New Business Models to Realise Benefits of the IoT Technology within the Automotive Industry

Abstract

This thesis seeks to identify new business models that pursue the exploitation of IoT benefits, with an emphasis on the automotive sector. The work is seeking realistic combinations capable of delivering the most impactful solutions. It considers the technology and business factors that are delivering the inevitable change, brought by the digitalisation. The crucial importance is given to the strategic measures that could enhance the value creation and aid realisation of anticipated benefits. A set of recommendations guides potential strategic measures towards impactful implementation of connectivity notions.

Full text

VIENNA UNIVERSITY OF ECONOMICS AND BUSINESS
MASTER THESIS
Title of the Master Thesis:
New Business Models to Realise Benefits of the IoT
Technology within the Automotive Industry
Author: Omar Veledar
Matriculation number: H11775866
Program: Professional MBA Project Management
Supervisor: Dr. Bernhard Scherzinger, MBA
I, Omar, Veledar, hereby declare:
1. that I have written this master thesis, "New Business Models to Realise Benefits of the IoT
Technology within the Automotive Industry" of 88 pages in length, independently and without
the use of any sources other than those listed,
2. that I have not made use of unauthorized assistance of any kind,
3. that I have not submitted parts of this work or the thesis as a whole, nationally or
internationally, as a graded paper, and
4. that this master thesis corresponds to the one assessed by my supervisor
.
Date
Signature

1
Professional MBA Project Management 2017-2019:
Abstract
This thesis seeks to identify new business models that pursue the
exploitation of IoT benefits, with an emphasis on the automotive sector.
The work is seeking realistic combinations capable of delivering the
most impactful solutions. It considers the technology and business
factors that are delivering the inevitable change, brought by the
digitalisation. The crucial importance is given to the strategic measures
that could enhance the value creation and aid realisation of anticipated
benefits. A set of recommendations guides potential strategic measures
towards impactful implementation of connectivity notions.
Author of Master Thesis: Omar Veledar
Title of Master Thesis: New Business Models to Realise the
Benefits of IoT Technology within the
Automotive Industry
Supervisor: Dr. Bernhard Scherzinger, MBA
Number of pages: 88
University: Vienna University of Economics and
Business – Executive Academy
Year: 2019

2
Contents
Abstract .............................................................................................................................................. 1
Table of Figures .................................................................................................................................. 6
List of Tables ....................................................................................................................................... 7
List of Abbreviations .......................................................................................................................... 8
Acknowledgements ............................................................................................................................ 9
1. Introduction ............................................................................................................................. 10
1.1. Digitalisation semantics ....................................................................................................... 11
1.2. Influences and realisation .................................................................................................... 11
1.2.1. The Drivers of Change ...................................................................................................... 12
1.1.1. The technology as an enabler of a business revolution ................................................... 13
1.1.2. The realisation - value creation ........................................................................................ 13
1.2. The objective of the thesis ................................................................................................... 14
1.3. Research question ................................................................................................................ 14
1.4. Thesis structure .................................................................................................................... 15
2. Readiness levels ....................................................................................................................... 16
2.1. Technology review ............................................................................................................... 16
2.1.1. Technology enablers ........................................................................................................ 17
2.1.2. Common application fields .............................................................................................. 19
2.1.2.1. Automotive .................................................................................................................. 19
2.1.2.2. Healthcare .................................................................................................................... 20
2.1.2.3. Smart homes ................................................................................................................ 20
2.1.2.4. Smart city ..................................................................................................................... 20
2.1.3. IoT applications in the automotive industry .................................................................... 20
2.1.3.1. Industry 4.0 .................................................................................................................. 21
2.1.3.2. Autonomous driving ..................................................................................................... 21
2.1.3.3. Predictive maintenance ............................................................................................... 22
2.1.3.4. Fleet management ....................................................................................................... 22

