be differentiated in combustion engine powered with gas or diesel, combustion engine with an additional electric battery (in most countries referred to as a hybrid or plug-in hybrid), and combustion engine with synthetic fuels. In the wake of the Volkswagen Diesel scandal in 2015, legislators have aggressively pushed for new emission evaluations, processes, and standards for internal combustion engines. The remaining competitive advantage of ICEs is its price tag, in 2020 the manufacturing cost gap to BEVs is

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Powertrain

Combustion engine

ICE C. En. with Gas/Diesel

HEV & PHEV C. En. with add. electric battery

Synfuel C. En. with synthetic fuels

BEV Electric Battery

FCEV Fuel Cell

Biofuel Bioethnaol or Biodiesel

Fig. 3.8 Main powertrain alternatives (Source: own illustration, 2020)

estimated to be around $10,000 for a similar sized vehicle (MIT Energy Initiative, 2019) (Fig. 3.8). Costs for batteries have declined extensively and will continue to do so, yet any future price predictions encompass a high degree of uncertainty, as, for example, the cost of raw materials and electricity need to be included. Hybrid Electric Vehicles (HEV) and Plugin Hybrid Electric Vehicle (PHEV) combine a combustion engine with battery propulsion. The plugin variant lets consumers additionally charge the battery and usually has a higher range than the HEV. Synthetic fuels (sometimes synfuels or efuels), represent an interesting case, but from a market-point of view this technology is still in its infancy. In theory, synthetic fuels could utilize existing combustion engines and infrastructure, such as gas stations, while releasing zero emissions (van Vliet et al., 2011). The downside of synthetic fuels is mainly the complex and energy-intensive (and GHG emitting) production process. Different synfuels can be found in the market, they are most commonly based on hydrogen which is produced by the electrolysis of water. Then, carbon dioxide is separated from the atmosphere or from other sources. Afterward, a chemical synthesis and purification of the desired fuel is required (Hänggi et al., 2019). The unanswered questions of competitive costs, transportation, and storage underscore the notion that the technology is not ready for mass scalability as of 2021. Still, several industry players such as Bosch or Porsche and start-ups believe that synthetic fuels have a future market (Automotive World, 2019). Prometheus fuels, a start-up based in Santa Cruz, USA, take a different route to synfuels. They aim to produce fuels by filtering atmospheric CO2 utilizing water, electricity, and nanotube membranes (Prometheus Fuels, 2020). These fuels are obtained via direct air capture methods using renewable energy, thus producing fuels with zero net carbon impact. Another case are biofuels that also span a wide array of diverse approaches, most commonly they are associated with liquid or gaseous fuels, like bioethanol or biodiesel. Biofuels are produced from various sources of biomass and utilize the traditional combustion engine. As there are several different types with heterogeneous

3.2 Sustainability: A Wicked Problem

47

manufacturing processes, the environmental impact is ambiguous (Chang et al., 2017). First, the production of biofuels requires energy. Second, it may reduce greenhouse gas emissions, as for instance soybeans that could potentially be used as an energy source can absorb carbon dioxide (CO2) as they grow. The negative environmental effect of further land utilization might offset this positive effect. Combustion engines can also be powered by compressed natural gas (CNG) or liquefied natural gas (LNG). Several further alternative fuels or sources of power for automobiles like solar, wind, wood gas, ammonia, or wind can be identified and the abundance of alternatives defy an simple analysis. Fuel cell powered cars (often referred to as fuel cell electric vehicles, short: FCEVs, are electric vehicles that employ a fuel cell and are locally emission free since they only produce water as a byproduct (Thompson et al., 2018). The fuel cell is an electrochemical device that combines oxygen and hydrogen in order to produce electricity (Stan, 2017). This technology has several advantages: it is emission free, hydrogen is virtually available in infinite quantities, and it enables quick refueling and long ranges. Interestingly, several industry players take very opposing stances regarding efficiency, costs, and environmentally friendliness of the fuel cell technology. Firms like Tesla or Volkswagen believe that FCEV is inferior to pure batteryelectric technology from an efficiency, emission, and practicality point of view. Volkswagen believes it is a “clear case”, they publicly estimate the overall efficiency from production to wheel with battery-electric at 90–70% and with hydrogen at a 25–30% efficiency rate (Volkswagen, 2019). They believe that high cost combined with an expensive infrastructure favors battery electric vehicles heavily. In the same vein, a study by Capgemini reaffirms the position that the costs of vehicles, storage, transportation, and charging lead to the superiority of battery electric vehicles (Capgemini Invent, 2020). A joint study by Deloitte and Ballard in 2020 comes to a very different conclusion: They calculate that the total cost of ownership will be less than the ones of battery-electric vehicles and internal combustion engines by the years 2026 and 2027 respectively (Deloitte, 2020). Several companies such as Honda, Toyota, or Hyundai take the same view and invest heavily in fuel cell technology (Green Car Reports, 2020). The Chinese government has rolled out large-scale subsidies on various levels to promote the technology on the largest car market in the world (Nikkei Asian Review, 2020). The 2020s will see several changes regarding its powertrains and markets will most likely come to different solutions, as the energy supply differs but also the strategic alignment of their domestic champions. For example, if Toyota manages to produce reliable FCEV vehicles at a considerable price point, the Japanese government will certainly back this option. Apart from the powertrain, sustainability across the supply chains and during manufacturing has become important to various stakeholders, independent of the industry (Sarkis, 2019). The automotive industry with its vast and complex interconnections is no different. Most OEMs have committed themselves to lessen the environmental impact across their supply chains. With the rise of battery and fuel cell technologies, the idea of a circular economy becomes even more important. Supply chain constraints for materials such as lithium, a critical component for EVs, might be addressed by closing the materials loop. An

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elaborate circular economy can lessen the dependence on the production and mining of lithium and rare earth metals. Extending the lifetime and refurbishment of components also eases the environmental impact of cars and components substantially. As a variety of different stakeholders exercise considerable pressure on automotive organizations, sustainability has become a strategic priority. Regulators on different levels are commanding comprehensive and increasingly stringent measures to lessen the environmental impact of automobiles. A growing number of cities are restricting certain types of vehicles. Amsterdam plans to expel all gasoline and diesel vehicles from the city by 2030 (CNN, 2019). In 2019, the city of Stuttgart, Germany, started to ban older diesel cars from entering the city (CNET, 2019). On a global level, we are witnessing a soaring public awareness regarding the harmful impact of transportation on the environment. Consumers and public-interest groups demand commitment and transparency on sustainability efforts by automotive organizations, which turns sustainability itself into a key strategic dimension. Chap. 2 puts an emphasis of the environmental impact of car as the primary means of transportation; mobility services are explored in Chap. 5.

3.3

A Flood of Money

Several financial forces are reshaping the world economy, we are witnessing two major developments. First, the world and its entire population are getting richer. Second, wealth is no longer concentrated in western countries, emerging and developing markets have outgrown advanced economies on an aggregated level. From 1990 to 2019 the GDP grew on average around 2.19% in advanced economies, while emerging and developing markets grew around 4.93% (International Monetary Fund, 2020a). Certainly, developing economies have more space for growth, but with regard to the GDP based on purchasing power parities (PPPs), which is the currency conversion rate to conduct an international comparison of economic activity; in 2008, the emerging and developing economies overtook the advanced economies on aggregate. This trend is continually increasing. Figure 3.9 depicts the 70 60 50 40 30 20 10 0 1980

1985

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Advanced economies

2000

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Emerging market and developing economies

Fig. 3.9 GDP based on PPP, share of world in % (Source: own illustration base on IMF, 2020b)

3.3 A Flood of Money

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6 4 2

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0 2016

2022

2028

Fig. 3.10 Global middle class in billion (Source: based on Kharas, 2017)

share of advanced and emerging/developing economies from 1980 onwards (International Monetary Fund, 2020b). Second, we are witnessing an unprecedented expansion of the global middle class. According to the Brookings Institution, around 140 million people are added each year to the global middle class (Homi Kharas, 2017). In general, the middle class is defined as earning between two-thirds and double of the respective country’s median income. As seen in Fig. 3.10, an estimated 2 billion people will be added to the global middle class from 2016 to 2028. The majority of these new entrants will be formed in Asia, and these gains can be attributed mostly to two countries: China and India. Apart from this staggering development, growth in the United States and Europe was rather sluggish on several levels. In terms of GDP, the United States grew from 2000 to 2019 at 2.09%, while in the Euro area growth was even slimmer at 1.39% (The World Bank, 2020). The middle class in both these major territories is stagnating or even shrinking. After the financial crisis in 2007/2008, the middle class in countries like Germany, Denmark, and Norway witnessed a decline. Only some Eastern-European countries like Poland and Estonia with a relatively small middle class saw an increase of the middle class (Vaughan-Whitehead, 2016). In 1971, the middle class was made up of 61% of the US adult population; this number shrank to around 50% in 2015 (Pew Research Center, 2015). Europe mirrors this development to a certain degree, yet even with strong national differences. Today, the middle class in Europe makes up for around 60% of the population. The shrinking of the middle class was further propelled by the financial crisis in 2007/2008. In order to stimulate growth and prevent deflation, central banks in North America and Europe drastically lowered the interest rates to unparalleled levels, see Fig. 3.11. These numbers represent the rounded average per year of the federal funds rate (FED) in the United States and the key interest rate in the European Union (ECB) (European Central Bank, 2020; Federal Reserve, 2020). In general, low-interest rates aim to stimulate economic growth by lowering the costs of borrowing money. If a firm wants to build a construction plant or a consumer wants to finance a home, their costs of doing so are lower than in a high-interest rate environment. While the United States has started to normalize their interest rates after 2015, the European Central Bank held the nominal interest rate at zero. For commercial lenders interest rates went even below zero, for instance in September 2019, the ECB was charging banks
0.5% to hold on to their own equity (Bloomberg, 2020a).

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Shaping the Future of Mobility

FED

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Fig. 3.11 Yearly average interest rates in EU and United States (Source: own calculations based on ECB and FED, 2020)

Apart from the EU and the United States, interest rates are historically low in most countries, compared to the last decades. Several, at times opposing reasons for low-interest rates have been put forward, some are structural and some political. Gordon (2000) argues that advances in digitalization have not led to a productivity boost as prior innovations have. Digital products are simply less capital-intensive, think of an app for micromobility vs. an assembly line for trucks. Intangible assets, such as data or source codes, are of much more importance to firms than in the past and they require less capital for investment (Demary & Vogtländer, 2018). Demographic factors such as low or negative population growth and increasing life expectancy contribute to low interest rates (Weizsäcker, 2014). The secular stagnation hypothesis claims that the global economy chronically suffers from a lack of demand and consequently investments are low (Summers, 2015). This is further fueled by aging populations and reduced capital intensity. As these factors will likely be persistent in the future, interest rates will potentially remain low for an extended period of time. The Economist went even as far as calling out the “the eternal zero”, referring to a zero-interest rate future (Economist, 2020). To spark growth in already low-interest rate environments, central banks turned to a rather unconventional monetary policy in the form of large-scale asset purchases, which is collectively known as quantitative easing (QE) (James Cashman et al., 2016). Central banks aim to encourage investment and lending by increasing the supply of money and thus stimulate the overall economy. The FED in the United States and ECB in Europe are actively buying government bonds and other securities, thereby lowering the overall cost of money. The effects and consequences of QE are still highly disputed. For instance, Hausken & Ncube (2013) find that this monetary policy did not have a significant effect on GDP growth. Economists worried that after the financial crisis, the practice of QE could have led to high inflation, but in the following years inflation remained surprisingly low. QE did in fact globally increase the prices of most assets, such as stocks, homes, or gold (Brown, 2015). With the recent COVID19 crisis, large unprecedented QE programs have been infused to the markets around the globe, outshining the previous programs by far (Financial Times, 2020). Ben Bernanke, former chairman of the Federal Reserve, remarkably and possibly

3.3 A Flood of Money

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in Mio. USD

0 -2000

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Fig. 3.12 Operating profits of Uber (Source: based on Uber SEC filling, 2020)

conclusively stated (p.118): “The problem with QE is it works in practice but it doesn’t work in theory” (Frey & Iselin, 2017). One major consequence of this loose monetary policy is that investors, such as investment funds, are now flushed with capital. In general, low-interest rates do increase the demand for riskier ventures, since safe investments like government bonds are becoming less attractive (Mirdala & Canale, 2017). For instance, the return on a 10-year government bond issued by the UK yields as of August 2020 a mere 0.14% (Bloomberg, 2020b). Some government yields are even negative, think of Switzerland or Germany, meaning that investors are paying money for the possibility of lending money to governments. Financial institutions (such as credit unions or insurance companies) are forced into low-margin environments, as their traditional business model of lending becomes unattractive. As investors globally search for profits, many are turning to private equity, which is composed of funds that are not listed on public exchanges and are directly invested into privately held companies (Diller & Kaserer, 2009). The industry, which includes venture capital, was sitting on $1.45 trillion of cash ready to invest by the end of 2019, which was the highest amount on record by far (CNBC, 2020b). As most private equity funds are organized to have a limited lifecycle, often in the range of 7 to 10 years, the money within these funds has to be invested. This in turn drives valuations of private companies’ sky-high. This is the main reason why companies like Uber were able to burn through billions of dollars since their inception. Exhibit 3.12 displays the operating profits of Uber from 2014 to 2019 (Uber Technologies Inc., 2020). Just in 2019 alone, Uber has burned through over $8.5 billion. In a world not flushed with capital, investors would probably start at one point to become wary and would stop pouring money into the Uber endeavor (Fig. 3.12). These billions in venture money have already fueled the transformation of the mobility sector. Since 2010, over $220 billion have been poured into the industry (McKinsey&Co, 2019c). Investments are mainly targeting the following areas: e-hailing, semiconductors, sensors, connectivity, charging, and batteries. These numbers pose a strong challenge for incumbent firms, as over 90% of those investments stem from non-automotive players, like tech-companies, venture-capitalist, and private-equity firms. Geographically, the monetary flows are very unequally distributed and are mainly directed toward China and the United States. Figure 3.13 shows the aggregated venture capital investments in travel and mobility

52

3

China United States Singapore India Indonesia Germany United Kingdom France South Africa Israel

Shaping the Future of Mobility 55682 52407

8792 7451 3615 2901 1595 1328 1169 1071

Fig. 3.13 Venture capital investments in travel and mobility in $M (Source: based on Lufthansa Innovation Hub, 2019)

tech from 2010 to 2019, sorted by country (Lufthansa Innovation Hub, 2019). By far, China and the United States are the main destinations of investments in mobility and travel, potentially further manifesting their leadership role in the new mobility services. Singapore with its almost neglectable domestic market stands out as a hub for mobility ventures, but a large chunk of these investments is directed toward Singapore’s Grab, a mobile technology platform which offers various services, most prominently ride-haling (Crunchbase, 2020). Probably, the epitome of this development is Japan’s very own Softbank, a conglomerate holding with stakes in various sectors such as software, technology, energy, and financial companies (SoftBank, 2020). They actively invest in upcoming industries and their most prominent investment vehicle is called Vision Fund. Launched in 2016, it has raised $100 billion, making it the largest fund ever put together. Its strategy is simple: swamp the company of their choice with much more capital than their competitors. This process aims to create the winner in a specific category and is following the winner-takes-it-all logic, which states that in a digital environment one company will dominate their respective market, just as Amazon dominates online retail (Taplin, 2017). The vision fund invests heavily in mobility, they are one of the biggest investors in Uber, Ola, Auto1, and Fair (both car sales platforms) and they are heavily investing in new technology like autonomous driving (CBS Insights, 2020). For instance, in May of 2020, Softbank led a $500 million funding round for DiDi’s autonomous unit (Bloomberg, 2020c). Billions of investments and the tactic of creating winners have fundamentally changed the automotive and mobility industry over the last decade.

3.4

Synthesis of Chap. 3

The main forces that shape and determine the future of mobility are the emergence of ecosystems, rising public awareness on sustainability, and a rather unusual financial environment. Digitalization has enabled ecosystems to flourish. Multi-sided platforms organize economic activity efficiently, as they bring various stakeholders together. Conquering an ecosystem has proven to be extremely profitable and this is

3.4 Synthesis of Chap. 3

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Acquisitions by Year

Corporate Venture Investments 31

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Fig. 3.14 M&A Activity by Alphabet (Source: based on Crunchbase, 2019)

why several firms aim to dominate multiple ecosystems in different industry segments simultaneously. The FAAMG firms that rose to dominance via peculiarities of ecosystems are particularly eager to engage and control numerous verticals. Apple provides several network advantages that increase the value of a single iPhone, most prominently the App Store contains over 500,000 apps from different vendors for all kinds of purposes. With the App store, Apple follows the cathedral and the bazaar model, with centralized architecture and decentralized value creation. The company has gone even further with other platforms, some are mere copy-cats while others are innovative. Apple Music is copying Spotify, Apple TV+ resembles Netflix, while Apple Arcade is a gaming platform like Origin. The vertical integration of software and hardware is on a higher level than with their competitors, especially with the iPhone and iPad (Harvard Business Review, 2020a). Tesla seems to have studied Apple’s playbook very closely and applied it in the context of the automotive industry. Tesla pursues a highly centralized architecture, with in-house software development and synchronized updates for all models. Just like Apple, they rely on limited product variances and clear naming strategies. On hardware, although simplified, Tesla mainly develops and designs technology and manages the final assembly. Suppliers do manufacture large parts of the proprietary hardware design on behalf of Tesla. They employ decentralized value contributions in areas such as music, video, gaming, and entertainment. Companies like Hulu, Netflix, Twitch, Spotify, or Atari have all built dedicated applications for the digital ecosystem of Tesla. The FAAMG companies grew aggressively via merger and acquisitions, often acquiring direct competitors. For instance, Facebook faced upcoming rivals like Instagram and WhatsApp and ended up buying both competitors (Romm, 2020). In total, Facebook has purchased and shut down at least 29 companies. Google (now Alphabet) has been equally aggressive, acquiring over 270 firms in the last two decades. Of these 270 companies, 171 acquisitions have been direct competitors and only one has been challenged by the authorities. Blockbuster products like google maps (Waze), Android, and YouTube originate in take-overs. Through conglomerate M&A activity, the acquisition of totally unrelated business, Alphabet has even branched out into other areas like robotics or renewable energy (Wu & A, 2019). Figure 3.14 illustrates the M&A activity by Alphabet (Crunchbase, 2019). From

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2000.00% 1790.00% 1800.00% 1600.00% 1400.00% 1162.28% 1200.00% 952.10% 1000.00% 730.64% 800.00% 513.84% 600.00% 400.00% 188.79% 170.21% 79.70% 97.79% 82.78% 89.80% 200.00% 0.00%

Fig. 3.15 Relative share price development from January 2011 to January 2021 (Source: own calculations based on Yahoo Finance)

2004 onwards, Alphabet mostly acquired entire companies, up until the last few years, when strategic investments have become the primary modus operandi. This may be even a sign that Alphabet fears a potential headwind if the acquisition spree is continued as in recent years. The monopolistic tendencies by big tech indeed faced backslashes by regulators on several levels. Yet, compared to the overall size of the businesses the financial and competitive setbacks are only minor. For instance, in 2017 the European Commission fined Google €2.42 billion for abusing dominance with their search engine (European Commission, 2017). Smyrnaios (2018) even goes as far and describes these companies as oligopolies, which rose to power through anti-competitive practices. Many other verdicts on data, taxation, and competition reaffirm the notion that big tech companies will potentially be tighter regulated in the future. As of today, these companies still leverage the data flywheel effect and thereby further renounce any potential competition. They dictate the pace of their markets, amass billions in revenue, are able to attract world-class talent, and pursue expansive strategies. Figure 3.15 shows the relative share price development from January 2011 until January 2021 of FAAMG compared to the S&P 500, the Japanese Nikkei, German DAX, and selected OEMs (Verizon Media, 2020). They have vastly outperformed the indices, as even the S&P 500 includes these five tech giants, meaning that the S&P 500 development without them would be much bleaker. The global and almost total market domination of FAAMG companies has truly asserted the term winner takes it all in the digital era (Kuchinke & Vidal, 2016). An almost complete domination of domains or even entire markets has led to quasi monopolies and substantial market power. At the same time, at the other end of the spectrum, automotive OEMs are still immensely profitable, yet they all lack a coherent growth story and are consequently downgraded by investors. The winner takes it all phenomenon is not new, of course. Well before the digital era, we saw industry shakeouts in PC manufacturing, tires, televisions, and penicillin. The number of producers typically peaked, and then fell by 70 to 97% (Klepper & Simons, 2005). In the 1900s, around 100 independent car manufacturers called the U.S. their home, in the 1950s only 11 and today there are two and a half independent