3
2.1.3.5. Electrification ............................................................................................................... 22
2.1.3.6. Truck platooning .......................................................................................................... 22
2.1.3.7. Mobility as a service (MaaS) ........................................................................................ 23
2.2. Market situation and future expectations ........................................................................... 23
2.3. Market drivers ...................................................................................................................... 24
2.3.1. Changing customers ......................................................................................................... 24
2.3.2. Competition ..................................................................................................................... 26
2.3.3. Changing business environments .................................................................................... 27
2.3.4. Market drivers in a nutshell ............................................................................................. 28
2.4. Barriers ................................................................................................................................. 28
2.4.1. Security Concerns ............................................................................................................. 28
2.4.2. Privacy concerns ............................................................................................................... 29
2.4.3. Implementation difficulties and lack of regulation and standardisation ......................... 29
2.4.4. Technology fragmentation ............................................................................................... 30
2.4.5. Industrialisation and market rollout ................................................................................ 30
2.4.6. Integrative service correlation ......................................................................................... 31
2.4.7. Solution financing............................................................................................................. 31
2.5. Relevant roadmaps and legislation ...................................................................................... 31
2.6. Capturing value through emerging and evolving models .................................................... 33
2.6.1. Subscription Model and/or Service Model ...................................................................... 34
2.6.2. Sharing model .................................................................................................................. 34
2.6.3. Outcome model ............................................................................................................... 35
2.6.4. The razor and blade model .............................................................................................. 35
2.6.5. Data monetisation model ................................................................................................ 36
2.6.6. Pay as you go .................................................................................................................... 37
2.7. Business models and data marketplace ............................................................................... 37
2.8. Business models in a nutshell .............................................................................................. 39
3. Analysis ..................................................................................................................................... 41
3.1. Five Basic Questions ............................................................................................................. 41

4
3.1.1. User Demands .................................................................................................................. 41
3.1.2. New solutions ................................................................................................................... 41
3.1.3. Trends ............................................................................................................................... 42
3.1.4. Improving market position ............................................................................................... 43
3.1.5. Cost position .................................................................................................................... 45
3.2. Cookbook analysis ................................................................................................................ 46
3.3. Benchmarking past promising technologies ........................................................................ 46
3.4. Porter's 5 forces ................................................................................................................... 47
3.4.1. Jockeying for the position (high) ...................................................................................... 47
3.4.2. The threat of new entrants (high) .................................................................................... 49
3.4.3. Bargaining power of buyers (moderate) .......................................................................... 51
3.4.4. Bargaining power of suppliers (moderate) ...................................................................... 51
3.4.5. The threat of substitute products/services (high) ........................................................... 52
3.4.6. Complementary providers (extremely high) .................................................................... 52
3.5. Expert Interview Summary ................................................................................................... 52
4. Results ...................................................................................................................................... 54
4.1. SWOT analysis ...................................................................................................................... 54
4.2. Evaluation of Options ........................................................................................................... 54
4.3. Specific models ..................................................................................................................... 54
4.3.1. Business model: EV charging infrastructure .................................................................... 54
4.3.2. Data based Business Model ............................................................................................. 59
4.4. Change and risk management.............................................................................................. 61
4.5. Expected Benefits ................................................................................................................. 62
5. Discussion and Recommendations .......................................................................................... 63
5.1. Summary .............................................................................................................................. 63
5.2. Challenges ............................................................................................................................ 64
5.3. Recommendations ............................................................................................................... 65
6. Conclusion ................................................................................................................................ 68
References........................................................................................................................................ 69

5
A. Appendix: Potential Customer Demands ................................................................................. 76
B. Appendix: Benchmarking Analysis ........................................................................................... 77
C. Appendix: Detailed SWOT Analysis .......................................................................................... 79
D. Appendix: Expected Benefits ................................................................................................... 82
E. Appendix: Expert Interviews .................................................................................................... 84

6
Table of Figures
Figure 1. Benefit Maximisation through Usage ............................................................................................... 12
Figure 2. Automotive Trend: Connected Driving ............................................................................................. 12
Figure 3. A Simplified View of the IoT Ecosystem ............................................................................................ 14
Figure 4. IoT Ecosystem from Technology and Business Applications Perspective ......................................... 17
Figure 5. Main IoT Technology Enablers and Providers ................................................................................... 17
Figure 6. General Vehicular Data Categories ................................................................................................... 39
Figure 7. Major Trends in the Automotive Domain ......................................................................................... 43
Figure 8. Simplified Automotive Supply Chain ................................................................................................. 44
Figure 9. Standardisation Needs for Data Revolution...................................................................................... 53
Figure 10. Data-based Business Model: Principle ............................................................................................ 59
Figure 11. Detailed SWOT Matrix .................................................................................................................... 79

7
List of Tables
Table 1. Social Impact of Industrial Revolutions… so far ................................................................................. 21
Table 2. Significant Automotive-related Contributing Applications to Data Marketplace .............................. 38
Table 3. SWOT Analysis (Short Summary) ....................................................................................................... 55
Table 4. Ansoff Matrix - Considered Options ................................................................................................... 56
Table 5. Business Model - EV Charging Infrastructure: Stakeholders .............................................................. 56
Table 6. Business Model Canvas: EV Charging Infrastructure.......................................................................... 58
Table 7. Business Model Canvas: Data Analytics Model .................................................................................. 60
Table 8. Five Basic Questions - Potential Customer Demands ........................................................................ 76
Table 9. Detailed SWOT Matrix ........................................................................................................................ 79
Table 10. Expected Benefits - Detail ................................................................................................................ 82

8
List of Abbreviations
AI Artificial Intelligence
BRICS Brazil, Russia, India, China and South African Republic
CPS Cyber-Physical Systems
DaaS Data as a Service
HMI Human Machine Interaction
ICT Information and Communication Technology
IoT Internet of Things
EC European Commission
EU European Union
EV Electric Vehicle
GDPR General Data Protection Regulation
M2M Machine to Machine
MaaS Mobility as a Service
MVP Minimum Viable Product
OEM Original Equipment Manufacturer

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Acknowledgements
Pope Innocent III commanded the Cathar Crusade to
eradicate heresy from Southern France in 1209. The
campaign began with the Massacre at Béziers, where 200
heretics were under the protection of the local inhabitants,
20 000 strong. Doubts could be raised about the exact words
spoken by Arnaud Amalric, a papal representative who
accompanied the crusade. The scriptures, written many
years later, claim that as the soldiers marched into the town,
they asked Amalric how to distinguish heretics from the
townspeople. The solution was allegedly simplified to:
“Kill them all. God will recognise his own”.
Many have contributed to this holy grail disguised in the
form of a thesis, but in a quest to look on the bright side of
life, differentiating between them would not contribute to
the maximisation of benefits and creation of value. Hence, I
refrain from distinguishing benevolent from malicious and
would simply like to express my thanks to all of them!
Omar Veledar

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1. Introduction
A central stage of digitalisation is taken by connectivity. Connectivity and digitalisation
are top automotive trends (KPMG Automotive Institute, 2019). Digitalisation carries a
genuine potential to reshape established value chains and to modify the rules of the
competitive game. However, benefits realisation promised by connectivity is only
possible if there are progressive technical developments, which calls for a reorientation
of strategy. The technology could only be improved if enough resources are dedicated to
the increase of critical knowledge in the field. Simultaneously, connectivity is no longer a
radical idea as it once was. Maturing technology is lowering the uncertainty level and is
creating a free path for the creators of applications to reap the benefits of their
investments.
One could be an integrator, which infers the outsourcing of critical activities and
establishes a high dependence on technology providers. The resulting limited control over
own processes and the shallow knowledge pool impair the realisation of unique benefits.
In contrast, building own capacity towards the emerging connectivity solutions support
the existing business models and new opportunities are revealed.
The smartphones have revolutionised the way we do things, but it took them several
years to create a real impact. However, organisations that were reluctant to engage with
the new technology, had to play a catching game. Similarly, the reluctance to engage
restricts capture of the opening Internet of Things (IoT) market. Participation in technical
progress and exploitation of data analytics is geared towards learning from successes and
failures. Hence, there is a room for stepping into the leadership position. A quarter of
executives also deem business model innovation as a significant factor essential for the
success of the IoT based strategy (The Economist Intelligence Unit, 2017). Consequently,
this thesis targets exploitation opportunities created by the integration of IoT into the
automotive industry as well as related business models.