3.4 Synthesis of Chap. 3

55

manufacturers, Ford, General Motors, and Fiat-Chrysler left. The issue now is that digital ecosystems are causing such disruptions to happen faster, more frequently, and potentially ending up with only one winner instead of a few. For automotive and mobility firms, ecosystems are imperative for two main reasons. First, an ecosystem is the potential entry point to any mobility service but also the opportunity to cross-sell or venture out into new business segments. This is why all incumbent players try to establish their own ecosystems. However, building a mobility ecosystem is quite difficult. It implies managing the delicate balance between tightly controlled in-house development and providing a space, where external providers can flourish. Second, the FAAMG’ s vastness of their platforms as well as their technological and financial capability reaches already far into the automotive and mobility sector. For instance, Google maps is dominating routing and navigation services and does already include ride-hailing or micromobility offerings in some markets. This position could easily be expanded through vertical or horizontal integration, think how Waymo’s (an Alphabet subsidiary) autonomous vehicles integrate into the Google maps universe. Big tech firms could rather easily revolutionize entire mobility markets and, as their M&A’s activities in the past has shown, are by no means afraid to do so. Any future mobility endeavor must strategically integrate sustainability. As for the concept itself, it checks all the boxes of a wicked problem. More so, the greater the misalignment of stakeholders, the more wicked the problem. This certainly applies to automotive and mobility sector with unequal participants, legacies, values, and priorities. Certainly, sustainability has become a strategic priority for automotive organizations. Governments, consumers, and investors are now pushing automotive organizations to change their ways of working, culture, and products. Investors and financial institutions started to consider sustainability efforts as strategic investment criteria (The Economist, 2018). Environmental, social, and governance (ESG) criteria have become an increasingly popular approach to evaluate firms in regards to potential investment opportunities. The world’s largest asset management company, BlackRock, with over $6 trillion investments under management does recognize ESG efforts as a key strategic directive for investment (BlackRock, 2020). With a presence in the boardrooms in over 1800 firms, BlackRock’s strategic direction carries considerable weight. Ergo, automotive and mobility organizations need to provide clear, comparable, consistent, and trusted sustainability disclosures. It might seem obvious, but the reality accentuates the wickedness of any disclosure efforts. Virtually all OEMs started to publish dedicated sustainability reports and commitments to reduce their environmental impact (General Motors, 2019). Yet, there is no comparable analytical framework that delivers synthesized information and guides companies on their sustainability disclosures (Searcy, 2016). Sustainability remains ambiguous. The 2020s will witness intense discussions on powertrain options, as the fate of companies highly depends on this very question. Different countries will come to different solutions that are aligned with the domestic champions, which in turn will lead to a further diversion of markets. It will be a highly politicized debate, as the focus will shift from a pure technological superiority focus to an administrative one. In 2020, the popular app TikTok became a plaything

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13.91%

5.1%

4.94%

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2.13%

2%

1.94%

1.8%

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Fig. 3.16 Patent holders in fuel-cell technology for automobiles in percent (Source: based on Statista, 2020)

in the Chinese-American trade wars (Sanger et al., 2020). This example could mirror the forthcoming debates on the powertrain of the future. As there are at least three major local emission-free powertrain options (BEV, FCEV, and Synfuels) available, the modal split is almost impossible to predict, especially if we look at largely opposing estimations in regard to these options. Figure 3.16 displays the distribution of patent holders in fuel-cell technology for automobiles. Asian companies clearly dominate the development of fuel-cell technology according to patent data, outrivaling American companies and European ones even more so, who apparently only play a marginal role in the progression of fuel cells (Statista, 2020). This reaffirms the notion that the advancement of technologies within the industry will be a highly politicized discussion and not solely about technology. From a strategic point of view, this represents a critical challenge for incumbent firms, as some such as Volkswagen go all in on BEV and might be financially strained if options like FCEV turn out to be favored by consumers or regulators. Even though sustainability is valued and demanded by customers as multiple studies show, this certainly did not resonate in the same manner regarding their choice of vehicles. The 2000s have witnessed a steady rise of the SUV. OEMs are offering these types of vehicles across segments due to the great demand by customers. For established automotive organizations, the various timelines on emission standards and technologies require a multilayered approach. The world is moving toward fewer emissions although at a very different pace with heterogeneous strategies and objectives. In combustion engines, OEMs, especially from Europe, exhibit a clear competitive advantage and can offer vehicles at a great price point while maintaining a decent margin. With a full-out focus on alternative powertrains, these OEMs might eliminate their main margin contributors too early, as other powertrains might not be as profitable in the beginning. The sources of energy are another highly politicized debate, which often defies a clear analysis and outlook. How and when the transition toward renewables takes place is certainly complicated and not predictable, yet it will influence the powertrain strategies of the industry. New entrants that offer mobility services are unshackled by any legacy commitments, thus enabling a clear focus on electrification. Most mobility providers like scooter-sharing firms, often do not even develop batteries in-house. They are

3.4 Synthesis of Chap. 3

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able to pursue green-field approaches and exhibit great flexibility in case new power options appear in the market. Strategically, but also economically, mobility providers do not rely on owning the technology, their business is services. The unprecedented expansion of the global middle class has fueled the demand for mobility and especially private cars over the last decades. This global middle class might serve as the growth engine for years to come and the industry should use this as an opportunity for growth. In Europe and North America, we can observe a light erosion of the middle class, potentially sidelining these segments as primary target groups. Certainly, the Danish middle class differs greatly from the Vietnamese middle class and in terms of potential spending power will remain ahead, yet in western countries the automotive demand seems to have plateaued. Consumers in western markets will increasingly lean toward new mobility services, like micromobility, as they might be more concerned about budgeting and sustainability aspects. Automotive companies in western markets will then focus on more luxury vehicles and potentially target older generations. Low-interest rates and the practice of quantitative easing have bolstered the pockets of private equity ventures. The surplus of money will keep investors pouring cash into several markets and most certainly large chunks of it will be poured into the automotive and mobility industry. Ubiquitous availability of capital with diminished investment options has somewhat distorted the competition between established organizations and newcomers. OEMs are expected to be profitable and even pay dividends to shareholders, while in terms of economic sustainability several mobility start-ups are rather feeble. Investors do not expect start-ups in the sector to become profitable right away. Ride-hailing companies are notorious for burning through billions, yet investors still keep pouring in money. The unbalanced expectation by investors hampers incumbents to seize new mobility markets. Disruption is driven by non-automotive companies, like tech or venture capital firms, as traditional automotive firms make up for less than 10% of all the investments in ACES technologies. The incumbent firms invest more in internal research and try to stay ahead of the curve via organic growth. A study by McKinsey finds that established automotive players issue around 85% of all patents in the transformative areas, such as sensors, batteries, charging, telematics, mapping, and connectivity (McKinsey&Co, 2019c). Large investments and the ability to scale without mass have allowed mobility start-ups to follow a strategy called blitzscaling (Harvard Business Review, 2016). This implies that these companies have reached considerable scope and market valuations, although mostly privately and not publicly, within a very short period of time. The rationale of blitzscaling relies on becoming the global first-mover at scale and then utilizing network effects. Although this approach is management-wise highly inefficient and burns through huge amounts of capital, basically all mobility sharing start-ups have followed this approach. Figure 3.17 shows the number of months the seven fastest mobility startups needed to reach $1B valuation, unicorn status (Lufthansa Innovation Hub, 2019). The quickest ones, Bird, Lime, and Hellobike, are all scooter or bike sharing companies, while Momenta, Zoox (acquired by Amazon in 2020), and Caocao Zhuanche are all companies that concentrate on autonomous driving technology.

58 Caocao Zhuanche Grab Zoox Momenta Hellobike Lime Bird

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Shaping the Future of Mobility 37 35 32 30 26

20 15

Fig. 3.17 Number of months to unicorn status (Source: based on Lufthansa Innovation Hub, 2019)

These sky-high valuations for start-ups in the autonomous sector are surprising at first sight, as their technologies are still years away from the mass-market (see Sect. 4.4). At second sight, investors are simply putting value to the enormous disruptive potential as well as the immersive technology setting of autonomous driving. This scale and speed that blitzscaling entails is unprecedented and principally incommensurable to the velocity of the automotive industry, which usually requires at least around 4–5 years to develop new cars. As venture capitalism has become de rigueur in the mobility industry, established players are trying to catch up. Firms like Bosch, Peugeot, Ford, General Motors, and Porsche are eager to buy into new mobility trends. BMW, for instance, has set up the rather independent BMWi ventures with investments in several companies that mark a strong departure from the relatively narrow-minded automotive industry. Their portfolio includes companies focusing on data encryption, data preparation software, plastic recycling, fleet automation, 3D-scanners, and logistics (BMWi Ventures, 2020). This wide array of companies underscores how the automotive industry is being disrupted by several uncorrelated trends and how the mobility industry and the automotive industry are converging.

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Reuters. (2020b). Renault and Nissan rebuild their alliance to ride out the coronavirus storm. https://www.reuters.com/article/us-autos-nissan-renault-alliance/renault-and-nissan-rebuildtheir-alliance-to-ride-out-the-coronavirus-storm-idUSKBN232373 Romm, T. (2020, July 29). Amazon, Apple, Facebook and Google grilled on Capitol Hill over their market power. The Washington Post. https://www.washingtonpost.com/technology/2020/07/ 29/apple-google-facebook-amazon-congress-hearing/ Sanger D. et al. (2020, September 13). Oracle chosen as TikTok’s tech partner, as Microsoft’s bid is rejected. The New York Times. https://www.nytimes.com/2020/09/13/technology/tiktokmicrosoft-oracle-bytedance.html Sarkis, J. (2019). Handbook on the sustainable supply chain. Research Handbooks in Business and Management Series. Searcy, C. (2016). Measuring Enterprise Sustainability. Business Strategy and the Environment, 25 (2), 120–133. https://doi.org/10.1002/bse.1861. Skilton, M. (2016). Building digital ecosystem architectures: A guide to enterprise architecting digital technologies in the digital enterprise. Business in the digital economy. Palgrave Macmillan. https://www.loc.gov/catdir/enhancements/fy1613/2015019853-b.html Smyrnaios, N. (2018). Internet oligopoly: The corporate takeover of our digital world. Digital activism and society. Emerald Publishing Limited. SoftBank. (2020, October 29). Company Info | About Us | SoftBank. https://www.softbank.jp/en/ corp/aboutus/ Stan, C. (2017). Alternative propulsion for automobiles. Springer International Publishing. https:// doi.org/10.1007/978-3-319-31930-8. Statista. (2020). Trends in der Automobilindustrie. Stevens, A., & Strauss, L. (2018). Chasing digital: A playbook for the new economy. John Wiley & Sons Incorporated. https://ebookcentral.proquest.com/lib/gbv/detail.action?docID¼5478978 Strategy Analytics. (2017). In-vehicle UX report. https://www.strategyanalytics.com/accessservices/automotive/in-vehicle-ux/reports/report-detail/consumer-interest-for-in-carsmartphone-mirroring-is-almost-universal#.Wd942WhSw2w0.3067323840854923 Summers, L. H. (2015). Demand side secular stagnation. American Economic Review, 105(5), 60–65. Taplin, J. T. (2017). Move fast and break things: How Facebook, Google, and Amazon cornered culture and undermined democracy. Pan Macmillan UK. TechCrunch. (2019). Baidu announces Apollo enterprise. https://techcrunch.com/2019/01/08/ baidu-announces-apollo-enterprise-its-new-platform-for-mass-produced-autonomous-vehicles/ The Economist. (2018). What is sustainable finance? https://www.economist.com/the-economistexplains/2018/04/17/what-is-sustainable-finance The Guardian. (2015). Documents confirm Apple is building selfdriving car. The Verge. (2019). GM will use Google’s embedded Android automotive OS in cars starting in 2021. https://www.theverge.com/2019/9/5/20851021/general-motors-android-auto-googleinfotainment The World Bank. (2020, August 12). GDP growth (annual %)–European Union, United States, China | Data. https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG?end¼2019& locations¼EU-US-CN&start¼2000 Thomasson, E. (2017, September 14). Sheryl Sandberg assures German automakers that Facebook isn’t building a car–Business Insider. Business Insider. https://www.businessinsider.com/ sheryl-sandberg-assures-german-automakers-that-facebook-isnt-building-a-car-2017-9?r=DE& IR=T Thompson, S. T., James, B. D., Huya-Kouadio, J. M., Houchins, C., DeSantis, D. A., Ahluwalia, R., Wilson, A. R., Kleen, G., & Papageorgopoulos, D. (2018). Direct hydrogen fuel cell electric vehicle cost analysis: System and high-volume manufacturing description, validation, and outlook. Journal of Power Sources, 399, 304–313. https://doi.org/10.1016/j.jpowsour.2018. 07.100.

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4

Forms of MaaS

4.1

Drive

After exploring the developments that lead to a new mobility regime in the first place as well as examining the forces that shape its future, this chapter takes an inside look into the current forms of mobility and the promise of autonomous technology. I categorize present-day forms of new mobility into drive, be-driven, and ecosystem, see Fig. 4.1. Each mode has several subcategories, which are described hereafter. For illustration purposes, numerous examples are provided throughout the chapter, but it should not be regarded as a concluding overview, as there are myriads of firms with different value propositions in an ever-evolving market. Drive encompasses all modes where consumers steer the vehicle themselves and is further broken down here into finance & leasing, subscription, corporate fleet management, car sharing, and micromobility. Apart from purchasing a car on its own, financing, at times called credit, and leasing (hereafter: F&L) have become quite popular within the last two decades. Even though financial solutions have been around for much longer, with General Motors being the first OEM to establish a non-banking financing source for car loans in 1919, only within the last two decades, financing and leasing have become the primary source of acquiring vehicles (KPMG, 2012). In 2019, around 84% of US and 78% of German new cars were financed or leased (Bundesverband Deutscher Leasing-Unternehmen, 2019). Financing a vehicle means to carry a rather large one-time payment and pay off the debt over several years to a financial institution. Leasing provides the customer with a vehicle for a fixed period of time. After this tenure, customers can either purchase the vehicle for its residual value or return it to the leasing company (KPMG, 2012). For OEMs, F&L solutions have become a cornerstone of their profitability; all OEMs own banking or leasing licenses through their financial institutions, called captives. A study by Strategy& in 2019 puts the profitability of selling vehicles at around 5–7%, while F&L solutions are basically double than that, around 10–15% (Strategy&, 2019). Certainly, these captives offer further solutions, e.g., insurance or fleet management, but essentially, they have been incredibly # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Ackermann, Mobility-as-a-Service, Management for Professionals, https://doi.org/10.1007/978-3-030-75590-4_4

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4 Forms of MaaS

Drive

Be-Driven

Finance, Leasing & Subscription

Ride-sharing

Fleet management Ride-hailing Carsharing Micromobility

Ecosystem

Integrated Mobility Ecosystem

Bus & Shuttle

Fig. 4.1 MaaS modes (Source: own illustration, 2020)

profitable with just two single products: financing and leasing. The captive business model will be challenged in the future by several independent forces (Deloitte, 2018a). For instance, increasing regulation puts capital requirements for F&L under pressure and may have a decreasing effect on profitability. A general shift away from ownership will yield new business models in the industry. New and upcoming mobility modes like car or bike sharing are increasingly favored by consumers and regulators alike and will force captives to change. Across all industries, a number of very different types of subscription models are rapidly gaining traction. Popular industry inspiring frontrunners are Netflix and Spotify. Some even call this the dawn of the “new subscription economy”, where virtually all products and services will be based on subscription (Tzuo & Weisert, 2018). In an automotive context, subscription models have become quite popular, even though the first large-scale subscription models have only been introduced in 2017 (Oliver Wyman, 2019). There is not a coherent definition for vehicle subscription, since it is such an embryonic concept (KPMG, 2018). In principle, it revolves around the notion of temporary ownership with increased flexibility for customers, e.g., switching cars according to seasons. Subscription modes are designed to be a convenient alternative to leasing. With subscribing, customers pay a monthly fee including vehicle access, tax, maintenance, insurance, and servicing, with gas or electricity being the only incremental consumer expense. The subscription model is supposed to alleviate the upfront capital requirements and long-term contracts for customers, especially younger generations. This rather new playing field for the industry is populated by start-ups (e.g., Fair or Cazoo), rental-car companies (e.g., Hertz and Enterprise), and OEMs (e.g., Porsche, Hyundai, or Cadillac). These vendors have been introducing a variety of programs to the market, varying in length, vehicle options, and pricing. Subscription provides greater flexibility and convenience for customers, but might potentially be financially less attractive for the industry than traditional financial solutions. Moreover, as subscription services offer the choice to replace the car as desired, this opens up another battlefield for the consumer interface. As subscription services are rather recent and most players put market share before profitability, conclusive data on margins of subscription models

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are not yet available (Strategy&, 2019). In addition, the impact of the subscription model heavily depends on the rationality of customers. In their seminal paper “Paying not to go to the Gym”, Stefano DellaVigna and Ulrike Malmendier examine how and why US gym members choose different types of contracts (DellaVigna & Malmendier, 2006). They conclude that customers do not rationally choose their contracts and often pick contracts that are disadvantageous regarding their individual behavior. For example, they find that members choosing a flat monthly fee of over $70 visit the gym on average 4.3 times a month. This means customers pay per visit more than $17, even though they could use the 10-visit pass and pay only $10 per visit. This behavioral pattern can be observed across many industries. In the automotive context, this implies that if subscription customers will choose to interchange the car rather often, then this business model will likely not be a big margin contributor for the industry. Corporate fleet management is another substantial margin contributor in the automotive value chain and it implies handling the corporate fleet for mid to large corporations. Usually designated corporate car sharing providers, called fleet management companies (hereafter: FMC), take upon the execution. FMCs aim at reducing the total cost of ownership for their customers and typically offer services over the entire life cycle of a vehicle, such as contracts, vehicle selection, financing, registration, insurance, and maintenance. In Europe, nearly two out of three new cars are sold via the corporate channel. This is rather unique from a global perspective and can be attributed to favorable tax treatments and behavioral motives, e.g., status thinking. This is why fleet management in Europe is by far the largest market and is also the most advanced in many ways (Deloitte, 2017). OEMs have bought into this steady growth, e.g., Daimler taking over Athlon and BMW acquiring Alphabet (Fleet Europe, 2019). The first fleet management companies started handling corporate vehicles in the 1970s. Carsharing is an umbrella term that covers multiple modes of sharing. Generally, it is a short-term rental allowing users a choice of vehicle and pick-up/drop-off locations. Most of these types of services use smartphone apps or RFID cards to allow users temporary access to the vehicle. Carsharing has experienced consistent double-digit growth over the last decade and a half, especially in urbanized areas, where customers do not see the need for a personal car anymore. In 2021, carsharing is a well-established business model in North America, Europe, and Asia. The main value drivers for customers are a potential cost advantage and flexibility. Car ownership necessitates fixed costs like maintenance, fueling, depreciation, and insurance. Carsharing customers pay a time-based or distance-based usage fee. Certainly, the cost per kilometer is much higher in car sharing than with private ownership, implying that a direct substitution is only viable for customers with low annual mileage (Nourinejad & Roorda, 2015). Some carsharing schemes allow free flow parking within a certain zone, the so-called designated area. This area is geo-fenced using GPS and telematics technology, creating a virtual fence. Other services require the user to return the vehicle to certain locations. This is called station-based rental. Several companies have tried to mix and match free floating with station-based rental but few have come up with a viable model. Currently, most

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4 Forms of MaaS

Fig. 4.2 Carsharing business models (Source: own illustration, 2020)

Carsharing

P2P

B2C

Rental

Stationbased

FreeFloating

operators offer either free floating or station-based rental since the services require different operational routines. The two major distinctions are displayed in Fig. 4.2. The first distinctive mode is peer-to-peer (P2P) car sharing. This model utilizes privately owned vehicles, which are temporarily made available for shared use by an individual or members of a P2P company. The entire booking and rental process is enabled by intermediary platforms such as TURO in the United States or Drivy in France. These intermediaries process the transaction, offer insurance, and sometimes equip the car with telematics devices to ensure easy access (Wilhelms et al., 2017). On paper, peer-to-peer models solve business and environmental problems, but in practice, they represent only a tiny fraction of the global transportation market. Several startups, suppliers, and OEMs have ventured out into this space with several concepts, but large fractions of them have retreated. In 2014, for instance, Audi launched a project called Unite in Stockholm, Sweden (Nordic 9, 2020). The model was being hailed as an entirely new model of sharing, allowing up to five people to share a car via a booking app. Members of the group had a personal Unite beacon, which enabled the in-car system to recognize the individual member driving and charge them accordingly. Within the app, a shared calendar showed car availability and allowed for communication between the members. Although this concept looked innovative and as a resource-efficient way to share a vehicle, it never gained ground and Audi was forced to close the project in 2017. It remains to be seen if models like this will play a much bigger role in the future, since they encompass several advantages, but have not been picked up by consumers as anticipated. The second mode is business-to-consumer (B2C) carsharing that in terms of revenue and usage is by far the biggest market. This mode can be subcategorized further into rental (popular providers are Hertz & Alamo), station-based (e.g., Getaround), and free-floating (e.g., Share Now). Each mode incorporates different characteristics and features very different market dynamics. The traditional car rental model has been around for over 100 years (Hertz, 2020). Trips and rental lengths are considerably longer than other modes of car sharing. In the United States, customers rent out a vehicle on average for 10.1 days, while French customers rent out vehicles