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1.1. Digitalisation semantics
“No matter how much you love exploring digital manipulations, you must remember that
the way we walk will always remain to be analogue”. That is a phrase, from my
undergraduate days, which remains true. However, the evolving technologies are causing
the ‘analogue’ to superficially lose its appeal and ‘digitalisation’ is propping into the
everyday business language. Its overuse demands practical answers to replace the
rhetoric. The real meaning of ‘digitalisation’ is baffling, even for the experts.
The disambiguation is semantic. While ‘digitisation’ is the process of analogue data
conversion into its digital equivalent, ‘digitalisation’ refers to the digital transformation
of a business or industry (Bloomberg, 2018) (Brennen & Kreiss, 2016) (Parviainen,
Tihinen, Kääriäinen, & Teppola, 2017) (Grey & Rumpe, 2015). It is not only concerned with
the data and related processes, but also with the reworking or formation of business
models that accommodate the new assets. Digitalisation is leveraging on digitisation to
enhance business processes. Its appeal is derived from the potential to reinvent the way
we utilise assets to add value as well as to create impact. Digitisation is a technical
component of digitalisation.
1.2. Influences and realisation
Digitalisation appears either as an aspiration in mission statements or as a supplementary
objective that should eventually contribute to overarching goals. That eventuality is
predominantly an open-ended abstract matter that often lacks a specific definition. What
lies within the core of digitalisation is a change. For that change to be effective, the
rhetoric must be replaced by a factual implementation. It is only the usage that
maximises the benefits, as depicted in Figure 1, while the plans and the deliverables are
effectively meaningless in terms of impact creation (Centre of Excellence in Project
Management (CoEPM²), 2016) (Driver, 2000-2019). The resistance is inevitable, as in
many cases it is more appreciated to benefit from the past than to have a boat rocked.
The incentives are rarely based on courageous decision making about a complete
overhaul of strategy. Hence, the resistance to change becomes logically justifiable.

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Figure 1. Benefit Maximisation through Usage
1.2.1. The Drivers of Change
The status quo is unsustainable (Figure 2), due to the combination of societal
expectations, technology push and the ever more evident market pull (Veledar,
Damjanovic-Behrendt, & Macher, Digital Twins for Dependability Improvement of
Autonomous Driving, In Print). Despite the resisting mindsets, the digitalisation is
inevitable (Armengaud, Peischl, Priller, & Veledar, 2019) (Partridge, 2017) (IF Editorial
Team, 2018) (Brechbuhl, 2015) (Bughin, et al., 2016) and organisations must evolve or
face extinction (Anadkat, 2017). The speed and quality of success are highly dependent
on openness towards changes, on skills to execute the same and on willingness to start
implementing. The adjustments are unavoidable in this field and are likely to be a
decisive factor in the survival of organisations (Ismail, 2017).
Figure 2. Automotive Trend: Connected Driving

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1.1.1. The technology as an enabler of a business revolution
IoT is creating a business revolution that is enabled by technology (Schwab, 2016)
(Maynard, 2018). That relies on a digital feedback loop, which improves interactions
between employees, assets, operations and customers (George, 2019). That revolution in
the automotive domain is propped by the concept of a connected and automated vehicle
(Macher, Diwold, Veledar, Armengaud, & Römer, In-print). The result is that the vehicles
are evolving from means of transportation to integrated systems in a connected world
of things.
This steady IoT evolution is also contributing to the megatrend, named Industry 4.0. This
technology uplift is supported by an amalgamation of automation and data exchange.
Over 90 per cent of data generated in the world is contributed by IoT devices, the
connected “smart things” that also involve us. The number of active devices is estimated
at 2 billion in 2006 and is projected to be 200 billion by 2020 (Marr, 2018). This business
revolution is a key reason for research interest in IoT.
1.1.2. The realisation - value creation
The expected benefits are not likely to be realised through wrapping of the old-fashioned
business methods into a ‘digital layer’. The business activities must partake in holistic
digitalisation to create value (UNCTAD, 2018). The world is going through an industrial
revolution (Schwab, 2016), hence drawing Industry 4.0 into a driving seat of industrial
digitalisation. Superficial patching-up will not transform businesses into entities capable
of competing in the upcoming market conditions. However, hopes of a miraculous
discovery of wrapping IoT solutions are evident, as 55% of surveyed executives expect IoT
to contribute to internal cost savings and to generate external revenue, despite the IoT
investments being well below the projected levels (The Economist Intelligence Unit,
2017). There is a discrepancy between investment and expectations. The change must
be right from the heart, all the way to the outer extremities. That can be only achieved if
the digitalisation is embraced in practical terms.

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1.2. The objective of the thesis
Considering the existing IoT ecosystem of Figure 3 (Telecommunication Standardization
Sector of ITU, 2012), the objective of the proposed work is to define new business model
possibilities that rely on the utilisation of IoT technology within the automotive industry.
Figure 3. A Simplified View of the IoT Ecosystem
The business models are to be defined in terms of structure and generic strategy only.
Consequently, the work is to seek realistic combinations and strategic partnerships that
would deliver the most impactful solutions. The aim is also to outline the
complementary types of providers in terms of impact creation. Detailed implementation
of the business models is not an objective.
1.3. Research question
The IoT environment encompasses a wide range of stakeholders (Telecommunication
Standardization Sector of ITU, 2012). They map to a variety of fixed business roles (Figure
3). However, the relationships between the roles are dynamic and every stakeholder is
free to fulfil more than a single role, subject to their core business skills, interests and
strategy. In general terms, the roles are defined as:
• Device providers deliver data as prescribed by the service logic or respond to the
data arriving from other stakeholders

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• The pivotal role of the network provider is to control the flow of delivered data
through capabilities of the IoT network
• Platform providers offer a range of capabilities, all of which are aimed at system
integration and data handling (storage, processing or device management)
• They are also the interface to the application providers, which combine assets
into an application worthy of being presented to the…
• …end user, application customer.
The main interest lies in the definition of the new business models and the relevant
strategy needed for benefits realisation and exploitation of the derived results.
Consequently, the research question is:
What are potential new business models and general value creation strategic measures
that could realise the promised benefits of the IoT technology within the automotive
industry? Also, the work should describe the expected benefits in broad terms and what
general exploitation measures would increase the impact?
Some (vertical) providers might attempt to occupy a combination of roles, if not the
complete supply chain. However, the question is how effective such a strategy would be.
1.4. Thesis structure
A review of existing and emerging technologies and business possibilities is provided in
Chapter 2. It considers potential progress and its appropriateness for the automotive
sector. Chapter 3 is concerned with the analysis of the most promising ideas that aim
towards the implementation of IoT into the automotive world. The resulting options are
evaluated in chapter 4 and the ensuing recommendations are presented in chapter 5. The
conclusion of chapter 6 is followed by some supplementary materials in the appendices.

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2. Readiness levels
This chapter provides a review of the existing and emerging technologies that are
empowering the evident business revolution. The focus is on the suitability of
developments for the business environment and exploitation through the business
models with emphasised automotive applications.
2.1. Technology review
Uncommonly for an engineering term, the IoT has no generally agreed definition. The
attempts to define it, focus on describing its operation and features. It is viewed as an
enabler of business outcomes related to operations or customer relations or any part of
the business that could benefit from new insights based on the analysis of additional
data (Daecher, 2017). The interests are rooted in the ability to expand the internet into
physical devices and to collect data. That is possible, as the assets are increasingly
equipped with sensors and are connected at any time, at any place and with anything.
Theoretically, they offer an infinite supply of real-time data. Some relayed data is raw,
while some are a selected set that is passed by the edge technology, which at the same
time performs real-time data processing and decision making on its own, as instructed.
Platforms are responsible for the management of the received data. The applications
ingest huge data volumes (Big Data) and use analytics of different levels of sophistication
to complete the loop by adding some value through the creation of proposals for actions.
A review of the simplified IoT ecosystem (Figure 3) with a focus on technology and
services (Armengaud, Peischl, Priller, & Veledar, 2019) is particularised in Figure 4, where
the virtuous circle is closed, leading to new insights; the bottom line is the creation of the
value through management of the real-time data (Daecher, 2017). That opens a
possibility for optimisations and transformation of businesses and industries through the
digitalisation.

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Figure 4. IoT Ecosystem from Technology and Business Applications Perspective
2.1.1. Technology enablers
To extend its reach, IoT leans on technical developments, some of which are described in
this section. These must be considered if exploiting IoT capabilities in new business
settings. A crossover between these technologies and different providers from Figure 3 is
depicted in Figure 5 (partially derived from (Veledar, et al., In Review)).
Figure 5. Main IoT Technology Enablers and Providers