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for 6.5 days (Rhinocar, 2020). Larger car rental companies aggregate rental stations in pools, which are groups of stations that share the same fleet (Oliveira et al., 2017). These pools are usually independent from other administrative divisions. The pool design may be frequently reallocated as a means to meet, e.g., seasonal changes in demand. A specific set of cars is assigned to each pool, often times consisting of different categories. Typical categories are compact car, midsize, or SUV. Generally, the size and the composition of the fleet are determined by the provider’s strategic positioning, e.g., a premium strategy yields primarily luxury vehicles in the fleet. Station-based carsharing has been around for over 20 years (Deloitte, 2017). A typical usage are round trips with the same start and end point. The length of the trips is usually much shorter than with traditional car rental, but longer than free-floating carsharing. Station-based carsharing lacks the flexibility of free-floating, but it can be sustainable in urbanized as well as medium-sized areas. It is often locally organized and seldom dominated by big international players, since success is often attached to regional particularities and local knowledge, which hinders the scalability of the service. For instance, LetsGo is a station-based carsharing provider in Denmark, operating only in the cities of Copenhagen, Odense, and Aarhus. They started their operations in 2007 and have been successful since (LetsGo, 2020). Free-floating carsharing is the most recent of the three types. Customers are able to pick-up and return the car anywhere within a predetermined area (Becker et al., 2018). Pioneered by car2go, the idea was conceptualized in 2007 and the first trials starting in October 2008 in Ulm, Germany (car2go, 2017). In March 2009, public pilot tests began in the city and the official market launch was 2010. Car2go quickly expanded globally and by 2012, they were present in several locations throughout North America and Europe. Shortly after the initial inception, several other companies began copying the free-floating idea, and the mode was widely available in urbanized areas. Free-floating rides are mainly short one-way trips (Deloitte, 2017). Operating areas are mainly highly urbanized agglomerations, which is why providers mostly go with small to medium-sized cars in their fleets. This ensures comparably easy handling and parking. The potential of carsharing in reducing private car ownership gained substantial attention in the industry and with policy makers (Liao et al., 2020). The automotive industry expects a significant share of their future sales via the sharing channel, whereas policy makers focus on the potential in relieving the negative externalities of car production and usage. A study by AT Kearney finds that the substitution effect between car sharing and private car use is rather limited (Kearney, 2019). Consumers mainly regard car sharing as a complementary service, at best replacing public transportation. Other studies reinforce the observation that carsharing only has minimal effects on the replacement of cars, while others find mild or even significant potential for substitution (BCG, 2016; Liao et al., 2020). Carsharing remains a very regional subject, highly dependent on local particularities. Consequently, the true impact and the market development of carsharing are currently unclear; it might very well significantly replace private car ownership, but could also remain a smaller market with limited application. Ownership of private cars has traditionally a longer lifecycle, in the United States around 7 years, in the European Union around 11 years

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(CNBC, 2017; European Automobile Manufacturers Association, 2020). Therefore, causality between private car ownership and car sharing is time-lagged, given that certain car sharing modes have just been introduced in recent years. Carsharing does and will look different in the future across Europe, United States, and China (BCG, 2016). In Europe, regional authorities started to disincentive private car usage in urban agglomerations, creating opportunities for providers. The US is geographically bigger and more sparsely populated than Europe; carsharing is therefore limited to a few densely populated areas. China presents a mixed picture. In cities like Beijing or Shanghai, car ownership decreases meaningfully due to the high costs of obtaining a license plate, immense operational costs, and restrictions. In lower-tier cities private car ownership remains a status symbol for the middle class, leaving little room for carsharing. Micromobility can be defined as an electrically powered transportation solution involving e-bikes, electric scooters (short: e-scooters), or any other lightweight vehicle used as a shared medium among multiple users (Porsche Consulting, 2019). Usually, three defining characteristics are being named:first, covering short distances of not more than eight kilometers; second, reaching a top speed of 25kilometers an hour; and third, a weight limit of 500 kilograms. This differentiates lightweight utility vehicles from other transportation solutions and thus excludes automobiles (Deloitte Insights, 2019). Here, we only examine shared micromobility. The first micromobility movement was a community bicycle program in Amsterdam, the Netherlands, starting in the late 1960s. The bicycles were bound to fixed stations, called docks. A rack-locking system was holding the bikes in place and the business model was based on subscription. Certainly, there was no website or app in place and consumers who wanted to jump on a bike, simply went to the stations and checked if a bike was available. If not, they were out of luck. In 1995, a company, which later became known as Bycyklen, started the first truly large-scale micromobility program with around 1000 bikes. A number of competitors followed suit and this first version of micromobility (station-based bicycle sharing) are still in operation today. Around 2000, Deutsche Bahn in Germany introduced the freefloating era. Bikes could be unlocked via SMS, later replaced with smartphone functionality, anywhere in a designated area. In China, several companies followed a similar scheme and although the market grew to some extent, it still was primarily a niche in the overall transportation market. The application of miniature electric motors gave rise to the next innovative breakthrough in micromobility, the birth of the e-bike, e-moped, and e-scooter. The first shared e-scooter program was introduced in September 2017 in Santa Monica, USA. By the end of 2019, e-scooter sharing services could be found in 626 cities in 53 countries, highlighting the exponential growth (EY, 2020). Numerous startups have adopted the idea of sharing electric scooters and started covering rapidly the most important markets in North America, Europe, and Asia. This swift expansion has been fueled mainly by venture capital; the incumbents in the automotive and transportation sector have only participated indirectly. The adoption by customers has been nothing short of impressive. According to EY, it took carsharing 7 and bike-sharing 6 years to reach a threshold of 10 million rides globally. Ride-hailing

NUMBER OF BICYCLES IN 1.000

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71 2,350 1,700 890 18

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Fig. 4.3 Number of shared bicycles in selected cities (Source: based on Roland Berger, 2018)

managed to reach this threshold within 4 years, while e-scooters reached 10 million rides in less than 2 years. In Europe, e-scooter sharing was introduced in 2018 and by 2019 major Europeans cities were basically bombarded with e-scooters. Just in 2020, there were over 20 million users in Europe alone (EY, 2020). This is mainly due to the fact that providers followed a first-to-market strategy, quickly taking over new cities, sacrificing profitability or operational efficiency. In Asia, especially in China and India, the micromobility market grew massively as well, almost doubling from 2017 (1 BN$) to 2020 (2 BN$) with over 3 billion users in total (Daxue Consulting, 2020). Ofo and Mobike, the Chinese market leaders, which cover around 90% of the entire market, put their focus on shared bicycles. China was once called the “Kingdom of bicycles”, being the biggest global market (Arthur D. Little, 2017). However, the unprecedented economic growth since the beginning of the twenty-first century has led to a rapid expansion in private cars and public transportation, whereas private bike usage dropped sharply. The introduction of shared systems as well as governmental systems have prompted a renaissance of the bike in China. Currently, the prevailing model in China is a combination of free-floating and docked systems. Users can end rentals anywhere in geo-fenced areas but are financially incentivized to leave bicycles at docks. Exhibit 4.3 displays the number of shared bicycles (non- and motorized) in selected cities around the globe in 2017 (Roland Berger, 2018). This underscores the incredible vastness and potential of the Chinese market alone, as there are far more users than anywhere else in the world (Fig. 4.3). Micromobility seems to have hit a sweet spot. In the United States, in the European Union, and in China, around 50–60% of all trips are less than 8 km (McKinsey&Co, 2019). Further, 35% of all trips globally cover distances of less than 2 km. All these trips could benefit or even be replaced by micromobility devices. Exhibit 4.4 displays the mean distance by device/utility in Germany (in KM) in 2019 (Civity Management Consultants, 2020). The highlighted bars show the three main e-scooter providers in Germany: Tier, Lime, and Voi. According to this data, micromobility serves rather as a complement to mobility offerings, not as a replacement for existing modes. The trip length varies heavily, depending on the city or country, as exemplified by Washington, DC, where the

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Public transpo. Car Subway Taxi Carsharing Bus Tram Bicycle Tier Voi Lime By foot

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11.5 9.5 8.4 6.4 5.8 5.4 4.9 3.4 1.96 1.81 1.75 0.9

Fig. 4.4 Mean distance by the device in Germany in kilometer (Source: based on Civity Management Consultants, 2019)

average e-scooter trip was 0.65 km and shared bike trips average 2.61 km (McKenzie, 2019) (Fig. 4.4). Micromobility can potentially be an environmentally friendly alternative for short-distance transportation and could serve as a connector between different modes and points of transportation. Since e-scooters are lightweight, the environmental footprint seems advantageous to other forms of transportation. To put things into perspective, a Tesla Model 3 weighs 2305 kg while an average e-scooter weighs around 23 kg. A single kilowatt-hour powers a Tesla Model 3 for 6.6 KM and an average e-scooter for around 1332.5 KM, stressing the efficiency of micromobility (Wired, 2020). The environmental impact of e-scooters is still disputed. A study at the North Carolina State University suggested that e-scooters produce more emissions per passenger mile than a standard bus with high ridership or an e-bike (Hollingsworth et al., 2019). This is mainly because the average life-cycle of shared e-scooters hovers around 3–6 months (as of 2021) and that the bulk of emissions is caused during production. However, all providers aim at greatly expanding the lifecycle of scooters in usage, which will ultimately lead to greener credentials. It seems rather premature to judge an industry that was formed in the last 3–4 years and that was primarily focused on accumulating market share. As such, e-scooters and other forms of micromobility are most suitable for bike lanes, but there is no clear roadmap on how to integrate e-scooters into the transportation sphere. To this end, e-scooters do not perform well in bad weather or in brick-lined streets and are not suitable for all age groups. Yet, micromobility could potentially replace up to 80% of personal travel in urbanized areas. The range of newer models, durability, and thus the lifecycle of e-scooters will improve drastically. This poses a direct threat to transportation providers and the automotive industry. In 2021, there is no regulatory framework on any national or supranational level. Local authorities have issued a myriad of heterogeneous legislations. Important facets such as charging, speed limits, parking, public safety, and vandalism are still handled differently on local levels. This hampers the profitability and the

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sustainability of the entire industry. For example, Madrid issued licenses to 22 different organizations, each assigned to designated areas. While some cities have embraced micromobility, like the Danish city of Arhus which set up designated parking hubs, others like Jakarta or Singapore have banned e-scooters entirely in 2020 (CNN, 2020). When in the spring of 2018, several companies launched e-scooter sharing in San Francisco, there was no regulatory framework in place, and the city was overrun. The San Francisco Board of Supervisors received “thousands of complaints” and then forced a temporary ban on all activities (The Guardian, 2018). The Municipal Transportation Agency of San Francisco conducted a several-months-long process to determine a regulatory framework. The city then required operators to provide user education, insurance, to share trip data, and offer low-income options. Potential providers were forced to deliver a detailed service plan, addressing safety concerns, sidewalk riding, parking areas, and potential vandalism. In the end, several companies submitted a permit application, but only two providers were initially admitted. The comparably strict framework of San Francisco illustrates how micromobility is regulated on a regional level and it seems reasonable to locally address micromobility conditions. Each city or urban area has very heterogeneous requirements, which need to be addressed individually. On a national level, a policy framework needs to be put in place, which allows for regional adaption and consistent adjustments since the industry will certainly further evolve and change within the next years. If micromobility truly wants to become a cornerstone of urban mobility, it needs to show if it can replace other means of transportation (e.g., cars) or if it just replaces walking on foot. To this day, the rapid rise of micromobility is still astonishing, given that designated infrastructure is only present in very few cities. The true potential of micromobility will be unlocked if cities decide to specifically design urban areas according to its needs. The future might also see new innovative micromobility vehicles down the road. For instance, the Swedish company Vässla produces a bike which is basically a mix between a bike and an e-scooter (Vassla, 2020).

4.2

Be-Driven

Be-Driven is be broken down as shown in Fig. 4.5. The three distinct modes are ridesharing, ride-hailing, and bus/shuttle. Ride-sharing, or sometimes carpooling, refers to shared vehicles among multiple consumers for a single specific trip, bringing together previously unacquainted consumers with similar origins and destinations (Sperling, 2018). These types of sharing entered the market in the early 2000s and have spread prevalently in Europe and the United States. Currently, two types of offerings can be identified. In the first mode, which was popularized by BlaBlaCar in France and Mitfahrgelegenheit in Germany (later acquired by BlaBlaCar) in the early 2000s, consumers are offering other consumers a seat in their private car for a, mostly predetermined, route, e.g., from Paris to Lyon. This type is primarily used for longer trips between cities. The second mode offers a shared trip in a vehicle driven by a commercial driver.

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BE-DRIVEN

Ride-sharing

Private Car

Pro. Driver

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Independent drivers

Bus Shuttle

Licensed Taxi

Fig. 4.5 Main Be-Driven modes (Source: own illustration, 2020)

Primarily, UberPool and Lyft Shared have been introducing this business model into the market since 2014. Consumers who are willing to share a trip along a similar path are rewarded with a discounted fare (Morris et al., 2020). Matching with co-riders happens “on the fly”, with new pick-ups and drop-offs, even route deviations can be added throughout the trip (Schwieterman & Smith, 2018). Consumers may be matched with multiple other riders or even ride solo, depending on the availability of suitable matches. This mode is applicable for shorter trips in urbanized areas. Ride-sharing providers act only as intermediaries; the quality, service as well the overall experience is handled by private consumers or commercial drivers, who collect a fee for their offer. The providers monetize agency fees via their platform. The two models share similar features and some companies even offer a mix of both variants. Several providers continue to experiment with pooling services, such as Uber or Moia, of Volkswagen, who let customers walk to certain inner city hot spots in order to enter the pooled ride. Ride-sharing represents a potentially sustainable solution since it could lead to an overall reduction of traffic congestion and is potentially economically favorable for consumers (Coppola & Silvestri, 2019). For instance, a study of New York city by scholars at the MIT concluded that 95% of the taxi demand would be met by just two thousand pooled vehicles (10 persons per vehicle), compared to the nearly fourteen thousand taxis that actually operate in the city (Alonso-Mora et al., 2017). Yet, it has been held back for several reasons. First, the general customer experience is often subpar, pricing is complex and largely opaque. Moreover, customers do not know what to expect in advance, neither the persons who they share the ride with nor which route they will take, which potentially will increase travel time. In a recent survey with 309 participants, Morris et al. (2020) found that drivers are largely dissatisfied with the shared commercial variant (Morris et al., 2020). Most drivers perceive their compensation to be unfair. Trips seem to be more difficult and stressful, involving confusing and rapidly changing trip instructions. Public opinion on this service presents a mixed picture. Pratt et al. (2019) analyzed over 2000 tweets about the services, finding that the most common tone is humoristic, but there is considerable negative tweeting about routing and travel time. This uncertainty and complexity of the service has often left consumers

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and drivers alienated, leading to an underwhelming scalability and monetization from a business perspective (Pratt et al., 2019). Ride-hailing or e-hailing services are almost ubiquitous mobility services around the globe. Despite the omnipresence, ride-hailing trips only represent about 1% globally of the overall number of kilometers traveled (Arthur D.Little, 2020). This type of service has grown faster than carsharing or ride-sharing in the last years and is expected to grow even further in the upcoming years; this is why companies like Uber, DiDi, Ola, or Lyft have literally been bombarded with venture capital (Crunchbase, 2019). Ride-hailing services exhibit several competitive advantages over traditional taxi operators. Operators allocate resources much more efficiently, dispatching vehicles is executed via algorithms (Rosenblat, 2018). Traditional radio dispatching and consumers calling call-centers in-person is replaced by digital solutions. Real-time information on demand and market conditions allow platforms to dynamically adjust pricing. On peak demand, high prices motivate drivers to participate, in case of a football match on Sunday, creating a bigger fleet. Consumers might benefit from more affordable trips during lower demand. This practice is only possible if regulation permits this dynamic allocation. In Europe, these ways and means are often forbidden, but allowed in North America. Ride-hailing platforms today tend to be based on two diverging business models. First, ride-hailing operators that work with independent drivers who use their own, non-commercial, vehicles. These platforms are often called transport network companies, short: TNCs, examples are DiDi and Ola. The first large-scale implementations of ridehailing dates back to 2008, all of which developed on the West Coast of the United States. Shortly thereafter, the biggest firms penetrated the entire North American, Asian, and European markets. All embrace an asset-light, peer-to-peer model (Sperling, 2018). In the second model, operators rely on licensed taxi companies and drivers to serve in their fleets. This leverages existing infrastructure and thereby circumvents the questions of operating license and labor laws. In principle, the already existing taxi business is digitalized, exemplified by firms such as FreeNow, LeCab, and Gett. Recently, we have witnessed the emergence of operators that combine both operating models. Most likely, both models will converge in the future, and depending on the legislative demands, only one model will remain. To continue, the initial proposition of Uber and others that drivers are independent contractors or even peers has been challenged on many levels. For instance, in June of 2020, California’s Public Utilities Commission presumed drivers to be employees under the AB-5, the state’s gig work law (Sonnemaker, 2020). Ride-hailing companies have often operated in a regulatory grey area and advertised their service as peer-to-peer, while in reality most rides are offered by professional drivers rather resembling traditional taxi operators (Henao & Marshall, 2019). In the traditional setting, taxi drivers needed elaborated knowledge of the cities and peculiarities. Digitalization and the smartphone with its ubiquitous information proposition, including routing and navigation functionality, have completely eliminated the need for local knowledge. The global ride-hailing market remains highly fragmented, as the traditional taxi lobbies exercise their political power and heterogeneous local regulations prevent global solutions. In North America regulation has

76 35 30 25 20 15 10 5 0

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Lyft

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Fig. 4.6 Average daily rides in million in 2019 (Source: based on Statista, 2020)

been sparsely thus far, that is why Uber and Lyft were able to disrupt the transportation market. Both companies combine for over 90% of the market, other competitors remain small. Europe is more disintegrated, providers on the continent offer local adaptions of ride-hailing due to different regulations. In Asia, DiDi, Grab, and Ola dominate the market and have experienced double-digit growth numbers in recent years. Figure 4.6 shows the global average daily rides as of 2019 (in Mil.) of the four most popular ride-hailing services, Uber, Lyft, Grab, and DiDi (Statista, 2020). This underscores how in terms of rides, DiDi leads the way, again highlighting the sheer size of the Chinese markets as DiDi mainly operates in China. For ride-hailing companies, a major challenge is to keep abreast with changing regulative environments, as legislation differs not just between countries but also regions and cities. For authorities, it remains a difficult task as well, as ride-hailing providers continue to change and at times even disguise their services. Traditional taxi operators are known for lobbying against ride-hailing platforms, but draconian regulation only hinders innovation and makes the consumer lose out. Ride-hailing operators do need to be regulated and recent examples from legislators, like California, could level the market, leading to desirable outcomes for society, consumers, and operators. Bus and shuttle ridership is in principle ride-sharing and ride-hailing with different vehicle types. The business and operational mechanics are strongly congruent. Two distinct business models can be observed in the market. First, longdistance (or inter-urban) travel with buses (Dürr&Hüschelrath, 2017). Departure, destination, and estimated arrival time are determined upfront, similar to public transportation. Provider examples are Flixbus or Greyhound buses. Flixbus started in Germany in 2013 and has become the largest provider in Europe and expanded into the United States in 2019. They rely on an asset-light model, principally providing a platform for independent bus companies while transpiring a strong corporate look and feel toward the customer (Gaggero et al., 2019). Second, there are mainly medium-range shared trips in urbanized areas in a shuttle-type vehicle. Notable companies are Moia (which belongs to Volkswagen) and Uber Bus. These operators offer singular ride-hailing and are matched with other users in the same direction, similar to ride-hailing (Gilibert et al., 2019).