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5G is a new generation of cellular technology that foresees the fast delivery of huge
amounts of data. It also promises high reliability at low power requirements. Hence, 5G is
a crucial contributor to IoT, as it enhances reliable and fast communication of vast data
quantities at low power. There are concerns surrounding 5G technology and its potential
success (Baraniuk, 2019). However, these are outside of the scope of this work.
Machine to machine (M2M) is direct communication between devices with no human
interactions. It is vital for the collection of sensor data and its delivery via the network. Its
major deviation from IoT is that M2M devices are not necessarily connected to the
internet. The two device groups are often bundled and dealt with together as they
duplicate certain features and IoT often does rely on M2M communication.
IoT and AI are highly interconnected. IoT is there to cluster the data, while AI is a set of
algorithms used to mimic cognitive skills at a low level so that machines can make use of
the data and can generate appropriate decisions.
Cloud computing is on-demand computing that utilises shared resources and removes
the need from the users to manage or to have the knowledge and skills necessary for
dealing with those resources. The benefit of cloud computing is its ability to provide
independent services based on IoT delivered data while maintaining full flexibility. Cloud
computing contributes to reliability and secure operation of IoT. These are two important
aspects that IoT must deliver should it remain a credible technology of the future.
While cloud computing employs shared centralised resources and diminishes the need for
maintenance and operation, edge computing localises decision-making close to the
action i.e. data analysis and decisions occur near the sensor location. It is a crucial enabler
of Autonomous Driving (AD), as it responds to driving scenarios in real-time and
minimises data flow to the outside world. A sound and efficient implementation of the
edge computing lowers the strain on communications, caused by the high data flow.
As the number of connected “things” is rapidly increasing, their real value is encapsulated
in the range of data generating low-cost sensors. These devices sense and inform of their

19
environment. The evolving technology is improving sensor abilities while pushing down
their costs. That is significant for the automotive industry, as the key factor of AD is the
ability to sense the vehicles’ environment and to communicate this data for adequate
decision making. The future of connected vehicles is highly reliant on sensors and
semiconductor innovations (Infineon Technologies, 2016).
2.1.2. Common application fields
By connecting devices, IoT fashions smarter results and exploits common items in a style
that is not previously thought of. Many industrial domains benefit from such
applications. The resulting technology evolution is demanding from the service providers
to respond to the changes, even at the consumer level. While a 2015 survey predicted
that almost half of the consumers would own an IoT device by 2020 (Murdoch & Johnson,
2015), the same survey no longer reviews device numbers. Instead, it assumes that we all
own some connectivity enabled “thing” and the theme of the survey is shifted from
quantity to the relevance and adaptability (Sovie, et al., 2019). That proves the speed of
change and the level of penetration of IoT devices in unthinkable domains. Some major
applications are establishing themselves as major contributors to the IoT expansion.
2.1.2.1. Automotive
The autonomous revolution is converting vehicles from the classic view of mechanical
systems to a digital playground for Cyber-Physical Systems (CPS) and IoT. CPS is a union of
intelligent electronic-mechanical-physical systems that incorporate cognitive and human-
centric applications. They collect data, make decisions and control vehicles. Hence,
vehicles are edging towards complete automation and are also turning into data hubs.
The connectivity is a crucial enabler of this progress towards the AD and is also a tool for
enhancing user experiences, be it in terms of driving or infotainment.

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2.1.2.2. Healthcare
Smart systems are increasingly making decisions that affect our health and wellbeing. The
handling of ample physiological data quantities from patients is very demanding. Some
complex decision-making in life-threatening situations leverages on intelligent machines.
IoT communicates personal data so that it could be analysed in real-time. The healthcare
and the fitness sector are experiencing a boom driven by connected wearables.
2.1.2.3. Smart homes
Integration of smart meters into homes is contributing to optimised energy consumption
and improved user experiences. That is further enhanced with distributed (renewable)
electric power generation, which longs for optimised charging and discharging, be it
either the user or grid centric. Smart home applications rely on decisions based on
analysis of sensor data, which is collected and communicated using IoT.
2.1.2.4. Smart city
Smart City is a concept powered by data management and insightful analysis. Decisions
about and for the citizens improve life quality through the utilisation of infrastructure. A
huge number of connected sensors are engaged for improvements in city operations.
What do the monitoring and management imply, depends on the city and its available
infrastructure. Hence, the beauty of IoT, in this case, is seen in its flexibility and
adaptability aimed at optimising a full range of public assets at a reduced cost. Some of
the exemplary infrastructural assets include utilities, smart buildings, emergency services,
parking spaces, electric vehicle (EV) charging stations, advertising infrastructure etc.
2.1.3. IoT applications in the automotive industry
IoT bears benefits through implementation and consequent usage (section 1.2). Two key
automotive trends (AD and Industry 4.0) are complemented with many IoT device
options. Some of these are described in this section.