4.3 Ecosystem

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Ecosystem

An integrated mobility ecosystem is the combination of the aforementioned and additional services (Wong & Hensher, 2020). An ecosystem strategy in mobility aims to integrate all of the aforementioned services as well as adjunct services, like parking, charging, ticketing, routing, and payment. Most OEMs and mobility providers pursue the objective to build up and dominate or at least command points of strategic control within an ecosystem. Different strategical archetypes can be found in the market. Vji et al. identifiy three broad archetypal models for the provision of MaaS (Vij et al., 2020). Figure 4.7 displays the models according to their degree of integration into local transportation. International and well-funded providers are likely to build walled gardens in order to keep customers within their ecosystem. They are closed platforms where the provider retains a tight control over product and services. Examples are Uber, DiDi, or Lyft. This approach in mobility services is largely inspired by and closely resembles a strategy employed by the FAAMG firms. The main rationale behind this strategy is to own the entire mobility ecosystem and keep competition out. For instance, Uber offers a variety of mobility services in some markets, such as ridehailing (with different options), ride-sharing, journey planning, scooters, and bikes. The recent lead investment of Uber in Lime, the largest micromobility provider, reaffirms this walled-garden strategy (TechCrunch, 2020). On a brokered platform, different mobility operators can choose to offer their services. The platform provider has to broker individual deals on a case-by-case basis with the operators. The platform might be run by an existing transport operator, such as a public transport operator integrating micromobility options or run by a third-party player such as a telecom operator or a specialized mobility platform, for example, ReachNow. A mobility broker has to negotiate with numerous operators, making the model difficult to maintain and scale. The open marketplace model is characterized as a situation where all transport services can be sold and resold on any platform. The aim of this model is to enable greater integration of mobility services and avoid asymmetries in the market. The open marketplace needs to be facilitated or governed by public agencies in order to ensure the general accessibility of the

Walled Garden Uber

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Whim Finland

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Fig. 4.7 MaaS ecosystem archetypes (Source: based on Vij et al., 2020)

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platform. Mobility services are in direct competition to each other. The model has been primarily championed by the Finnish national government who mandates an open data approach. All mobility operators must provide access to all information relating to their services, ticketing and payment to any other actor interested in integrating. This legislative approach potentially allows any mobility integrator to resell trips, think scooter rides, from their competitor on their own platform. In such a perfect competitive scenario, no supplier enjoys control over the market as all suppliers are now price takers. The walled-garden approach is certainly the most profitable solution for firms, but from a public policy point of view rather suboptimal for a number of reasons. First, integration between mobility services, from scooter sharing to ride-hailing and public transport, will be partial at best, as neither service wants to integrate direct competition. Second, the model may lead to monopolistic or oligopolistic situations, where only large operators strive. Third, privately operated walled gardens might lead to perverse societal outcomes. For example, ride-hailing firms do heavily subsidized rides in order to reach revenue and traction targets in cities, which could lead to a decrease in the use of public transport, ultimately resulting in greater congestion and higher emissions. The open marketplace model is motivated by consumer benefits and the fear of negative externalities from atomistic mobility. This model eliminates the network effect of ecosystems and thus has been met with strong resistance from mobility providers. In line with advancing sustainability efforts, increasing urbanization and expected tighter regulation mobility ecosystems will very likely be regulated by the public sector. Potentially, the biggest hurdle in establishing brokered platforms or even open marketplaces is the commercial viability. Low-profit margins in mobility services, ticketing, and payment might force public policy to financially nurture these platforms to enable holistic mobility. All studies agree upon the fact that the government has to play a central role in mobility ecosystems (Wong et al., 2020). While some advocate for government agency to assume the broker role, others believe that private ownership lead to more innovative solutions for customers and society. An integral service of any mobility ecosystem is ticketing, or often referred to as smart ticketing, a service that enables multimodal or end-to-end mobility solutions aiming to replace singular and paper-based systems. Although the smartcard technology has been around since the late 1960s, it only began to be used in the late 1990s, with Korea and Hong Kong leading the way (Ellison et al., 2017). For consumers, the need to physically purchase a ticket significantly aggravates the travel experience, for instance with the introduction of London’s smart ticket solution, the Oyster card, boarding rates increased by four times. Smart ticketing offers increased speed, convenience, and security against loss and theft (Turner & Wilson, 2010). Currently, there are four different types of smart ticket solutions available. First, a dedicated transportation smart card. Second, a contactless debit or credit card. Third, an app or integrative app on a smartphone and fourth, a smartphone emulating a credit or debit card (PWC, 2018). From a consumer perspective, all services such as public transportation, carsharing, ride hailing, and micromobility have to interconnect seamlessly. For instance, ReachNow in

4.3 Ecosystem Type 1

79 Type 2

Type 3

CCS2 (EU)

CHAdeMO

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Fig. 4.8 Overview selected connector types (Source: own illustration, 2020)

Germany enables Tier scooter rental, this resembles the brokered platform model. In this case, an individual deal between ReachNow and Tier has to be brokered, which hampers profitability and scalability for both operators, as ReachNow aims to dominate the ecosystem while Tier started initially with scooters but has gone multimodal with mopeds and also aims to sustain its own ecosystem (Intelligent Transport, 2020). Digitalization has led to the emergence of parking as a service (Deloitte, 2018b). Traditionally parking was, and often still is, a single paper-based punctual approach, geographically very limited. Lack of transparency on availability and pricing creates consumer frustration and considerable economic costs. For example, Americans spend 17 hours per year on average searching for parking (Inrix, 2017). Advances within the vehicle, smartphone, and infrastructure connectivity are changing the outlook for parking providers. Seamless and dynamic pricing can lead to higher utilization of existing space and higher consumer satisfaction. The electrification of the powertrain has led to several unusual partnerships and charging itself became a new business opportunity. For the consumer, charging networks are difficult to grasp, as multiple connector types are in use and networks compete directly with each other. Some networks are specific to OEMs, some are provided by the public and some are operated by independent charging networks. Across the world regions, incompatible networks have emerged. The industry has pursued diverse connector types to suit the needs of the local energy grid characteristics, but also actively lobbied to establish their own standards. Figure 4.8 shows an oversimplification of selected connector types. Apart from connectors, other types of charging variants can be found in the market, most notably wireless charging or battery swapping. It might take a few years until a single dominant design emerges or a few selected methods become established. While virtually all OEMs have joint charging partnerships or alliances, Tesla stands out with its own network and connector, following a high vertical integration (Neckermann, 2015). The company does not hold any network partners and it only has very selected retail partners, such as Target in the United States. As other OEMs or charging operators pursue different strategies and connector types in different world regions, Tesla has settled to establish its own standard globally. A fundamental cornerstone of any multi-modal ecosystem is routing functionality. All sharing services rely on some sort of location service and utilize GPS-technology or cell tower and Wi-Fi triangulation (Schreieck et al., 2018). Consumers are provided with real-time data and flexible routes, comparing the cheapest, fastest, or most environment-friendly route (Keller et al., 2018). Companies in that area provide a range of services, which include digital map

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tiles, geocoding which is the conversion of street names into coordinates and vice versa, points of interests, traffic-optimized routing, and incident information. Most sharing services rely on Google Maps. There are some competitors in the market but Google Maps holds a dominant market position. For instance, Uber relies on Google Maps and has paid $58million for their service between 2016 and 2018. This figure might even be deflated, since the venture arm of Alphabet holds a significant share on Uber (CNBC, 2019). In their SEC-filing, Uber states that Google maps is (p.46) “. . . critical to the functionality of our platform. We do not believe that an alternative mapping solution exists that can provide the global functionality that we require to offer our platform in all of the markets in which we operate.” (United States Securities and Exchange Commission, 2019). To counteract the overdependence on Google Maps, a few OEMs collaborated and acquired the mapping service Here from Nokia. The ownership structure at present includes BMW, Daimler, Intel, Bosch, Pioneer, NTT, and Mitsubishi (HERE, 2020). In general, the creation and maintenance of maps requires significant investments, technology, and expertise. Input for maps depends on various sources and machine-learning technology is applied for imagery processing. The imagery captured for Google’s Street View provided Google with the foundation to automate the extraction of street names, addresses, and business names from over 170 billion street view images across 87 countries. Google relies on content partners, it claims to lean on over 1000 authoritative data sources, including the US Geological Survey, the National Institute of Statistics (INEGI) in Mexico, local municipalities, and housing developers. Google enables its content partners to upload relevant content via their online tool (Google, 2019). Statistical analysis of GPS data from smartphones and vehicles are used to calculate traffic speeds, incidents, newly built roads, or newly implemented lane restrictions. Ticketing, parking, charging, and routing solutions require payment functionality, that is why automotive and mobility companies recognized its importance and picked their spot in the value chain (Willing et al., 2017). Simply docking on to existing payment providers such as Visa, Paypal, Klarna, or Ayden is a potential solution. It is not really a technical challenge to establish payment functionality, the process of applying and maintaining licenses is associated with considerable effort and functions as a market entry barrier. Recent examples of mobility and automotive companies lead to the conclusion that these companies do not want to rely on thirdparty services alone. Uber holds several banking or finance licenses and introduced a new division in 2019, Uber Money (Finextra Research, 2019). Grab and Gojek also developed new revenue streams by branching out into financial services. OEMs own finance or even banking licenses via their captives in most developed markets and are aiming to capitalize on these on their new mobility offerings.

4.4 Autonomous Future

4.4

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Autonomous Future

The previously described forms of mobility will certainly be disrupted with the advent of autonomous technology. In an autonomous future, ecosystems will hold even more importance, since all mobility transactions will be channeled through an ecosystem. In a true full-autonomous vehicle scenario, mobility might be a commodity, with the highest profits likely being generated by ecosystems with direct customer access. This is the main reason for the race toward mobility ecosystems. Autonomous driving can be classified according to the degree of automation. The most common classification in standards is the one by the International Society of Automotive Engineers (SAE). There are six levels of automation, starting with level 0 which implies no automation at all (Herrmann et al., 2018). At level 1, the driver controls forward, backward, or sideways movements of the vehicle, while the system performs the other functions. The driver has to constantly monitor the system and is only assisted by some features like cruise control. Level 2 implies the partial automation of the vehicle with automated functions, like acceleration and steering. The driver must maintain control of all driving tasks and monitor the environment at all times. Conditional automation is level 3, implying that the vehicle can run autonomously, but the driver must be ready to take control of the vehicle at all times. Level 4 connotes high automation, the vehicle is principally able to perform all driving functionalities, but the driver still needs to be able to take control of the vehicle and display a general awareness. With level 5—full automation—the vehicle controls all driving functions under all possible conditions (Coppola & Silvestri, 2019). The intervention of the driver is not required at any time or in any traffic situation. In this scenario, a child could be seated in the driving seat or there will not be any driver seat at all. Exhibit 4.9 summarizes the different levels and highlights the mental awareness levels (Fig. 4.9): The roads in a level 5 autonomous future might look very different. By 2040 half of all cars could be autonomous (Barclays Research, 2015). Most likely will we see four main types of vehicles with the advent of full-autonomous driving: (1) Non-autonomous traditional automobiles, with two main distinctions; an

Level 1 Assist Level: Early Warning Systems Feet Off

Level 2 Assist Level: Traffic Control Hand Off

Level 3 Assist Level: Awareness of Takeover Eyes Off

Level 4 Assist Level: General Awareness Mind Off

Level 5 Full Autonomous Driving Brain Off

Fig. 4.9 Automation levels in autonomous driving (Source: own illustration, 2020)

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Level 4 2025

Nvidia Tesla

2018

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Hyundai Renault-Nissan

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Ford 2012

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Fig. 4.10 Public statements regarding autonomous driving in 2018 and 2019 (Source: own illustration based on annual reports and public information)

affordable solution for performance and work, potentially powered by a combustion engine. A luxury variant for leisure, a classic combustion engine-powered automobile, which might be taken for a stroll on the weekends. Just like people drive old-timers today. (2) Family autonomous vehicle: A family might share one or two driverless vehicles, depending on the size of the family or its needs. (3) Shared autonomous vehicles: An on-demand chauffeur service without the physical driver. Most likely, these services will be found in urbanized areas, since the main application will be short and medium-range trips. (4) Pooled shared autonomous vehicles, where the vehicles serve multiple passengers simultaneously. These types of vehicles could vary massively, depending on the purpose, range, and pool size, imaginably ranging from mid-sized vans to double-decker buses (Herger, 2020). Small on-demand shuttles could serve the inner cities, picking up only a handful of passengers. These pooled shared vehicles could also potentially replace medium to long distance trips, in principle serving as a Flixbus or Greyhound Bus without the driver. It is currently unclear how the modal split of these four types of vehicles will be, since it depends on a variety of factors. First and foremost, regulation will be decisive but also the additional costs of autonomous technology. Current estimates present a mixed picture and it might even be the case that autonomous technology remains a premium feature in the near future due to the high additional cost (Nunes & Hernandez, 2019). All industry players, including OEMs, suppliers, tech companies, or start-ups have advanced toward level 2 and 3. The arrival of selfdriving capabilities was promised by several companies and it rather seems that mass-adopting will stretch well into the 2030s (Deloitte, 2019). In terms of communication, we have seen a race-to-the-bottom where one company publicizes timelines in order to achieve media and investor attention. This pushes other companies to adjust their own timelines to keep up. Most notably Elon Musk of Tesla has promised a full autonomous experience on multiple occasions, but the dates have gone by without the advent of this technology (Dhawan, 2018). Figure 4.10 shows selected timelines for the advent of level 4 and 5 as published by the respective companies in 2018/2019. As this book was finished in early 2021, none of the firms

4.4 Autonomous Future

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have come close to hitting their target. Reaching full autonomous capability may seem harder than anticipated. Many in the industry argue that achieving the first 90% toward level 5 is almost achieved. The last 10% in development, accounting for challenging and unexpected traffic situations, will be difficult to achieve and most likely take more time. Jen-Hsun Huang, CEO of Nivida, put forward the idea of a 99.999999% accuracy in the development of autonomous cars, whereby the last percentage point can only be achieved at very great expense (Herrmann et al., 2018). The demand for this extremely high accuracy can be explained by the tendency that humans are more likely to condone failures by other humans than failures caused by an algorithm. To this end, humans do not trust machines to the same extent than other humans. Furthermore, liability issues demand almost flawless technology in order to be practically operational for mobility providers and insurance companies (Chinen, 2019). From today’s perspective, a complete transfer of liability from the driver to the mobility provider should be assumed. When level 5 autonomous vehicles will hit the road, pre-programmed algorithms have to start to decide over life and death situations. This ethical debate is encapsulated by the trolley problem, a philosophical thought experiment (Abney et al., 2017). In the automotive context, it comes down to one particular statement: Should a runway trolley that threatens to run over six people be deliberately diverted to a sidetrack so that only one person is killed? This argument has been thought through by many different instances with different characteristics, yet the outcome depends widely on one’s own perception of moral understanding or intuition. A commonly-cited example is the tunnel case by Jan Gogoll and Julian F. Müller, illustrating potential daily complexity (Gogoll & Müller, 2017): Imagine you are sitting in your autonomous car going at a steady pace entering a tunnel. In front of you is a school bus with children on board going at the same pace as you are. In the left lane there is a single car with two passengers overtaking you. For some reason the bus in front of you brakes and your car cannot brake to avoid crashing into the bus. There are three different strategies your car can follow: First, brake and crash into the bus, which will result in the loss of lives on the bus. Second, steer into the passing car on your left—pushing it into the wall, saving your life but killing the other car’s two passengers. Third, it can steer itself (and you) into the right hand sidewall of the tunnel, sacrificing you but sparing all other participants’ lives.

Problems like these have to be understood commonly by society and solutions have to be agreed upon. In 2016, a group at the Massachusetts Institute of Technology Media Lab set up the moral machine and let users judge on moral problems via an online platform in an autonomous car context (Awad et al., 2018). It provides users with harsh but very real decisions. In case there are only two options, run over two children or five elderly people, what should the autonomous vehicle do? The authors collected some 39.6 million judgement calls in 10 languages from 233 different countries. The results show that there are clear regional ethical differences, for example, people from western countries like Norway or Canada favor different solutions than people from eastern countries like Japan or Korea. Future regulations

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might therefore differ regionally. As of now, there does not seem to be any omniscient solution to the trolley problem, in the future there has to be an active discussion from society on ethics and technology. The moral machine experiment implicates that legal or moral environments might rather be local than global. In addition, software itself is prone to errors. A tiny error, e.g., a missing punctuation mark, can lead to a system-wide failure. As an industry rule of thumb, programmers make between 0.5 and 50 errors in every 1000 lines of code they write (Why Companies Struggle with Recalcitrant IT, 2020.000Z). As modern cars contain tens of millions of lines of code, chances of a complete error-free system are close to zero. A prominent, although unfortunate example represents the Boeing’s 737 Max aircraft. After software flaws caused two fatal crashes, the 737 max was grounded and has led to turmoil within the industry and at Boing. The advancement of autonomous cars depends on many different technologies and factors (Herrmann et al., 2018). High-definition maps, cameras, LIDAR-sensors, radar, processing intelligence, and cyber security need to progress. Once a dominant design emerges, a harmonization of norms and standards for vehicle connectivity will occur. Put simply, once a fully autonomous car leaves France, it has to function in Switzerland, Italy, and Belgium as well. A 5-G network needs to be in place to enable reciprocal communication. In general, connected vehicles are equipped with advanced communication technologies that allow the exchange of information. Vehicle-to-Everything (V2X) can be further broken down into: (1) Vehicle-toVehicle (V2V) technologies that exchange data between vehicles, for example, to provide traffic alerts. (2) Vehicle-to-Infrastructure (V2I) technologies for exchanging data between vehicles and infrastructure, e.g., being warned about situations of danger. (3) Vehicle-to-People (V2P) technologies for exchanging data between vehicles and connected devices such as smartphones or wearables. (4) Vehicle-toNetwork (V2N) technologies exchange data between vehicles and cellular infrastructure, for instance to receive real-time information on traffic conditions (Coppola & Silvestri, 2019). An advanced artificial intelligence is a prerequisite in order to process the trolley problem and thus reaching autonomous capabilities (Deloitte, 2019). In an automotive context AI can be divided into two domains. First, perception enabling autonomy and second, interaction enabling personality (Augstein et al., 2018). Perception refers to the understanding of the environment around and inside the vehicle. It involves the analysis of data in order to identify and classify objects and the drivable area. The desired outcome is a highly accurate environment model. Autonomy implies the ability to navigate within the environment model. Interaction refers to the ability of the vehicle to communicate with the driver, e.g., understand hand gestures or speech patterns. Personality is created when the vehicle correctly understands the driver’s state and motivations so that a complete natural communication is established. In the AI context, the most challenging transition is between level 3 and level 4 (Deloitte, 2019). Classic rule-based and linear software functionality would need to consider every possible use case or combination of use cases in any given traffic situation, which is virtually impossible from level 4 onwards. Figure 4.11 illustrates the “if-then-else” functionality. There are two possible

4.4 Autonomous Future

85 IF (A = True) Then B Else C End If True

False

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Fig. 4.11 If-then-else statement (Source: own illustration, 2020)

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Fig. 4.12 Passenger costs per kilometer (Source: own illustration, various sources, 2020)

scenarios in this statement; (1) B, meaning if A is true, or (2) C, if A is false. This basic conditional statement is being used a million times each day, it is common in any software and even Microsoft Excel. Depicting all possible scenarios for autonomous driving is virtually impossible with this linear concept. Self-learning systems need to move beyond the “if then” approach and have to mimic human decisionmaking, predict behavior, and interpret data in real time. Potentially the biggest benefit of autonomous vehicles is the decrease in cost for mobility. Figure 4.12 compares the passenger cost per KM of today with self-driving vehicles. Based on several studies, which often outline a worst and best-case scenario, the cost reduction ranges from 45% to 82%. Numerous potential benefits of autonomous vehicles can be added to the list: Lower operating cost through lower parking, less energy consumption. Elderly, ill and other people that are unable to drive will perceive new means of transportation at potentially lower costs. Some studies suggest that lane capacity, vehicles per lane per hour, might be increased up to 500%, which would result in greatly reduced congestion (Anderson et al., 2014). Vehicles might even be able to park closer together, reducing the parking space area up to 30%. Consumers will significantly save time, depending on

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the study, ranging from two to eight hours a week (Capgemini Research Institute, 2019). They will use these timesavings for three main activities: work, entertainment/socialization, and relaxation (Fraunhofer IAO, 2016). This is the main reason, why so many companies pursue interests in autonomous driving, since the in-car experience can be utilized with media, retail, sports, healthcare, banking, and many more. The potential use cases are virtually limitless, ranging from rowing machines in cars to having an appointment with the doctor via the in-car screen. Selfdriving vehicles will come in many different form factors such as cars, pods or scooters and will lead to the ultimate convergence of industries, just as the smartphone did in the mid-2000s.

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Rosenblat, A. (2018). Uberland: How algorithms are rewriting the rules of work. University of California Press. https://ebookcentral.proquest.com/lib/gbv/detail.action?docID¼5507632. Schreieck, M., Pflügler, C., Soto Setzke, D., Wiesche, M., & Krcmar, H. (2018). Improving urban transportation: An open plat-form for digital mobility services. In C. Linnhoff-Popien, R. Schneider, & M. Zaddach (Eds.), Digital marketplaces unleashed (pp. 479–489). Berlin Heidelberg: Springer. https://doi.org/10.1007/978-3-662-49275-8_43. Schwieterman, J., & Smith, C. S. (2018). Sharing the ride: A paired-trip analysis of UberPool and Chicago Transit Authority services in Chicago, Illinois. Research in Transportation Economics, 71, 9–16. https://doi.org/10.1016/j.retrec.2018.10.003. Sonnemaker, T. (2020). Uber and Lyft drivers are now employees under California law, according to a new ruling from regulators. businessinsider. https://www.businessinsider.com/uber-lyftdrivers-declared-employees-by-california-regulators-2020-6?r¼DE&IR¼T Sperling, D. (2018). Three revolutions: Steering automated, shared, an electric vehicles to a better future. Island Press. Statista. (2020). Mobility services 2020. Strategy&. (2019). The 2019 strategy& digital auto report. TechCrunch. (2020). Uber leads $170M Lime investment, offloads Jump to Lime. https:// techcrunch.com/2020/05/07/uber-leads-170-million-lime-investment-offloads-jump-to-lime/ The Economist. (2020). Why companies struggle with recalcitrant IT (2020, July 16.000Z). https:// www.economist.com/business/2020/07/18/why-companies-struggle-with-recalcitrant-it The Guardian. (2018). Who ruined San Francisco? How its scooter wars sparked a blame game. https://www.theguardian.com/us-news/2018/apr/27/san-francisco-tech-industry-scootershomelessness Turner, M., & Wilson, R. (2010). Smart and integrated ticketing in the UK: Piecing together the jigsaw. Computer Law & Security Review, 26(2), 170–177. https://doi.org/10.1016/j.clsr.2010. 01.015. Tzuo, T., & Weisert, G. (2018). Subscribed: Why the subscription model will be your company’s future–and what to do about it. United States Securities and Exchange Commission. (2019). Form S-1 Uber technologies, Inc. https://www.sec.gov/Archives/edgar/data/1543151/000119312519103850/d647752ds1.htm Vassla. (2020, August 8). About us | Electric scooters for adults | Vässla. https://www.vassla.com/ about-us Vij, A., Dühr, S., Xiong, P., Cash, B., Gurd, B., & Boell, S. (2020). MaaS business models: Emerging business models in the digital economy, and lessons for Mobility as a Service (MaaS) operators and regulators. Wilhelms, M.-P., Merfeld, K., & Henkel, S. (2017). Yours, mine, and ours: A user-centric analysis of opportunities and challenges in peer-to-peer asset sharing. Business Horizons, 60(6), 771–781. Willing, C., Brandt, T., & Neumann, D. (2017). Intermodal Mobility. Business & Information Systems Engineering, 59(3), 173–179. https://doi.org/10.1007/s12599-017-0471-7. Wired. (2020, July 17). Forget the car. E-Scooters could save the city. https://www.wired.com/ story/e-scooter-micromobility-infographics-cost-emissions/ Wong, Y. Z., & Hensher, D. A. (2020). Delivering mobility as a service (MaaS) through a broker/ aggregator business model. Transportation, 17, 1340006. https://doi.org/10.1007/s11116-02010113-z. Wong, Y. Z., Hensher, D. A., & Mulley, C. (2020). Mobility as a service (MaaS): Charting a future context. Transportation Research Part A: Policy and Practice, 131, 5–19. https://doi.org/10. 1016/j.tra.2019.09.030.

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5.1

A Framework for MaaS

So, what is mobility-as-a-service today and what are its implications and challenges from a business perspective? MaaS is hailed as the one-stop solution that efficiently integrates all modes of transportation. It is the megatrend that disrupts automotive, mobility, and transportation and forms an entirely new industry. MaaS is enabled by digital technologies; fundamental changes within society and the ongoing urbanization necessitate a fresh perspective on mobility. Coined by some Finnish entrepreneurs in 2014, the term mobility-as-a-service has proliferated and evolved beyond its initial conception (Hensher, 2020). MaaS has been labeled many things, even a “sustainable post-car system” (Audouin & Finger, 2018). It is a multidimensional proposition spanning across independent actors in order to facilitate mobility. A high degree of vagueness surrounds the entire concept. Table 5.1 displays selected definitions, adapted from Sochor et al. (2018), highlighting various facets of MaaS (Sochor et al., 2018). Various keywords like multimodality, integration, service, bundling, ecosystem, seamless, accessibility, or door-to-door arise from these definitions and aid in comprehending the MaaS universe. As new mobility services are constantly emerging and evolving, it does not seem plausible to put a finite label on MaaS. More so, it is not a radical paradigm shift in mobility, MaaS is an extension of a decades-old idea of integrated mobility. It is an evolution of conceptualized propositions facilitated by digitalization, urbanization, and changing consumer demands. Table 5.1 is not about an exhaustive attempt to collect all existing definitions, it is rather indicative on how heterogeneously MaaS is perceived. Major reasons for these discrepancies are the several stakeholders that determine MaaS. Different types of stakeholders, each with dissimilar agendas, shape the proposition: cities and municipalities, passengers, operators, and service providers (Siemens Mobility, 2020). Cities aim to reduce congestion and emission while increasing accessibility and visibility throughout all mobility options. Passengers are looking for a single point of entry, including multimodal options and additional # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Ackermann, Mobility-as-a-Service, Management for Professionals, https://doi.org/10.1007/978-3-030-75590-4_5

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Table 5.1 Various MaaS definitions (Source: adapted and extended from Sochor et al., 2018) Kamargianni & Matyas (2017)

Ghanbari et al., (2015) Vij et al., (2020)

Burrows et al., (2015)

Hietanen, (2014)

MaaS Alliance (2020)

“Mobility as a Service is a user-centric, intelligent mobility distribution model in which all mobility service providers’ offerings are aggregated by a sole mobility operator and supplied to users through a single digital platform.” “MaaS, a multi-actor environment that provides seamless door-to-door services for end users by combining several modes of transportation.” “MaaS systems offer consumers access to multiple transport modes and services, owned and operated by different mobility service providers, through an integrated digital platform for planning, booking, and payment.” “MaaS can be defined as: The provision of transport as a flexible, personalized on-demand service that integrates all types of mobility opportunities and presents them to the user in a completely integrated manner to enable them to get from A to B as easily as possible.” “MaaS is a mobility distribution model in which a customer’s major transportation needs are met over one interface and are offered by a service provider. Typically, services are bundled into a package.” “Mobility as a Service (MaaS) is the integration of various forms of transport services into a single mobility service accessible on demand.” “The key concept behind MaaS is to put the users, both travelers, and goods, at the core of transport services, offering them tailor-made mobility solutions based on their individual needs. This means that, for the first time, easy access to the most appropriate transport mode or service will be included in a bundle of flexible travel service options for end users.”

services like payment. Operators intend to integrate their services into their own platform or into other value chains enabling a larger customer base. Within the MaaS universe, three main route types can be distinguished: point-to-point, adaptive and free-floating. Point-to-point trips are characterized by a designated start and end point. That applies to station-based carsharing with short trips and random pick-up times, car rental, docked bicycle systems, and to long-distance bus or shuttle with longer trips and fixed starting points. With ride-hailing and ride-sharing consumers are picked up and dropped off at their desired location. The adaptive or intelligent route is not predetermined, it unfolds due to customer demand and external environment, like traffic. Examples are Uber Pool or Moia, which offer ridesharing with multiple passengers to a desired target location. Along the way, additional customers are picked up and further destinations are added, the route enfolds along the way. Free-floating routes allow for vehicle placement anywhere in the designated area, vehicles can “float freely”. The area is geofenced, which means a virtual parameter confines users via GPS technology. Customers decide on individual routes anywhere within the designated area, they can basically leave and drop off vehicles to their desire. Configuring the optimal geofenced area is very individual for each city and it often involves a trial and error approach. Most micromobility offerings, such as e-scooters or e-bikes, are free-floating. The different types shape various business

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TRAVEL

INFRASTRUCTURE/CITIES

DRIVE

BE-DRIVEN

ECOSYSTEM

DELIVERY

LOGISTICS

MOBILITY-AS-A-SERVICE

PUBLIC TRANSPORTATION

Fig. 5.1 Mobility-as-a-service framework (Source: own illustration, 2020)

aspects such as pricing, duration of the ride, utilization, and management routines. Initial route types were rather distinctive to the respective business model. For example, operational practices for long-distance busing exhibit fundamentally different requirements compared to micromobility and it is impossible to realize any economics of scope. E-scooter providers do often rely on external staff for charging, the so-called juicers, which hover around the geofenced area. This staff cannot simply be utilized for daily operations in long-distance busing. However, in the last years, MaaS providers started to mix-and-match. The future will most likely see merging models, for instance, algorithm-based adaptive routing can be applied in the free-floating context. Based on these insights and the previous chapters, I propose the following framework for mobility-as-a-service from a business perspective, see Fig. 5.1. Drive, be-driven, and ecosystem make-up the three pillars for mobility solutions, they all interact with infrastructure and cities. The three pillars and the infrastructure constitute the mobility-as-a-service framework and define an entire industry. Automotive and mobility firms operate within the three pillars, brought forward in Chap. IV. Carsharing or e-mopeds rely on a given infrastructure, they are bound and molded by their surroundings. Mobility solutions condensed within the three pillars are thoroughly interwoven into infrastructure or cities. This is the main difference to private car transportation, which dominates most developed countries. The customer experience of purchasing and owning a Tesla model S is virtually the same around the globe, whereas a ride on an e-scooter in New York, Sao Paulo, or Tokyo is vastly different. Local peculiarities and legislative differences make up for a different, individual encounter as MaaS solutions are uniquely embedded into their environment. Individual agreements and permits with city officials have to be acquired. For instance, several cities around the globe have conducted intensive and demanding tenders in order to allow for micromobility services. Tenders like these can only be won by incorporating and building upon local knowledge. MaaS is not only available in urbanized areas by choice; it is rather confined to urbanized areas, as financial sustainability can only be achieved there. MaaS

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solutions necessitate a particular city size with a high local density in order to reach a threshold of users. A study by A.T. Kearney assumes that carsharing providers need at least a minimum population density of 6000 people per square kilometer and a city size of at least 500,000 inhabitants in order to break even (Kearney, 2019). Based on the example of Germany, they conclude that a mere five percent (4 out of 83 million) of the entire population lives in particularly dense areas and qualifies as potential customers. These findings can be applied for most mobility services to a different degree. Micromobility requires very dense areas whereas station-based carsharing requires rather little. This highlights the limitations of MaaS practices and that is why these services are not indefinitely scalable business models like Spotify or YouTube. MaaS operators are basically bound to dense areas and require a high manual and local knowledge. City-specific events like football games, fairs, street cleanings, and many more need to be accounted for on a local level. The different services condensed within the three pillars drive, be-driven, and ecosystem cannot be regarded as stand-alone premises. Services like routing, payment, and rental have to be considered as integrated solutions. Customers are taking up services based on convenience, preferably in a one-stop format. For instance, if two free-floating carsharing alternatives are present within a city, customers will very likely choose the closest one. Customers are no longer fixated on brands, they prefer convenience. The MaaS industry is adjunct to public transportation and travel, delivery, and the logistics industry. In academic research, public transportation represents an integral part of the MaaS proposition. This is entirely plausible since mitigating negative externalities (e.g., congestion) requires all modes of transportation to be part of the equation. Nevertheless, public modes like trains, subways, or public buses are mostly run by state authorities or by companies that act on behalf of the government. It is very rarely an open and competitive market and that is why, from a business perspective, public transportation has to be considered as an adjunct market of MaaS. Certainly, providers need to integrate and consider public transportation as part of the local infrastructure. From this perspective, public transportation can be regarded as a complementary service to increase utilization. As an indirect consequence, even public transportation is due for disruption (Deloitte Insights, 2020). The availability of multimodal options and increased data opportunities combined with simplified processing power leads to new possibilities for transportation agencies. The future of public transportation will very likely be determined by a much more profound and deepened relationship between MaaS providers and transportation officials (Canales et al., 2017). Demand for MaaS services is often higher in close proximity to central stations or other major public transportation hubs. Further adjunct markets are travel, delivery, and logistics. Digitalization and the increasing tendency of consumers to access services via ecosystems will ultimately lead to a convergence of MaaS, public transportation, travel, logistics, and delivery. In the following, the adjunct markets and its idiosyncrasies are described. Travel is one of the biggest contributors to the global economy, several estimates put it at a staggering 10% of global GDP (McKinsey&Co, 2018). Over the past decades, travel has seen steady growth, even when financial crises and pandemics have affected the

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global economy. The travel industry comprises the following subsectors: air travel, accommodation, passenger rail, cruises, touring and activities, and car rental. The market of logistics covers the subsectors of trucking, freight forwarding, rail cargo, air cargo, and ocean cargo and contributes approximately 5% to the global GDP. Each of these subsectors is a multi-billion dollar industry that has experienced tremendous growth over the last decades. For instance, from 2009 to 2017, US hotel gross bookings grew from $116 billion to $185 billion whereas airline revenue increased from $155 billion to $222 billion (Deloitte, 2019). Just for comparison, car rental in the United States jumped from $116 billion to 185 billion over the same period. The infamous last mile problem, the gap between distributor and consumer, has traditionally been solved by the consumer. Think how consumers flock into supermarkets and carry groceries to their home, bridging the last mile. The rise of online commerce (or e-commerce) gave leeway to an entirely new industry: delivery. This new industry addresses the last mile in a different way by serving the goods right at the consumer’s doorstep. Currently, four main consumer categories make up the delivery market: food and beverage, drugs and medical supplies, personal care, and pet products (BCG, 2020). As e-commerce continues to grow, many more categories may be added to this list. Companies that operate in the delivery market can mainly be divided into two categories: parcel delivery and instant delivery. Established players, such as FedEx and UPS, dominate parcel delivery. It is a decades-old market, yet in the last years, it has basically exploded due to the rise of e-commerce and advanced greatly thanks to elaborated supply chain management and digitalization (Deloitte, 2016). Instant delivery usually covers distances of up to 10kms, as otherwise, it becomes economically unfeasible. Start-ups like Deliveroo or DoorDash have risen rapidly in the last years by focusing on food and beverage delivery to the customer doorstep. Uber has already branched out into the market too, and their offerings Uber Eats has grown tremendously since its inception in 2014. Yet, the margins for delivery are apparently even worse than their ride-hailing services (Roberts, 2020). Even with the advent of elaborated routing, mapping, advanced logistics processes, and the smartphone, the fundamental and challenging economics of the last mile depend on two key factors (strategy+business, 2018). First, the route density—how many parcels can be shipped via the route. Second, the size of goods—how many items can be delivered via one delivery run. The lines between instant and parcel delivery have become blurred, as traditional parcel carriers like DHL and others have established same-day delivery. Amazon is pushing heavily its one-day delivery practice in many markets around the globe. This potentially foreshadows a wave of consolidations in a market with already razorblade thin margins. Mobility-as-a-service is not just a mere service functionality; it is fundamentally altering value chains in the industry. For most products of the twentieth century, value chains looked rather generic: supplier—manufacturer—distributor—consumer. Distributors were heavily integrated with suppliers and manufacturers. For most parts, distribution was controlled by manufacturers. In the automotive context, it meant that traditional OEMs like Ford or BMW are the centerpiece of all value

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streams. They dictate the terms and exercise control along the value chain, upwards and downwards. Suppliers or retail-outlets highly depend on OEMs, aiming to secure exclusivity rights for production or car distribution. The rise and consequent dominance of big tech firms made it clear that this value chain logic is no longer carved in stone. From a distribution perspective, it is not about locking in the supplies, it is now about locking in the consumers. Think how car dealerships lock-in exclusive cars, whereas companies like Uber or DiDi lock-in consumers. Digital intermediaries are now controlling the supply of consumers. The internet, with almost zero transaction costs, has neutralized the advantage of offline distributors. This is called Aggregation Theory, articulated first by Ben Thompson in 2015 (The Verge, 2020). In principle, the idea is not fundamentally different from a traditional high-street fashion store, which also aggregates consumers, yet in online transactions, the scale of aggregation is almost infinite and leads in various segments to single point of truth scenarios. Think how Google is aggregating on a global scale any search requests and thereby controlling the advertisement market. The same aggregation logic applies to MaaS. As of 2021, there are several mobility companies with vastly different value propositions competing over market share. Yet, history tells us that only very few companies might become true aggregators of mobility. Automotive firms are used to sell physical products on one-time transaction basis, MaaS services is a reoccurring value proposition. All this leads to direct customer interaction and implies vast consequences for organizational and operational routines. Figure 5.2 depicts the main operational layers in MaaS from an aggregation theory perspective. The decisive success factor in such a framework is the consumer experience, as this attracts more consumers, thus attracts more suppliers, thereby creating considerable leverage over other competitors. Apart from market dominance and substantial profits, integrated mobility experience can mitigate negative externalities. For instance, Ellison et al. (2017) find in an empirical study of Sydney residents that just simplifying the ticketing process results in significant car use reductions and increases in public transportation and walking. In regard to Fig. 5.2, consumers

Supplier

• Provides software & hardware solutions • For all layers

Manufacturer

• Production of devices, e.g. scooter or car

Provider

• Offers services & products, e.g. car rental • Directly or via ecosystem

Ecosystem

• Customer interface for variety of services • Additional services, e.g. routing

Consumer

Fig. 5.2 Layers in mobility-as-a-service (Source: own illustration, 2020)

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access mobility via ecosystems, which often offer a variety of mobility services like e-scooters, ride-hailing, and additional offerings like ticketing and payment. The existence of ecosystems has diminished industry barriers, that is why mobility, public transportation, travel, logistics, and delivery will most likely be accessed via one ecosystem. Think how Uber is already tapping its feet into ride-hailing, freight delivery, food delivery, and public transport (Uber, 2020). This industry convergence will only increase in the future as the adjunct markets of logistics, travel, delivery, and public transportation are all subject to the force of aggregation. A super-app for mobility is on the horizon. Providers in the framework serve products like car rental or services like routing directly or via ecosystems. Subsequently, manufacturers produce and assemble the device (e.g., e-moped). Suppliers provide hardware and software solutions for all layers. Firms as such are not bound to specific layers, they can occupy multiple layers and as recent examples have highlighted, various industry players intend to solidify their position by doing exactly so.

5.2

Assessing MaaS

Various heterogeneous mobility models materialized over the last decades due to a mix of technological development, urbanization, business innovation, and general regulative and environmental change in society. Yet several mobility providers, especially from the automotive establishment, have a tough time to compete in a constantly changing market. This is why the main MaaS modes, the ones we see in the markets today, are evaluated in the following sections toward profitability, scalability, complexity, and sustainability. These four dimensions represent the major challenges for establishing and running new mobility services. Scalability denotes how a service can be replicated for growth without disproportionate additional costs, like localization efforts. Complexity comprises the following dimensions: difficulty in building and providing the service, need for manual optimization, and innovation rate of competition. Sustainability denotes the environmental impact of the transportation modes via a lifecycle assessment. Life cycle assessment (LCA) takes into account the design, production, transport, utilization, maintenance, recycling, and disposal of the respective product or service. Profitability: digitalization has altered the way business is conducted on several levels. Agility, the way to quickly respond and adapt to an ever-changing environment has become de rigueur. Customers are engaged in bi-directional communication, demand instant feedback, and command unprecedented transparency over prices and vendors. Technology enables organizations to scale across borders, markets, and products. Digital ecosystems allow for vertical and horizontal integration utilizing network effects and lead to the elimination of intermediaries. For instance, Airbnb rose to prominence as a platform for accommodation, but now offers a subcategory called experiences such as restaurant bookings, treasure hunts,

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wine tasting, and much more (Airbnb Newsroom, 2020). How digitalization, unlimited choices, price transparency, and blitzscaling impact the profitability of businesses overall is not conclusive. In general, it seems that incumbents of any industry have become under pressure in the recent years, whereas digital vendors were able to increase profitability. Calligaris et al., (2018) have analyzed the markups of firms across 26 countries between 2001 and 2014. Mark-up is the amount of a selling price minus the amount of the input cost, serving as a proxy for profitability. They demonstrate that mark-ups overall have been increasing over time, but they are mainly driven by firms at the top of the mark-up distribution. Mark-ups are higher in digital-intensive sectors than in less-digitally intensive sectors and the disparity has increased over the sample period. These findings seem reasonable, as digital and intangible products have virtually zero marginal costs and enjoy benefits of scale. That is why companies like Apple and Alphabet are among the most profitable companies in the world. As incumbents in less digital industries are expanding in digital segments, they are increasingly confronted by the winner-takes-it-all phenomenon, as digitalization leads to industry shake-outs with very few winners. A study by Bain (2020) depicts the average growth rates of GDP and net income in major world regions from 1990 to 2018, see Fig. 5.3. In the United States and developed Europe, the net income of firms has grown almost twice as fast as the GDP, even more so in Asia. Massive disruptions like the dot-com bubble, terrorism, or the financial crisis of 2007/2008 have not derailed the consistent growth of corporate profits. Yet, according to Bain, while the average profitability has risen, the median profitability has actually decreased, meaning most of the increases can be attributed to a few larger firms. For example, in 2020, the top 1% of publicly listed companies in the United States accounted for 41% of corporate profits. Digital technologies have increased the benefits of scale and M&A activity has led to higher concentration rates in most sectors. Small and midsize firms, especially in non-digital industries, have therefore a much tougher time to realize profitability. Several trends are likely to increase the pressure on margins even further. A changing labor market in advanced economies might lead to shortages and rising wages. Advancing automation and digitalization might further cement the dominance of the already existing digital ecosystems. As these ecosystems continue to grow, smaller

8.3

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7.6 5.6

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3.3

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Developed Europe Nominal GDP growth

4.5 2.7

Developed Asia

Other dev. Economies

Net income growth

Fig. 5.3 Average growth rates of GDP and net income (Source: based on Bain & Company, 2020)

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companies might increasingly depend on tech companies as catching up is virtually no longer possible. For example, any mobility start-up will heavily rely on services provided by the FAAMG firms. Think of an e-scooter sharing start-up which connects to its users through an app. Most likely, they will rely on Google for maps, Android and advertising, Apple for iOS, Microsoft for Windows and productivity, Facebook for social media marketing, and Amazon potentially for cloud services. To build up any of these services in-house is too cost-intensive, as these big companies are replicating at a low cost their digital services to myriads of companies thus widening the gap to any potential rival. Corporations are increasingly trapped in the cobweb of big tech. The automotive industry has enjoyed strong growth and margins over the last decades, but several forces might change that pretty picture. The bedrock of profitability in the industry is financial services. Financing and leasing are much more profitable than directly selling the car as profitability for financial services hovers around 10–15%, while selling amounts to 5–7%. Certainly, this varies heavily across OEMs and a company like Porsche certainly exhibits higher contribution margins than Fiat. The new kid on the block, subscription services, are destined to grow at the expense of traditional financial services. Car subscription can be thought of as a leasing arrangement with an all-powerful customer, certainly eroding the high margins of financing or leasing. Selling or financing cars has traditionally been in the hands of dealerships, OEMs are rarely engaged in direct sales and in the some states in the United States direct sales are even prohibited altogether (Crane, 2015). The rationale of dealerships is to lure customers in stores and then up- or cross-sell on features and additional services. The digital era has put pressure on this arrangement, as customers increasingly demand transparency in the sales process. Tesla even went as far and skipped this established industry practice altogether, solely focusing on direct sales (Stringham et al., 2015). Their sales model is a combination of small outlets within cities and a bare-bones website, which allows for limited customization and direct order. Traditional OEMs rely on the manufacturer’s suggested retail price (MSRP) which does not mirror the total cost of the transaction. Even as customers are demanding transparency, OEMs and dealers are doing baby steps toward this direction, they are often hampered by the established sales structure. Nevertheless, more transparency on the customer side, as witnessed in numerous other industries, will most likely result in decreasing margins in selling, financing, leasing, or subscription. The traditional car rental business has been dominated by a few global providers, most notably Enterprise, Hertz, Avis Budget, Sixt, and Europcar. Each one of them operates in different markets and segments, ranging from budget to luxury vehicle rental. Two distinct business models determine the relationship toward OEMs. Companies like Hertz purchase cars in bulk at great discounts and remarket these cars after the rental tenure is over. This model heavily depends on the residual values of cars and is known as the at-risk model (Campbell, 2020). In the buyback model, rental companies agree to take on a certain amount of vehicles and the OEM will buy them back at a fixed price after rental duration. In principle, the profitability of these two distinct approaches boils down to the simple formula: more risk, more reward. If