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2.1.3.1. Industry 4.0
Industry 4.0 is a topic for itself that extends the tradition of deep societal change, as all
industrial revolutions so far (Table 1). It is not explicit to the automotive domain, but
some of its features are specific in its context. This industrial revolution dictates the
usage of automation, which enables the compartmentalisation of production. The
distributed manufacturing is driven by IoT and CPS which is acclimatising (Kagermann &
Wahlster, 2013) (Wang, Törngren, & Onori, 2015) and is creating a major impact in the
manufacturing context (The Economist Intelligence Unit, 2017). The interconnectedness
and transparency are critical for the automotive industry, as they enable effective life-
cycle management. A peculiarity is seen in the testing processes which are continuously
fed with data (Macher, Diwold, Veledar, Armengaud, & Römer, In-print).
Table 1. Social Impact of Industrial Revolutions… so far
Industrial
revolution
(~1800)
Second industrial
revolution (~1900)
Third industrial
revolution (~1950…) Industry 4.0 (now…)
Progress Mechanisation
(textile industry)
Electricity, gas, oil,
assembly line (cars),
phone, radio
Nuclear energy,
semiconductors
(transistors,
microprocessors)
IoT, CPS, AI, AD,
Smart anything, edge
computing, cloud
services…
Main
character
Move from
manual labour to
machines
Mass production,
industrialisation Automation
Merging
technologies,
distributed
manufacturing
Mobility Steam engine -
train
Internal combustion
engine / cars -- AD, Smart mobility,
MaaS
Social
impact Move to the cities Birth of consumer
culture Continual urbanisation Smart cities
2.1.3.2. Autonomous driving
The control of autonomous vehicles entails low or no human interaction. Distinct levels of
automation are defined by the standard J3016 (Society of Automotive Engineers, 2018).
The road to full automation depends on connectivity options, which add the possibility to
share driving data. The communication includes different vehicles and infrastructure. This
transformation is likely to be swifter than anticipated, just as the change from horse-
drawn carriages to the motor-vehicles at the start of the 20
th
century (Rowlatt, 2018).

22
2.1.3.3. Predictive maintenance
Maintenance and servicing are based on time intervals disregarding the real state of the
maintained assets. If the asset age is a primary factor in timing replacements or service,
there are inevitable unforeseen failures and exchanges. In contrast, predictive
maintenance leverages on data to decide about replacements just before failures occur.
The benefits are expressed in lower maintenance costs and reduction of downtimes.
2.1.3.4. Fleet management
Real-time fleet monitoring offers efficiency improvements through enhanced fleet
management based on data and insights. The main benefit of fleet monitoring is
maximised utilisation of mobile assets. If integrated within the concept of Smart City,
fleet monitoring delivers powerful benefits for both, organisations and citizens. Fleet
management is especially beneficial for logistics and freight organisations.
2.1.3.5. Electrification
As EV evolution is edging towards a mass-marketable option, there is a need to respond
to the lack of charging facilities and the resulting inability to charge batteries as required.
IoT highlights energy-related business prospects. The benefits are also felt by the energy
distribution grid. That is further emphasised when combining the trend or renewable
energy and the element of unpredictable supply. The EV drivers should be incentivised to
charge their vehicles at specific times and locations, so to minimise the disruptions to the
electric grid. Those benefits are maximised through IoT data exploitation.
2.1.3.6. Truck platooning
Platooning is an AD sub-use-case. It lets small convoys of delivery vehicles to follow each
other very closely and to unanimously react to the road conditions. Such functionality is
enabled through digital connectivity. The added value is seen in a considerable reduction
in fuel consumption as well as improvements in terms of road congestion.