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residual values are rising as well as the overall demand for used cars, then the at-risk model is more profitable, but in times of a market downturn, say a global pandemic, the buyback model represents a much safer bet. Profitability in the car rental industry ranges greatly, somewhere from 6 to 11% (Nedrelid Corporate Advisory, 2020). It would seem obvious that OEMs should simply vertically integrate and suck up the activities of rental companies, but several failed attempts have highlighted that this is no easy endeavor. High up-front capital expenditures, complex operational processes, and the issue of multi-brands, e.g., if Ford has to deal with the residual values of Renault, have largely refrained the OEMs from doing so. Traditional car rental is still a multi-billion dollar business, a far cry for station-based and freefloating carsharing services that struggle to achieve a positive margin contribution for several reasons (Lagadic et al., 2019). Station-based carsharing does not allow for growth at scale and is therefore often run by small and local players. Use cases are rather limited, as it does not solve the last mile-problem. If customers quickly want a car, they have to get to a station on their own. The more station a provider owns, the lesser the last mile-problem is, yet maintaining several stations, for instance in inner cities, is very cost-intensive. In the free-floating model, the generation of revenue depends on the duration of the ride and the usage frequency. As freefloating is restricted to a certain geo-fenced area, the duration of rides will be rather short, consequently the frequency of rides has to be high. Those are the main reasons why free-floating carsharing is only financially viable in few selected areas, as it necessitates a reasonable city size with a high local population density (Kearney, 2019). Traffic needs to move at a certain average speed, otherwise public transport is the much better alternative. This is why carsharing never took off in developing countries like India or Indonesia, as their cities suffer from high levels of congestion. In developing countries, you often find small buses collecting people along the road with a fixed route, a simple and non-digital alternative, yet very inexpensive and effective. To continue, as acquiring and maintaining a car is quite expensive, the aforementioned models barely edge into the black. Micromobility, especially e-scooters, seem to have very reasonable economics, explaining the massive influx of venture capital in the market. The cost of producing an e-scooter is somewhere in the range of 400–500$ and the profitability (unit economics) of individual trips ranges widely depending on the country and city, but it is estimated to be somewhere in between 0.30 and—0.75$ (BCG, 2019; McKinsey&Co, 2019). Figure 5.4 breaks down the unit economics of shared e-scooters (Porsche Consulting, 2019). Providers have to overcome the challenge of largely egalitarian offers, as e-scooter services are virtually identical. If consumers regard these services as commodities, their choice depends on proximity or price. The biggest cost block is charging and relocation and providers are under immense pressure to employ highly efficient operational routines. If cities and legislators continue to push micromobility and begin to lay out designated infrastructures, it could potentially create strong headwinds for operators of shared programs. The decline of sales prices in micromobility might accelerate even further and customers might outright abandon

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Fig. 5.4 Unit economics shared e-scooter in USD (Source: own illustration based on Porsche Consulting, 2019)

shared services and simply start to own devices again, whether it be a scooter, moped, or bike. A major issue for all ride-hailing operators is financial sustainability, most notoriously exemplified by Uber. All major players have burned through billions in the last years and many have questioned their long-term profitability. Initially, Uber pitched to their investors an asset-light business model with strong network effects, based on a first-mover advantage. Ample resources were supposed to create a “winner”, copying the quasi-monopolistic market dominance of the big tech companies in the mobility sector. Yet, the assumptions of Uber have not held up well over time, they were not able to create complete dominance, for a few simple reasons. Only weak network effects were created as drivers, who act independently, are simply offering their services via multiple vendors such as DiDi, Lyft, and Ola. Both passengers and drivers routinely switch between services, with no potential lock-in mechanism in sight (Sherman, 2019). Uber even lost ground to other competitors, like Bolt from Estonia, who raised much less capital and are still able to compete. As ride-hailing became a staple in the mobility patterns of users, a lot of thought is given to the commission of the platforms. As Uber takes around 20–25% from its drivers, services like Bolt take around 15% in Europe. For example, DiDi has entered the Australian market in 2020 and has publicly announced their commission to be at 5% (Derwin, 2020). Drivers talk about commission models very openly and even try to convince customers to switch services, because “it is better for drivers” (Various, 2020). It appears that ride-hailing business models are becoming a commodity and are less technologically complex to be mastered than previously assumed. The entire business concept of ride-hailing seems to suffer from

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one fundamental logical flaw. The traditional taxi business has never been profitable for drivers or operators without regulated supply and demand. It is still one of the most regulated industries in the world and in most cities; the market is heavily restricted and controlled through licenses. The poster child of this development is probably the taxi medallion system of New York City. In this closed market scenario, only medallion holders are able to provide taxi services. Interest in medallions spiked over the years, sometimes through shady business practices, as in 2013 the average medallion price peaked at around one million USD and has declined sharply thereafter, just before ride-hailing became a thing (Kamga et al., 2015). Ride-hailing companies do not operate in such a closed market scenario; they are consistently adding drivers to each city pool in order to stimulate positive network effects. This results in a race to the bottom scenario, considerably hampering any long-term profitability outlook for ride-hailing operators. Long-distance buses have been around for over a century, their business propositions have not altered much over the last decades. Nowadays, customers are able to interact and book the trips via the internet and the smartphone. The industry can be characterized by rather low profitability, stable and simple business models (European Commission, 2016). Scalability is hampered in this point-to-point system as adding new points (e.g., stops) slows down overall travel time. Pooling rides or shuttling represent an extension of the ride-hailing proposition. Of all MaaS modes, pooled rides and shuttle buses strongly correlate to public buses or trains. As public transportation is heavily subsidized, private providers will find themselves in a pretty competitive environment with very slim margins. Services that make up an integrated mobility ecosystem, like ticketing, payment, parking, charging, and journey planning are, as stand-alone propositions, characterized by rather low profitability while being quite complex to master. They are only viable when a firm wins a dominant position and can exercise its market power, thus generating heavy and recurring traffic. Owning an integrated mobility platform can potentially be very profitable, as it acts as the gateway to mobility and but also to non-mobility services. It seems reasonable to assume that all modes depicted in Table 5.1 will at one point be consumed by only very few or even one single mobility platform. This is why MaaS has often been described as the “Netflix of urban transportation”, a centralized gateway for decentralized value proposition in the transportation sphere (Reid, 2019). Scalability and complexity: scalability denotes how easily a service can be scaled, while complexity comprises the difficulty in building and providing the service, the need for localization, and the innovation rate of competition. Modes like purchasing, financing, leasing, and subscription can be ramped up at ease and are not too complex to achieve proficiency. A long-standing tradition in the automotive industry is to sell vehicles via third parties, the dealerships. Accelerated through the pandemic of 2020, the first customer touch point will increasingly be online and so will the final sales transaction. OMEs are already pushing heavily toward direct sales in selective markets and Tesla has delivered the proof of concept and the ease of selling cars digitally. The main hurdles holding OEMs back are the long-standing relationships and commitments with their retail partners and the fear of potentially

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cannibalizing sales. Yet, if not the OEM dominates the online sales channel, other players, potentially start-ups or tech companies, will. OEMs enjoy an inherent advantage in providing financial services, as they are able to bundle different products and services. Regularly, they hold off competition by indirectly subsidizing their offers. As for a theoretical example, if Volkswagen wants to ramp up sales of their Golf model, then instead of lowering the up-front purchase price, subsidizing the interest rates via Volkswagen Financial Services is the more sound option, which keeps the MSRP intact. Bundling artificially low-interest rates with motor insurance or additional tires makes any outside comparison, for example, a car credit from the Santander Bank, very difficult to evaluate for customers. The same logic should work in subscription services, and even though some new entrants populate this young market segment, OEMs should be able to dominate this sales avenue. Scaling services like station-based or free-floating carsharing is rather limited and quite complex to handle. They still necessitate large over-head costs, like apps and backend development, telematics solutions, vehicle maintenance, and customersupport. This is the main reason why carsharing has a very hard time to achieve profitability. Micromobility exhibits substantially lower vehicle cost, but still necessitates the same over-head costs. In free-floating models and in some cases for station-based offerings, a sustainable business model depends on three main variables: (1) designated, geofenced area, (2) operational efficiency, and (3) relocation. The business area is very individual to each city and is determined by the following variables: points of interests, income, mobility patterns, age of residents, charging facilities, ownership of vehicles like cars or bikes, average travel speed, public infrastructure, and the availability of designated parking or lanes. How freefloating systems function on a daily basis, the operational efficiency, is shaped by the following: solutions for parking, cleaning, maintenance, charging, handling of fees and accidents, availability of labor, labor laws, and the regulatory environment. Relocation, in principle, revolves around the notion that vehicles need to be transported from areas with low demand to areas with high demand. This can be quite challenging in real life. Consider the map, Fig. 5.5, of lower Manhattan and neighboring Brooklyn (Open Street Map, 2020). If demand in Duane, Thomas, and Worth Street in Manhattan is high and low in Water and Plymouth Street in Brooklyn, then vehicles should theoretically be relocated. Yet, it depends on whether the cost for relocation outweighs the financial gains, as several factors make up the cost for relocation: manual labor, distance, number of vehicles that can be relocated at the same time, utilization rate, and trip length at target location. These factors are very heterogeneous and individual to each city. The relocation equation will be vastly different in Berlin, Copenhagen, Vancouver, or Shanghai and has to be managed on a local level. The type of vehicle is another factor, as relocating a car is more cost-intensive than an e-scooter, as multiple e-scooters can be transported to the target destination simultaneously. Incentivizing users to relocate the vehicles themselves is another approach to address this problem. The two most common approaches are to provide users with free usage or discounted fares. Yet

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Fig. 5.5 Manhattan and Brooklyn (Source: Open Street Map, 2020)

these practices can only be applied to a certain degree, otherwise the mobility service will be confusing for regular customers. Consider our example, if the demand in Duane Street in Manhattan is high but to a different degree than in Worth Street, it is difficult to transpose this to a customer who is coming from Water Street in Brooklyn. A potential relocation calculation may go like this: a free-floating car that is relocated from Water Street to Duane Street may yield in a 70% likelihood that the next trip length will be longer than 5 km, but relocated from Water Street to Worth Street is only a 20% likelihood that the next trip will be longer than 5 km. This rudimentary equation serves only for explanatory purposes, as the true cost-benefit analysis of relocation is much more sophisticated. Regular customers will be completely overwhelmed and left confused when involved into these complex relocation schemes. It is one of the biggest cost-block and firms have to utilize complex algorithms and put highly efficient operational practices in place to achieve profitability. In addition, in recent years the aim of MaaS providers was to demonstrate the viability of their business models, driving up utilization with extensive relocation schemes and thereby sacrificing profitability for market growth. It has led to a distortion of the true demand for mobility, as consumers will have more vehicles at their disposal as opposed to a situation where providers have to prove themselves as financially sustainable. For an integrated mobility ecosystem approach, adding and maintaining services like ticketing, payment, parking, and charging often requires extensive contractual work and can only be automated to a limited degree. Public transportation is often

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organized at the local level by quasi-private corporations, which are owned or act on behalf of the state. For instance, the Metropolitan Transportation Authority (MTA) is North America’s largest transportation network throughout New York and compromises buses, subways, and rail cars (MTA, 2020). Combining all services across countries or even regions like the European Union implies the integration of countless stakeholders. For example, Berlin-based Omio provides multi-modal comparisons when traveling in Europe (Omio, 2021). When booking a ticket of Deutsche Bahn, Germany’s premier railway company, via Omio, the mark-up is in the low single-digits (Various, 2020). Public transportation firms or agencies often show little interest for multi-modal integration as they aim to provide these services themselves. This hampers greatly the financial viability of any vendor, who can only flourish by gaining a dominant market position and thereby attracting heavy traffic. Sustainability is a complex dimension. As the entire concept encompasses environmental, social, and economic aspects, we will here only aspire to evaluate the environmental perspective. Even this perspective is made up of various facets. The most comprehensive environmental breakdown is the life cycle analysis, which tracks every stage from inception, manufacturing, energy utilization in production and usage, recycling, and disposition. Life-cycle assessments in the transportation area usually consist of three main components. First, the vehicle component which entails the manufacturing, delivery, maintenance, and disposal. Second, the fuel component which compromises the well-to-tank (production, processing, and delivery of fuel or energy) and tank-to-wheel (in-vehicle conversion of energy into motion). Third, the infrastructure component is the construction, maintenance, and end-of-life management essential for vehicle operations, e.g., roads and gas stations. This type of evaluation has been applied by the Joint Research Centre (JRC) of the European Commission and the European Council for Automotive Research and Development (EUCAR). Yet, in order to evaluate new mobility-services, a fourth component has to be included, operational services like relocation, maintenance, or charging (International Transport Forum, 2020). During the life cycle of a vehicle, several emissions are omitted such as greenhouse gases, particulate matter, nitrogen oxides, sulfur dioxide, and carbon monoxide. As sustainability is so multifaceted, most studies tend to take a rather selective approach and focus on certain negative externalities and particular regions. Hereafter, selected studies on the various forms of mobility are presented in order to underscore the intricacy of sustainability. For instance, Nijland & van Meerkerk, (2017) survey carsharing participants in the Netherlands in order to quantify the effects on car ownership, car use, and CO2 emissions (Nijland & van Meerkerk, 2017). They found that carsharing reduces car ownership, but only to a certain degree as the shared vehicles mostly replace a second or third car. The participants drove 15% to 20% fewer car kilometers and emit between 240 and 390 fewer kilograms of CO2 per person. Vasconcelos et al. (2017) analyze the environmental and financial aspects for different station-based carsharing variants in a case study simulation based on Lisbon, Portugal (Vasconcelos et al., 2017). They find that

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station-based carsharing is only financially sustainable with diesel-powered engines and would result in a net loss when utilizing electric vehicles. Yet, only the electric vehicle variant would yield a positive environmental result, but only without relocation which would heavily decrease potential environmental benefits. Namazu and Dowlatabadi (2015) find in a case study approach for Vancouver, Canada, that carsharing fleets can reduce GHGs emissions by more than 30% (Namazu & Dowlatabadi, 2015). Sustainability in mobility is also a matter of focused policy. Akyelken et al. (2018) highlight in a case study on carsharing in London that sustainability is often hampered by ineffective governance as too many actors with different agendas are involved in regulation and supervision. Hartl et al. (2018) demonstrate that the sustainability of carsharing is perceived as a positive side effect for carsharing and that convenience or financial considerations play a much bigger role. Jenn (2020) analyzes the effects of electric vehicles utilized for ride-hailing in California and states that electric vehicles perform just as well as combustion engine vehicles in ride-hailing scenarios, while emission reduction benefits are approximately three times higher. McQueen et al. (2020) demonstrate that micromobility vehicles replace car trips in several cities. Yet, a high number of micromobility trips are also replacing plain walking, questioning the overall positive net impact of micromobility. Tirachini, (2019) study in Mexico City how a digital mobility platform, here an app, affects the traveled distance and they show that sharing rides in middle-size vehicles can lead to a decrease in traffic and congestion, while sharing cars have opposite effects. Apart from scenarios with a singular focus, some studies pursue a holistic perspective. These studies mostly consider transportation by a private combustion engine-powered car as the most polluting option, somewhere emitting between 200 and 350 CO2 grams per person per kilometer (EY, 2020; Siemens Mobility, 2020). The International Transport Forum (2020) measured and compared the environmental outcomes of new mobility services and traditional modes of transportation on aggregated levels by a life-cycle analysis. They conclude that private bikes and e-bikes exhibit the lowest life-cycle energy requirements and GHG emissions per km if used regularly. In contrast, traditional taxis and ride-hailing exhibit the highest energy consumption and GHG emissions, even higher than private cars as empty vehicle travel and additional operational service worsen the environmental balance sheet. In the same way, shared e-scooters and e-bikes share similar energy requirements and GHG emissions like private mopeds using combustion engines. The sustainability equation of any mobility service is heavily obstructed by the so-called deadheading, which refers to the travel of an empty vehicle. It can take various forms, depending on the mode. In public transport, deadheading encompasses the level of utilization and the need to move the vehicles, e.g., buses or trains, from the depot to the area of operation. For example, the deadhead travel for urban bus services varies widely, from very few empty kilometers in India, at 1–3%, up to 28% in Australia (International Transport Forum, 2020). In carsharing,

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Private Residence • Commute to business area

Business Area • Log-in to platforms • Cruising or waiting

107

Ride request • Drive to customer location

Pick-up • Drive to target

Drop-off • Drive back to high demand area

End of Shift • Commute to private residence • Log-out

Fig. 5.6 Deadheading in ride-hailing (Source: own illustration, 2020)

deadheading consists mostly of staff driving vehicles for maintenance, service, and relocation reasons. Micromobility services only necessitate a very limited amount of empty trips, but for operational services often buses or vans are required, which is an indirect form of deadheading. It seems to be most problematic for ride-hailing if several empty rides are a prerequisite for providing such service (Shaheen et al., 2020). Figure 5.6 illustrates a potential ride-hailing journey. Most notably passengers are only being served during the steps “passenger pick-up” and “passenger drop-off”. All other steps are deadheaded rides, the driver is moving the vehicle without a passenger. These steps add up to a significant portion of the overall vehicle journey. Henao and Marshall calculate that for 100 miles with a passenger, ride-hailing vehicles are deadheading an additional 69 miles (Henao & Marshall, 2019). Most studies estimate that deadheading in ride-hailing ranges between 42% and 81% of the traveled distance. These absurdly high numbers are a major issue from a sustainability point of view. The Union of Concerned Scientists states that a non-pooled ride-hailing trip is 47% more polluting than a private car ride due to extensive deadheading (Union of Concerned Scientists, 2020). The growing societal pressure for greener transportation further accentuates the question mark for ridehailing as a cornerstone of future multi-modal transportation. In other words, ridehailing seems simply not sustainable, from an environmental but also from a financial perspective. The rationale of deadheading applies also to privately owned vehicles as personal trips are not exempt from extracurricular activities, such as browsing for parking or running the engine while waiting for friends. Mobility is also about how services and offerings complement and substitute each other, the so-called modal shift, for example, if a newly established bus route will get people to abandon their private car. Several surveys, most took place in the United States, show that around 20% of e-hailing trips replace personal auto trips and another 20% replace traditional taxi-services (Arthur D. Little, 2020). The bulk of trips, approximately 60%, takes place instead of public transportation, walking, biking, or simply would not have been made without the availability of somewhat inexpensive ride-haling options. Ride-hailing both substitutes and complements public transit, walking, and biking. Micromobility providers pitch to the public the idea that their offerings are perfect for bridging the last mile gap and that they will increase usage of public transportation. To this end, there is mixed evidence

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signaling that micromobility drives up linkages with public transport and encourages multimodal behavior. In San Francisco, 34% of micromobility trips were made multimodal, either to get to or from public transportation (San Francisco Municipal Transportation Agency, 2019). In contrast, the city council of Minneapolis found that the majority of survey respondents used less than 10% of their e-scooter trips to combine with public transport in transit (McQueen et al., 2020). More so, Suatmadi et al. (2019) study the introduction of on-demand motorcycle taxis, called “online ojek”, in the Jakarta Metropolitan Area and find that modal shift is not mitigating negative externalities, meaning the availability of new services increases mobility options but also lets emissions surge. Mobility services are poised to thrive if their modal shift has a positive impact on the sustainability equation. This is where micromobility might truly shine. Given that, 48% of automobile trips in the twenty-five most congested US metro areas are less than 5 km, the lightweight and minimal energy requirements of micromobility truly have the potential to replace a considerable amount of private car or ride-hailing trips (McQueen et al., 2020). Measuring adjunct environmental aspects such as air quality, congestion, noise, and the utilization of space should be part of any truly comprehensive sustainability assessment, yet the evaluation is often confined to regional particularities and thus challenges any assessment on aggregated levels. The studies presented vary heavily in methods, perceptions, findings, and outcomes, underscoring the complexity in quantifying sustainability. A full-disclosure policy needs to be mandatory for any mobility provider, as sustainability requires different actors to jointly set common goals. Regulators in the form of cities and municipalities will certainly tighten the screws in the years to come and firms need to be prepared, which will be discussed in the next chapter. Table 5.1 summarizes the previous findings and evaluates the profitability, scalability, complexity, and sustainability of the respective MaaS models. The assessment is rather indicative and qualitative in nature, as fundamental changes in technology, consumer behavior, and business models are still underway and concluding evaluations do seem farfetched. High scores on profitability, scalability, and complexity denote great profit potential, a highly scalable but also an immersive and complex business model. High marks in sustainability are associated with a low environmental impact (Fig. 5.7).

Micromobility P2P

Be-Driven

Ecosystem

Long Tickets Integ. RideShuttle Char- Journey dist. & Pay- Parking Ecoging Planning Pro. hailing Bus Pooling ment system Drivers

Ridesharing

Very Low

○ Low

◔ Moderate

◑

High

◕

Very High

●

○ ◔ ○ ○ ○ ◑ ◕ ◕ ● ◑ ● ◑ ◑ ○ ◑ ◕ ● ● ● ● ●

○ ○ ○ ◔ ◑ ◔ ◑ ● ● ● ◕ ◕ ◕ ◕ ○ ◔ ◕ ◕ ◑ ● ●

● ● ● ● ◑ ○ ◔ ○ ◔ ◔ ◔ ◔ ◑ ◑ ◔ ◔ ◕ ◑ ◔ ◕ ●

● ◕ ● ◑ ◕ ◔ ○ ○ ◑ ◔ ◑ ○ ◔ ○ ◔ ○ ○ ◔ ◔ ○ ●

Finance SubBEV Leasing scrip. StationFreeEERental P2P Moped FECV based Floating Scooter Bike

Car Sharing

Drive

Fig. 5.7 Assessment of mobility-as-a-service models

Sustainability

Complexity

Scalability

Profitability

ICE

Purchase

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References Airbnb Newsroom. (2020, December 19). About us–airbnb newsroom. https://news.airbnb.com/ about-us/ Akyelken, N., Banister, D., & Givoni, M. (2018). The sustainability of shared mobility in London: The dilemma for governance. Sustainability, 10(2), 420. https://doi.org/10.3390/su10020420. Arthur D. Little. (2020). Rethinking on-demand mobility. Audouin, M., & Finger, M. (2018). The development of mobility-as-a-service in the Helsinki metropolitan area: A multi-level governance analysis. Research in Transportation Business & Management, 27, 24–35. https://doi.org/10.1016/j.rtbm.2018.09.001. Bain & Company. (2020). Peak profits. BCG. (2019). The promise and pitfalls of E-scooter sharing. BCG. (2020). Can delivery companies keep up with the post COVID-19 E-commerce boom. Burrows, A., Bradburn, J., & Cohen, T. (2015). Journeys of the future-introducing mobility as a service. Atkins Mobility. Calligaris, S., Crisculo, C., & Marcolin, L.. (2018). Mark-ups in the digital era. OECDpublishing, 2018/10. https://doi.org/10.1787/4efe2d25-en. Campbell, P. (2020, May 11). Running out of road: Rental car groups fight for survival. Financial times. https://www.ft.com/content/cb632d0b-6910-40d9-9596-ca97c479ba06 Canales, D., Bouton, S., Trimble, E., Thayne, J., Da Silva, L., Shastry, S., Knupfer, S., & Powell, M. (2017). Connected urban growth: public-private collaborations for transforming urban mobility. Coalition for urban transitions. London and Washington, DC. Available at: http:// newclimateeconomy.Net/content/cities-Working-Papers. Coalition for Urban Transitions C/o World Resources Institute, 10, 39–45. Crane, D. A. (2015). Tesla, dealer franchise laws, and the politics of crony capitalism. Iowa L. Rev., 101, 573. Deloitte. (2016). The rise of the sharing economy: Impact on the transportation space. Deloitte. (2019). 2019 US travel and hospitality outlook. Deloitte Insights (2020). Transportation trends 2020: What are the most transformational trends in mobility today? Derwin, J. (2020, January 10). Uber competitor DiDi's new reward system has slashed pay rates for Australian drivers, as they work punishing hours and battle it out for limited jobs. Business Insider Australia. https://www.businessinsider.com.au/didi-advance-australia-review-driverspay-rates-commission-vs-uber-rideshare-2020-1 Ellison, R. B., Ellison, A. B., Greaves, S. P., & Sampaio, B. (2017). Electronic ticketing systems as a mechanism for travel behaviour change? Evidence from Sydney’s Opal card. Transportation Research Part A: Policy and Practice, 99, 80–93. https://doi.org/10.1016/j.tra.2017.03.004. European Commission. (2016). Comprehensive study on passenger transport by coach in Europe. EY. (2020). Micromobility: Moving cities into a sustainable future. Ghanbari, A., Álvarez San-Jaime, O., Casey, T., & Markendahl, J. (2015). Repositioning in value chain for smart city ecosystems: A viable strategy for historical telecom actors. Hartl, B., Sabitzer, T., Hofmann, E., & Penz, E. (2018). Sustainability is a nice bonus the role of sustainability in carsharing from a consumer perspective. Journal of Cleaner Production, 202, 88–100. https://doi.org/10.1016/j.jclepro.2018.08.138. Henao, A., & Marshall, W. E. (2019). The impact of ride-hailing on vehicle miles traveled. Transportation, 46(6), 2173–2194. https://doi.org/10.1007/s11116-018-9923-2. Hensher, D. A. (2020). Understanding Mobility as a Service (MaaS): Past, present and future (First edition). Elsevier. International Transport Forum (2020). Good to go? Assessing the environmental performance of new mobility. Jenn, A. (2020). Emissions benefits of electric vehicles in Uber and Lyft ride-hailing services. Nature Energy, 5(7), 520–525.

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6

The Way Forward

6.1

Strategic Imperatives

The convergence of automotive and mobility is upheaving the industry in various ways. The ACES megatrends that shape the next decade require very large investments, impossible to be tackled by a single entity. Mobility services are constantly changing and evolving, they are characterized by an endless stream of simultaneous market entries and exits. As the outcome of the developments depicted in Chaps. 2 and 3 is inconclusive and convoluted, firms need to diversify their strategic commitments. No single firm can win it all. The future of mobility might resemble the big tech of today, where a few incredibly large companies dominate entire segments. To this end, automotive and mobility businesses are still very much isolated and run by heterogeneous organizations. Micromobility providers are fundamentally different from OEMs. As the industry is moving closer together, this creates challenges from an organizational perspective. In order to prepare and win in such a complex and low-margin environment, the following five strategic themes emerge: I. Digital Ambidexterity. Ambidexterity refers to the ability to use the left and right hand equally well. In the case of organizations, it denotes the ability of firms to engage in both exploitation and exploration (O'Reilly III & Tushman, 2013). Tusham and O’Reilly (1996, p.24) initiated this train of thought and stated that ambidexterity is the “the ability to simultaneously pursue both incremental and discontinuous innovation . . . from hosting multiple contradictory structures, processes, and cultures within the same firm” (Tushman & O’Reilly III, 1996). Exploitation is best described with terms such as refinement, profit, production, improvement, execution, cost control, and implementation. Exploration includes things such as ideation, knowledge creation, entrepreneurship, speed, and risktaking. It is fundamentally about simultaneously pursuing contrasting strategies within different environments. The problem is to find the right balance between short-term oriented exploitation and long-term exploration. Too much focus on exploitation leads to great and quick results, but once an industry progresses and # The Author(s), under exclusive license to Springer Nature Switzerland AG 2021 M. Ackermann, Mobility-as-a-Service, Management for Professionals, https://doi.org/10.1007/978-3-030-75590-4_6

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the initial value proposition is undermined, firms will find themselves within very dire circumstances. Similarly, too much focus on exploration diminishes the business and values of today. In terms of strategic management, it is about combining adaptability—the capacity to quickly act on opportunities, avoiding complacency, and adjusting to a VUCA environment—with alignment—the ability to create value within streamlined activities in the short term (Gibson & Birkinshaw, 2004). With regard to innovation management, it is about radical versus incremental innovation. For firms to succeed in changing environments, they need to master both exploitation and exploration. In the context of the automotive and mobility industry, what I call digital ambidexterity boils down to two main strategic dimensions. First, reconciling asset-heavy and asset-light business models. Second, running an organization that allows for heterogeneous structures and speeds. An asset-light business model is a company, which owns comparably few capital assets compared to the overall size of its operation (e.g., revenue), think companies like Netflix or Airbnb (Libert et al., 2016). Through digital tools and channels, these companies are able to reach a global scale without accumulating many physical assets. In recent years, investors have increasingly been concentrating on this type of business model, while downgrading traditional businesses that accumulated assets like manufacturing plants, properties, and equipment. This is an almost seismic shift, because since the industrial revolution, firms have tried to outcompete each other with the accumulation of physical assets. Firms like Airbus, Aldi, or Exxon Mobile are able to flourish and dominate competition through their sheer size. Ideologically, it meant the more assets the better. In the automotive industry, OEMs were racing to get bigger through internationalization, forces of consolidation, and economies of scale. This is why OEMs are the asset prone, multinational, and labor-intensive companies of today. The digital era has put this rationale under pressure, to say the least. Asset-light up-comers in many corners of the industry, like Grab, Turo, Wind, or Bolt are changing the competitive narrative. Figure 6.1 illustrates the underlining and conflicting characteristics of asset-light versus asset-heavy models. Asset-heavy companies base their value on hardware. Firms like General Motors own large parts of the value chain and their major strategic control point is the product they deliver. Additional revenue is generated around the car, like leasing or after-sales services. Asset-light approaches are business models that are based on brokering via marketplaces. Their strategic control points are the direct customer access and decentralized value creation. A company like Flixbus acts as an intermediary for independent bus providers. Asset-heavy companies often do not possess direct customer access or customer data, in the automotive context the final sales take place at mostly independent dealerships. As asset-light models can leverage on data, incumbents from the automotive sphere possess often very limited insights into their customers. In general, new mobility services like ride-hailing, routing, or ticketing are asset-light approaches. For incumbents to be successful, it is indispensable to adapt and integrate asset-light approaches in their core business proposition. However, it is not just that traditional businesses need to combine both strategic directives. New mobility providers chronically suffer from low profitability and a way to overcome this barrier might be to venture out into traditional asset-heavy

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ASSET LIGHT: Examples: Fair, Turo, Flixbus Business Model: brokering, marketplace Main Competitive Advantage: Network synergies based on software Customer Interaction: Direct & realtime

ASSET HEAVY: Examples: BMW, Ford Business Model: Sale of products and add. services Main Competitive Advantage: Market entry barriers based on hardware Customer Interaction: Indirect, monthly to yearly, limited feedback

Fig. 6.1 Asset-light vs. asset-heavy (Source: own illustration, 2020)

businesses. For example, some ride-hailing companies might be better off if they started to centralize value creation or engage in vertical integration. As companies need to master the balance between asset-light and heavy, they will be confronted with different organizational speeds. Asset-light models are based on software, enable direct customer access, frequent interaction, and necessitate direct feedback. Products and services undergo significant changes in a timely fashion, whereas in asset-heavy industries a portfolio change might take years. Think of an app with monthly updates from Voi Technology (e-scooter sharing) versus BMW’s 7series with a life cycle of a few years. Put simply, asset-light models exhibit shorter product-life cycles and that is why mobility start-ups resemble software firms, rather than automotive companies. Their structural processes and management routines are in alignment with their industry characteristics. In order to remain competitive, OEMs and suppliers in the automotive industry need to adapt various structures and speeds within a single company. Traditional car development will still need to follow the V-cycle process but mobility solutions have to be structured in a much more agile way, employing methods like Scrum or Kanban. They allow for greater flexibility and adaptability, perfectly suited for an ever-evolving mobility landscape, whereas the V-cycle is a more rigid structure suited for development in safety critical environments, like aircrafts or cars. Assetheavy business models do not seem to be favored by investors, yet research shows that these models are sometimes even more profitable than their asset-light counterparts are (BCG, 2014). Some services, like Share Now (formerly car2go), combine elements of both. Yet, these new services are mostly set-up in independent ventures, outside of the OEM mothership. This enables speed, agility, and a fundamentally different mind-set. The true challenge of these endeavors is the reconciliation between the traditional business and new mobility services. In the eyes of the customer, the automotive and mobility industry, despite all their differences, are just

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Fig. 6.2 Value vs. commodity (Source: own illustration, 2020)

one. Customers care about convenience and integrated solutions, that is why it seems reasonable to assume that in order to succeed with any mobility offering, successful firms need to conquer and master digital ambidexterity. II: Creating value in a world of commodities: New mobility services are often very much alike and operators have a tough time to articulate value. In the eyes of the consumer, e-scooter, e-bikes, carsharing, and ride-hailing providers are at times seemingly indistinguishable. An e-scooter from Lime functions just a well as one from Wind or Tier. Consequently, mobility providers need to answer the following question: how to create unique selling propositions? In the automotive world that question answers itself: driving performance, horsepower, brand, design, premium materials, and haptics are key differentiators. Ownership is key; consumers value their personally owned car and are willing to pay a premium. As the advent of the sharing economy did not stop in the realm of mobility, it has become principally more difficult to demonstrate value for customers in utilitarian offerings. Usage is the new ownership. Consumers do associate higher values with physically owned products in contrast to digitally shared products. Think of a vinyl record versus a song streamed via Spotify. There are several ways to articulate unique selling propositions in mobility services but also several trends that potentially undermine these. Figure 6.2 illustrates the opposing forces, value vs. commodity. In general, egalitarian offerings lead to low profitability. Physical products are subject to the individual assessment of their owners, best exemplified by expensive watches or pen collections. Automotive companies love to associate their products and experience with words like luxurious or premium, since operating within highend segments results in higher profits. A study by McKinsey states that the premium segment globally makes up 13% of vehicle sales but amounts to 40% of profits (McKinsey&Co, 2017). Porsche is known for publicly setting a minimum strategic target of 15% on the return on sales (Porsche Newsroom, 2020). This modus operandi does not transpose easily into mobility services. Growing awareness for sustainability only increases this very notion. A selling argument for premium cars is often their power output (horsepower), driving performance, and exterior design.

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Even though most countries in the world have speed limits and the actual speeds are even much lower, SUVs and sedans often sell with well above 200 horsepower, which is objectively unjustifiable. From an environmental point of view, this business proposition might not be sustainable. In all major markets (USA, EU, and China), regulators are actively pushing OEMs to reduce emissions. The diesel ban in selected European cities foreshadows tighter regulation in other areas like material components, sourcing, recycling, and ultimately even restricting horsepower. Mobility services are already forced to disclose and adhere to much higher environmental standards. Several cities around the globe have imposed various measures such as swappable batteries, local repair and employment, and strict recycling in their tenders. The electrification of the drive train levels the differentiation factor of acceleration, as electric motors provide full torque—the measure of rotational force—right from the start. EV customers are more concerned with charging and range issues, as they take great acceleration already for granted. Digitalization has brought consumers unprecedented levels of transparency in any category of business. They can compare minute pricings, operating areas, service hours, and reviews on mobility services anywhere from their smartphone. This is a stark contrast to the traditional automotive business where car sales processes are purposely nontransparent. In all forms of be-driven, as long as factors like reliability, safety and cleanliness are taken care of, consumers are mainly concerned about the costs and often do not care about premium offerings. This development is exemplified by Uber, which officially launched in San Francisco in 2011. Initially, it was a luxury-oriented service with premium cars in all-black and a price of 1.5 times that of a traditional taxi (Uber, 2018). Quickly realizing that customers are mainly concerned about the price, Uber introduced UberX in 2012. It introduced cheaper alternatives for consumers, as drivers were bringing their private cars into the fleet. This business model pivot was a big hit for Uber and they consequently scaled and expanded internationally with this approach. Yet, there are still several ways to create unique values for customers in mobility services. First and foremost lies the creation and integration of ecosystems. Multimodal offerings and additional services like ticketing, routing, payment can function as a potential lock-in for consumers. Once an ecosystem is established, it opens the door to cross-sell unrelated services or products like insurance or entertainment. An exceptional customer experience creates value for firms. This applies to the offlineexperience, e.g., renting out an e-bike with a designated smartphone-holder but also for the online-experience where a frictionless and straightforward customer journey can make all the difference. Even within shared environments, a great and consistent corporate look-and-feel from the app to the vehicles can be a differentiator. Especially in the ever-evolving mobility landscape, a noticeable brand recognition is important. It might even alleviate trust and safety concerns, in areas such as data privacy. Secure and trustworthy data ownership, especially in an ecosystem, can also be a key differentiator. Consumers are increasingly concerned about how big tech companies are handling their private data, especially in Europe and Asia it seems likely that many consumers and regulators do not want to continue and endure the carte blanche that FAAMG firms enjoyed over the last two decades. With

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Fig. 6.3 Sources of complexities (Source: own illustration, 2020)

operational excellence, firms can outwork their competition and thus create value. Charging, maintenance, and relocation are one of the biggest cost blocks in mobility services. Especially with free-floating models, consumers care mostly about accessibility and comfort; the proximity of a vehicle is the determining factor. III—Navigate Complexity: As the industry is evolving and transforming at a rapid speed, incumbents, mobility providers as well as start-ups are confronted with a plethora of interwoven and at times even conflicting challenges. The industry is in the midst of a fundamental disruption. Myriads of trends, developments, and challenges will frame the upcoming years. Consequently, all industry players need to learn to navigate complexity. From an economic point of view, the disruption of industries is characterized by rapid growth, market entries, and the attraction of enormous amounts of investments. The period of massive expansion is followed by consolidation, market exits, and investments do move on. In 1911, Joseph Schumpeter was the first to recognize that economies do not evolve smoothly, rather discontinuously (Schumpeter, 2003). Innovation evokes imitators, leading to a boom until the original innovator’s advantage is eliminated. This so-called creative destruction of industries takes place elsewhere and the next innovation cycle begins anew. The automotive industry in the United States consisted of around 100 independent car manufacturers in the 1900s, of 11 in the 1950s and today only two and half independent manufacturers are left. The phenomenon of industry shakeouts is not new, it just seems to happen a lot faster in the digital era. With mobility services, we are as of 2021 in the midst of a Schumpeterian cycle with rapidly expanding markets and massive rounds of investments. As with all industry disruptions, the future is unpredictable. Figure 6.3 exemplifies some of the disruptive themes, orbiting the major forces of new mobility, internet of things, and sustainability. To tap into or even dominate technologies in all major themes, firms need to carefully assess the direction and the amounts of investments. Two different types of technologies need to be mastered. First, investment in general applications technology and second in dedicated industry technology. General technology includes things such as deep learning, data processing, embedded software, 3D printing, and human-machine interface (HMI). Technology that is dedicated to the automotive and mobility industry embraces automated parking, battery development and recycling, fleet optimization and relocation, V2X connectivity, cybersecurity, automation in production, sustainability along the supply chain, and of course

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autonomous driving. Technologies for autonomous driving alone encompass various areas such as sensor data management, LIDAR, sign-recognition, routing and localization, traffic data, ultrasound, warning systems, processors as well as microcontrollers. Each technology segment encompasses enormous individual challenges. Take high-definition maps for example. An HD map is precisely down to a minimum of a 20 cm representation in three-dimensional space (Seif & Hu, 2016). It includes the lane sloping curvature, road edges, traffic signs, and lights. The challenges in HD mapping include the collection of and the ability to process incredible amounts of data and maintain attributes like traffic signs. The electrification of the drive train is another example of the many complexities that lure in the industry. It opens up an entirely new playing field as it necessitates investments in battery development, charging infrastructure, and plug-in technology. As of 2021, lithium-ion batteries are the industry standard and present an outstanding advancement over previous EV battery technologies such as nickel-metal hydride and lead-acid, yet the industry continues to advance existing technologies, while also focusing on different technologies such as lithium-air and solid-state batteries. Regulators are further complicating any sound strategy through a policy of carrots and sticks. Countless incentives, financial subsidies, tax breaks, and emission limits, sometimes even city based, are leading to a blurred picture. Beyond the orbits of new mobility, IoT, and sustainability, as outlined earlier within this book, several other forces such as aging populations, shrinking middle classes in western societies, public trade wars, and a bloated financial system are further complicating any extrapolation into the future. The past does not predict the future within the industry any more, linear thinking becomes obsolete. In order to ride with the wave of disruption and not being sucked under, firms must learn to navigate complexity. Agility, flexibility, and adaptability are key values in this endeavor. Embracing open innovation principles, firms need to commercialize both ideas from outside as well as seeking possibilities to externalize in-house innovations (Chesbrough, 2003). Traditional automotive development looks like a closed innovation poster child. It resembles a big-bang approach, cars are locked in the tightly shielded R&D funnel for years before being graced with customer feedback. In an era with unknown futures and outcomes, it is about replacing predefined targets with a compelling vision. It is about starting in the right direction and quickly establishing baselines based on customer feedback. Moving past the meticulous and extensive analytical approach with customer surveys on theoretical settings is of upmost importance. Traditional players love to survey their potential customer base on all kinds of things; like how important they believe that digital sales will be in the future or how likely they are to change brands. Today firms need to gain direct customer insights and consistently evolve their value proposition via A/B scenario testing. Data-based hypotheses should drive and shape the business model. Agility and adaptability need to be the overarching guidelines for all engineering and business processes. Reducing time to market is critical in the ever-changing mobility universe. To transcend the complexity, OEMs need to define, what an OEM of the future is supposed to look like. A software firm, a manufacturing company, a hardware supplier, a sales channel, a brand, or all of the

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above at the same time? Firms need to set strong visions that rise above singular trends and current business trajectories. Moreover, the ocean of complexity needs to be navigated by partnerships. IV Master Partnerships: As the industry looks increasingly like a colorful and hazy mosaic, firms need to engage in partnerships in order to grasp the bigger picture. Going forward is no longer a simple make-or-buy decision. Too many forces, emerging technologies, and new business models are simply too contrarious and too costly to master them all. Partnerships are a way of hedging, as markets evolve in so many different directions. In other words, firms cannot simply put all their eggs in one basket, they need to diversify by putting eggs in many baskets. The executive dimension of this diversification strategy is partnership engagement. It can come in many forms, ranging from short-term contracts, strategic alliances, licensing, franchising, equity alliances, joint ventures, and parent-subsidiary relationships. All aforementioned forms within this make-or-buy continuum need to be in a company’s playbook and employed on three distinct types of partnerships: (1) Partnerships within the industry. (2) Partnerships outside the industry. (3) Partnerships with the public. Over the last years, we have been able to witness an astonishing increase in all types of partnerships. Partnerships within the industry denote engagements between organizations with an automotive and mobility focus. Several cases should exemplify the uptick in engagements. Ford and Volkswagen invested jointly in Argo AI, a start-up for autonomous driving (Ford Motor Company, 2021). Ford also formed a joint-venture with Mahindra & Mahindra of India to get a stronger foothold in the Indian market and with Changan Automobile of China to specially address the demands of the Chinese market. General Motors, for instance, partnered up with Honda in 2020 to jointly develop combustion engine technology, seeking to cut cost (Bloomberg, 2020). Toyota has deepened their relationship with Subaru Corporation by commonly increasing each other’s shares in 2019. Being surprisingly open about the ongoing sincere disruption, the partnership was headlined with: “Expansion of the scope of partnership to survive this once-in-a-century period of profound transformation” (Toyota, 2021). The case of Ionity, a charging network for electric vehicles across Europe, is also uncommon practice for the industry. It is a joint venture of BMW Group, Hyundai Motor Group, Ford Motor Company, Mercedes Benz AG, and Volkswagen Group with Audi and Porsche (IONITY, 2021). Several retail partners are also part of this very project, like Shell, Eni, Avia, Apios, and Cepsa. Partnerships outside the industry are even more common, as depicted by the following examples. Volkswagen collaborated with several companies from various backgrounds. They teamed up with Microsoft to accelerate the digital transformation of the company, with Amazon to utilize web services and with Siemens to develop an industrial cloud. BMW engaged with SAP to build a data alliance, with Tencent, the Chinese tech giant, to develop automated driving technology, with Intel and Mobileye to do the same. Via their BMWi venture arm, they have invested in Lime, a micromobility sharing company, in Fair, a digital leasing company, in JustPark, a parking application and in Xometry, an on-demand manufacturing platform, underscoring how diverse arrangements today are (BMW i Ventures, 2020).

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As cities and metropolitan areas are the breeding ground for the next generation of MaaS concepts, partnerships with the public play an even more important role. Truly sustainable mobility systems need to be integrative in regard to public transportation, automotive companies, and mobility providers. Regulators are actively shaping mobility services by various measures such as full data disclosures, strict environmental requirements, or even commitments to local employment. A great example for this development was the e-scooter tender in Paris, which was hailed as the biggest in the world. The city of Paris demanded multiple commitments in all matters of economic, social, and environmental dimensions. It seems reasonable to assume that other cities and regions will take up the example of Paris and exercise their newly found power in negotiating new mobility services. Partnerships with the public do not stop there. For instance, the MaaS-Alliance is a public-private partnership with around 100 partners. The alliance promotes a common approach to MaaS to facilitate a single and open market for MaaS services with a primary focus on Europe (MAAS-Alliance, 2021). Many more examples could be added to this list, underscoring the far-reaching implications and significance of partnerships. Mastering partnerships involves decisions in three pivotal dimensions: (1) the degree of investment, (2) the extent of vertical integration, and (3) the scope of strategic control. All these decisions need to be made within the continuum of makeor-buy. In the last decade the number of partnerships in the areas of autonomous technologies, connectivity, electrification, and shared mobility has seen a 40-fold increase, see Fig. 6.4; data for 2020 is estimated (McKinsey&Co, 2020b). Partnerships represent a wide array of organizational settings and conclusive evidence on new forms like coopetition (when firms engage simultaneously in competition and cooperation) is ambiguous. A more narrow form of partnerships are M&As, which have been around longer and thus more extensively analyzed. Firms are wise to study and avoid the pitfalls of M&A activity. Concluding evaluations are mixed at best, a KPMG study from 2015 found that 83% of M&As activity has not translated into increased shareholder returns, another A. T. Kearney study from 2014 determined that overall returns of M&As were even negative (Rothaermel, 2018). In general, M&A activity suffers from two fundamental flaws. First, the managerial hubris, a form of self-delusion in which managers convince themselves of their superior skills and the belief to manage the assets of a

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target company, the one to acquire, more efficiently than the current management (Gabriel, 1998). Executives consistently exaggerate their own judgment and ability. Edzard Reuter, former CEO of Daimler, famously pursued the vision of transforming the company into a car-independent technological powerhouse in the 1980s. Investments in areas such as electronics, aerospace, aircraft manufacturing, household goods, and services followed suit. In the mid-1990s, after burning through billions, most of the acquisitions and investments were dissolved and Daimler focused again on manufacturing cars, effectively reversing all strategic decisions by Mr. Reuter. The second fundamental flaw is the desire to overcome competitive disadvantage. When industry changes unfold, struggling firms often join forces with competitors who are equally struggling. Joining forces might seem theoretically like a good idea, but several examples highlight that this train of thought does not transpire easily into business practice. Microsoft acquired Nokia in order to get a stronger foothold in the then emerging smartphone business. Microsoft spent billions without gaining any traction, ultimately withdrawing from the entire market. The most recent mega merger in the industry, Stellantis, is an automotive manufacturing corporation by the French Groupe PSA and ItalianAmerican Fiat Chrysler, officially formed in the beginning of 2021. Both PSA and Fiat Chrysler have been losing ground on its competitors in the last years and it remains to be seen if this newly formed merger can beat the odds. Many trends within the industry, depicted in Fig. 6.3, might even lead to contradicting outcomes. Firms cannot buy into all emerging trends and technologies, they have to be careful to avoid the trap of unrelated diversification. A lack of focus might lead to a conglomerate type of business. Several empirical studies have shown the negative effects of unrelated diversification on firm performance (Aggarwal & Samwick, 2003; Berger & Ofek, 1995; Denis et al., 1997). Firms need to master and proactively identify stakeholder management within partnerships as a key intangible resource. To navigate the various pitfalls of partnerships, a clear strategic focus that compromises all forms of engagement and navigates the myriads of complexities needs to be articulated. V Prepare for disruption: Various potentially disruptive forces in mobility have already been elaborated on in this very book. Some mobility solutions, which are currently rather theoretical in nature or in embryonic phases, might even lead to a mobility regime that is entirely different than discussed hitherto. For instance, the Hyperloop concept is a ground-based transportation system that could dramatically reduce travel time over medium range distances, for example, between San Diego and Las Vegas (Opgenoord & Caplan, 2018). The concept requires very little energy as it creates a vacuum environment that propels pods through its tubes with limited resistance. With top speeds potentially up to 500 km/h, it could easily match highspeed trains and aircrafts while being locally emission-free. The idea of urban-air mobility (UAM), the futuristic versions of flying taxis, has inspired numerous startups around the world. Their vision of developing small, electric-powered vertical takeoff and landing (eVTOL) aircrafts offers several advantages over traditional helicopter services, which are powered by singly large rotor motors (KPMG, 2019). The market is not bound by a single technology, rivaling concepts will have different

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use cases. Potential concepts might be inner city flight services between landing stations covering distances between 10 and 50 km, in principal rivaling taxi or ridehailing services in highly populated areas. Another potential use case are shuttle services on predefined routes covering distances between 50 and 200 km. A start-up called Joby Aviation out of Santa Cruz, California, works a futuristic helicopter that features six rotating propellers and offers a capacity of four riders (Joby Aviation, 2021). By the end of 2020, the firm has raised over $800 million in venture capital. Another start-up called Lilium, based in Munich, is developing a Jet-like aircraft with up to 300 km/h top speeds and a capacity of four passengers. Despite the lack of a proven business model, investments are surging. In the first half of 2020, $907 million venture capital was raised, approximately 20 times the amount of the whole of 2016 (Roland Berger, 2018). The main kicker for urban air mobility is that it does not require the cost-intensive construction and maintenance of infrastructure. As for comparison, high-speed trains seem like a potentially great alternative but several high-profile cases have proven that building designated infrastructure is a complex and time-consuming endeavor. The planned California High-Speed Rail system that connects the Los Angeles and San Francisco area has been under way since the mid-2000s. The project is notorious for creating political turmoil, burning through billions, and potentially only be completed by the early 2030s, if ever. Several other technologies could disrupt transportation as we know it, but probably only one has the greatest disruptive potential: autonomous vehicles. Once level 5 technology and a regulative framework are available, it stands to reason that shared autonomous transportation will be the most economically and environmentally feasible model. An even greater convergence will take place, for example, private car usage, ride-hailing, and public bus services can be replaced by a standardized autonomous vehicle for hire service. In the end, all modes subsumed under drive and be-driven in Fig. 4.1 might be replaced by modes with autonomous functionality. The mobility-as-a-service market as well its adjunct markets (travel, delivery, logistics, and public transportation) will merge into a single and interconnected market. In the face of disruption, the automotive industry has to be careful not to fall victim to the innovator’s dilemma. As Clayton Christensen pointed out in his seminal book: “The innovator’s dilemma: when new technologies cause great firms to fail”, great management itself is a root cause for firms to fail in a changing market environment (Christensen, 2013). Managerial routines of established companies often include tracking the competitor’s actions meticulously, consistently surveying customers, and investing in and building products with higher quality and performance. This brings out iterative innovations, something we can observe very well in the car industry, where most innovations are very cost-intensive, yet yield rather limited value to customers. Most modern cars are equipped with several connectivities and safety features and more premium materials, which lead to higher prices. Figure 6.5 displays the consumer price index of new car purchases annually in the UK from 2007 to 2019, where the year 2015 is set as the baseline, equaling 100 (Office for National Statistics [UK], 2020). As prices for new cars clearly

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increased, the perceived value of cars in the eyes of the customers did not increase to the same degree. The latest iterations of cars only produce minimal value improvements, which is a clear sign that automobiles are in the latter phase of the maturity stage in the S-curve that describes the life cycles of technologies. As disruptive technologies have fluid futures, it is impossible to predict the degree and exact timeline of disruption, it is only evident that the automotive industry is ripe for it. The case of shared e-scooters should function as a cautionary tale for the automotive industry. The adoption of e-scooters was much faster than previous mobility services, such as ride-hailing or carsharing. According to EY, it took carsharing 7 and bike sharing 6 years to reach a threshold of 10 million rides globally. Ride-hailing managed to reach this threshold in around 4 years, while e-scooters reached 10 million rides in less than 2 years (EY, 2020). This highlights that truly disruptive mobility services, for example, level 5 autonomous ride-hailing, will have an even bigger impact and mass-scale adoption could be instant. Accordingly, firms need to exercise dynamic capabilities, which are the managerial skills and organizational routines to integrate, build, and reconfigure internal competences and assets. Firms should also be able to integrate an evergrowing set of external resources, such as freelancers or crowdworkers, in order to address shifting business environments (Teece, 2018). Firms must constantly sense and seize opportunities and periodically transform facets of the organization and culture in order to address new competition, technology, and markets.

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Conclusions

The years of 2020 and 2021 will be remembered as the years that COVID-19 wreaked havoc around the world. In a matter of months, the virus has shattered families, communities, and the world economy. As this book was written in the midst of this crisis, this “black swan” event does severely influence the developments previously outlined. Digitalization went on the fast lane with virtual sales skyrocketing. Even the laggards turned to online shopping. The fourth industrial revolution, the automation, and connectivity of manufacturing and industrial practices will likely accelerate and reinforce investments in key battleground

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technologies like cloud computing or machine learning. Remote or home-based working has taken over many parts of the world. It seems likely that working from distance will remain here to some degree, especially in high-skilled jobs such as IT or finance. Urbanization came to a momentary stop; especially in western countries, real estate buyers have turned their heads away from city centers and toward suburban or even rural places. This might ease the pressure in parts of the housing market, especially in highly sought after areas like San Francisco, Stockholm, or Sydney. However, in the grand scheme of things, urbanization might just be on temporary hold, as the gravitational pull of the underlying economies that led to urbanization in the first place remain intact. Business travel and professional conferences appear to be the big losers, as firms realized that digital communication might be just as effective as personal communication, but much less costly. In the light of growing sustainability concerns, it might further usher the end of business travel as we know it. Leisure travel is destined to bounce back, driven by the very human desire to enjoy and explore. In response to the pandemic, governments around the globe have tremendously increased fiscal spendings. By the end of 2020, the G-20 economies put in place fiscal packages that in real terms are three times as large as the financial aid provided in the financial crisis of 2008 and even 30-times the size of the Marshall plan that facilitated the rebuilding in Europe after WWII (McKinsey&Co, 2020a). Low or even negative interest rates are expected to last. Financial markets were already flooded with money through the practice of quantitative easing, now the economic bazooka approach has amplified already existing developments like the rising intergenerational wealth gap. Those who owned assets before the pandemic experienced an increase in wealth, whereas the average consumer purchasing power did decrease. The COVID-19 crisis has exposed the increasing fragility of the world economy. As the world grapples with digitalization, automation, global warming, and many other forces, more interventionist governments may become the new norm. As the automotive and mobility industry is a vital part of the economy in many countries, governments will actively influence how the industry is moving forward, the days of deregulated global markets seem somewhat numbered. The doctrine of neoliberalism has sparked incredible wealth gains over the last decades in developed and developing nations, yet in the eyes of the public, its downsides have put neoliberalism increasingly under scrutiny. Even before the crisis, businesses had begun to face growing public demands to adhere to a wider view of corporate purpose beyond maximizing profitability and shareholder value. The future could see very fragmented and heterogeneous markets with a much larger role of the state in the years to come. Big tech companies benefitted from a world that went digital, whereas asset-heavy companies, such as OEMs and suppliers, were stuck with high fixed costs on manufacturing plants and infrastructure without the desired output. The long-term economic and social consequences of the pandemic are still very much uncertain, but as of 2021, it is the worst downturn since the great depression of the 1930s. COVID-19 has overlapped with, and to an extent aggravated, rising tensions between America and China. Disputes on trade and labor laws were on the political

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menu even before the pandemic hit. It might mark a turning point in economics and geopolitics, as China does appear to have handled the crisis much better than America and Europe. The automotive industry with its globally interlinked and complex supply chains may be looking to increase resilience by focusing on local suppliers, which ultimately will only lead to higher costs for society and consumers. Within the industry, some effects appear permanent in nature; others might represent only temporary dips. Billions were lost through lockdowns of retail outlets and manufacturing plants, but support was consistent by governments through various measures. As the global car sales reached a new record of almost 75 million new car units in 2019, it might take even a few years to reach pre-pandemic sales levels, if ever. In the meantime, private transportation via one’s own car became much more popular again, but potentially only temporary. The sales of electric vehicles have been remarkably robust. Governments around the world have double-downed in purchase subsidies and tax breaks. In Germany, for instance, vehicle registrations for battery-powered EVs increased by 43% in the first half of 2020 compared to 2019. Online sales of cars have skyrocketed during the epidemic. This pandemic-induced change in consumer behavior will transform the automotive industry indefinitely. Strategically, it implies moving beyond the brick-and-mortar approach in retail and move all-in towards creating digital experiences. Post-pandemic consumers will expect seamless omni-channel offerings for both car sales, loans, and subscription services. Compared to 2019, the global carbon dioxide emissions dropped by 6.4% in 2020, which is significant, but smaller than many climate experts predicted given the scale of the economic downturn. This will fuel arguments for a truly radical shift in society and certainly in the automotive industry as well. The virus delivered a huge blow to all sharing models, like ride-hailing, car sharing, and micromobility, as demand in some parts of the world evaporated overnight. The entire sector can be expected to pick up steam again and return to their pre-pandemic growth path, as the underlying economics for businesses, regulators, and consumers are simply too strong to be ignored, thus a reversal seems unlikely. The newly found admiration for hygiene and cleanliness of vehicles, is here to stay, although potentially to a lesser degree. Operationally, this does not represent much of a challenge as certain measures were already in place in a pre-pandemic world. Mobility operators had months of fine-tuning these processes and the associated costs are rather marginal. Just before the world was hit by COVID-19, the days of limitless capital for startups seemed numbered (Today online, 2019). Investors were increasingly looking for profitability, challenging the business models of several mobility companies. With the unprecedented market flooding, the profitability soul searching of mobility providers might be prolonged for a few years, as governments and investors are favoring short-term aid. The crisis has already widened the investment gap between OEMs, venture capital firms, and technology companies. Investments in ACES technologies from the top ten OEMs decreased from 47 to 16 transactions in the first half of 2020 compared to the previous year. Investments from VCs grew from 36 to 66 transactions, whereas the investments of the top ten tech players stayed on par with the previous year. The disruption of industries has always led to innovation

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and the most recent COVID-19 crisis, just like other crises before, might spark a new wave of creative destruction and innovation. The main thesis of this book is that we have finally reached peak car and that the appearance of new mobility services has sparked the convergence of the automotive and mobility industry. Personal car ownership will not disappear and still be the most relevant and reliable part of transportation in many parts of the world. The rapid expansion of the global middle class will fuel the demand for private cars for several more years, but other major developments like the rising public awareness for sustainability and the growth pains that urban agglomerations experience, do contradict this notion. Especially in Asia, car sales will continue to grow for the foreseeable future, but in other parts like Europe, the pinnacle of the car, seems to be in the past. MaaS models simply provide economically and environmentally beneficial alternatives. Younger generations are less car obsessed and value other things, but are also simply less wealthy. The rationale of smart cities leaves little room for the private car transportation as we know it. New mobility models will consistently emerge, incumbents and start-ups will continue to test and explore new mobility schemes. Shared mobility is destined to propel to unseen heights, as we experience a transition away from car ownership toward mobility access. In order to take part in this new mobility world, automotive firms must transform. From the lens of the automotive industry, all new mobility solutions are much more complex to be mastered. Simultaneously rooted in software and hardware, they require constant adaptation and progression in regard to an ever-changing mobility landscape. More so, most models require strong localization efforts, which further impedes profitability. All MaaS providers operate in areas with relatively complex business models and low profitability. Digitalization brought an unprecedented level of transparency and customer freedom, this is why the importance of establishing mobility ecosystems cannot be overstated. It will serve as an entry-point and cross-selling platform for any kind of service. Most mobility services require dense and highly populated areas, leaving rural areas car-dependent. The prerequisites for new mobility can simply not be found in rural areas. Even artificially creating conditions through, for instance, financial incentives will simply not be enough. Rural areas will continue to be car dependent and only change once a truly disruptive mobility solution, like autonomous driving, comes along. To this end, firms of the automotive and mobility industry are apparently swimming in different waters. An adequate analogy might be a cargo ship versus a speedboat. The automotive industry with its firms, many well over a century old, are stuck in their bricks-and-mortar mode. Tall hierarchies in pyramid-shaped organizations with hundreds of thousands of employees and a strong hardware legacy compared to mobility firms that are much leaner, more customer-focused, and software-minded makes competition somewhat distorted. Investors in companies like Ford, Toyota, or Daimler expect yearly profits and dividends. Upand-comers in the mobility sphere are often valued sky-high even though they have failed to demonstrate profitability over the course of a decade. Models like free-floating carsharing or ride-hailing are not a thing of start-ups anymore; they originated around 2008/2009. Certainly, investors are not valuing today’s bottom

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line, rather the potential outlook, but questions remain if some models will ever reach profitability. The convergence of these industries has just begun, a process that will unfold in phases over the next 20 years. The rise of ecosystems will ultimately catalyze the convergence of MaaS as well as its adjunct markets of public transportation, travel, logistics, and delivery. The arrival of autonomous vehicles in the massmarket will greatly exacerbate this convergence. A few business models will remain, such as driverless taxis or buses, which will serve as the main means of transportation. Autonomous technology appears to be the most environmentally feasible but also economically justifiable solution and it will be the dominant design of transportation. For companies, autonomous technologies will intensify the problematic nature of creating unique selling propositions, just like today customers do not care about flying with Boeing or Airbus. They care about price, reliability, routing, and service; the aircraft manufacturer seems irrelevant. For manufacturers that only provide the means, the car itself, will find themselves in a great margin squeeze, as the market entry barriers in car manufacturing are not as high as within aircraft manufacturing, most notoriously exemplified by Tesla. Autonomous vehicles will be the integral part of public transportation. Manufacturers might only deliver vehicles, just like bus manufactures today are delivering transportation agencies, like the San Francisco County Transportation Authority (SFCFTA), which serves the county of San Francisco. As sustainability has become a strategic priority for automotive and mobility providers, society and the consumer seem to be the big winner in the described scenario. More flexible and cheaper mobility options are now available at the consumers’ fingertips. Mobility has become transparent like never before. All the developments and trends that have been addressed within this book make up for a wicked problem from a strategic point of view. The greater the disagreement among stakeholders, the more wicked the problem. This notion could not be more true regarding the multilayered and arduous convergence of automotive and mobility industries. So how should firms navigate in such ambiguous environments? It boils down to the infamous statement of Charlies Darwin: “It is not the strongest of the species that survive, nor the most intelligent, but the one that is most responsive to change.”

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