23
2.1.3.7. Mobility as a service (MaaS)
MaaS represents a shift from private vehicle ownership towards mobility based on a
combination of public transport and shared economy. The key is in simplicity and
possibility to match mobility options to personal needs. The major driving forces for the
MaaS are the urbanisation and technological and business development.
2.2. Market situation and future expectations
The automotive domain is contributing to the EU’s labour market with 13.3 million jobs or
6.1% of EU employment (European Automobile Manufacturers Association, 2018). In
macro-economic terms, the trade surplus equals €90.3 billion, which is greater than a
third of the EU’s total trade balance in manufactured goods (European Automobile
Manufacturers Association, 2018). Bearing in mind the contribution of this sector to the
economy, it is no surprise that the governing bodies are showing considerable interest in
the automotive wellbeing. However, the success may not be viewed only through the
market position, but through the maintenance of a healthy triple point equilibrium
between the financial markets, the social impact and environmental sustainability. The
automotive sector counts on IoT to preserve that equilibrium.
The next big things are mobile connectivity, the value of the customer data and AD
(KPMG Automotive Institute, 2018). Connectivity is a prerequisite for future vehicles
(ERTRAC Working Group "Connectivity and Automated Driving", 2019). It also opens
avenues for novel business models and provides new ways of creating value for the end
user by delivery of more intelligent and adaptive products. Digitalisation provides a
unique prospect for attracting investments, while connectivity is a major sponsor of
future business paths (European Commission, 2016). By engaging connectivity, the
automotive sector aims to remain a major macroeconomic contributor. The connectivity,
inclusive of IoT, is estimated to have contributed to roughly 0.2% to the global GDP
(Sivakumaran & Castells, 2019). Forecasts are unreliable but estimates claim an increase
to somewhere around 3% of global GDP by 2035 (Sparks, 2017).

24
Hence, the potential methods of extracting value from dormant assets and processes are
enticing the executives to work towards the creation of new revenue streams and
improvement of supply chains through the usage of IoT. Data are central to these plans,
due to the potential to create value through better-informed business decisions. Data
also add to the smartness of products and services offered and improve user experience.
2.3. Market drivers
Subject to technology and industries in question, a simple search for drivers of innovation
and key performance indicators yields a variety of results. A broad categorisation creates
groups that are strongly related to market changes, such as changing customers,
competition and the business environment (Geoffin & Mitchel, 2017). The categories
related to the research question posed in this work are further elaborated in this section.
2.3.1. Changing customers
The reported effect of millennials on modern culture may not be ignored by the
automotive industry. Yet, the predicted effect is not as strong as anticipated. As this
generation is maturing, the expected low vehicle ownership and usage of new vehicle-
sharing schemes are diminishing in favour of private ownership (Buss, 2018). Still, the
ownership is decreasing due to the urbanisation and acceptance of MaaS. As MaaS is
becoming practical (Zipper, 2018), the users are shifting their mobility preferences due to
financial savings posed by not owning a vehicle. On average a cost of a vehicle in the EU
ranges around €600 per month (Harreman, 2016). MaaS is slashing that cost and the
customers’ expectations are altering according to their needs. The mobility stakeholders
are being forced by the end users to adapt their business models.
Changing customer behaviour goes further in terms of vehicle customisation (Rainbow
Audio, 1989-2019). The myriad of emerging customisation possibilities and new players
are threatening traditional business models. The customisation demands are a far cry
from the Henry Ford’s idea of “Any customer can have a car painted any color that he
wants so long as it is black” (Wikiquote, 2019). The customisation is a result of lifestyle

25
choices, so much so that majority of Original Equipment Manufacturers (OEMs) have
already installed fully connected infotainment systems in all of their new vehicles
(Heineke, Möller, Padhi, & Tschiesner, 2017). The connectivity is an important feature
not only in terms of basic vehicle functionalities but also in terms of additional services,
which are more open to creative business development. These models should consider
that vehicle occupants are perceiving interior as an extensive Human Machine Interaction
(HMI) system. As such, it provides comfort, safety, smart and enjoyable life-on-board in
all situations and benefits for the vehicle users as they can tailor their driving time for
other productive activities (Fonsalas, 2019).
At the higher end of the spectrum of human age, there is a different kind of need for
mobility change. The shape of the population pyramids (Population Pyramid, 2019) in
developed countries (Milne, 2019) and especially in Europe is consistently departing from
the suggestive shape in its name, i.e. the pyramid. The measured and the projected
population deviations reflect changes in fertility rates, life expectancy and migration flow
and are putting pressure on public spending (European Commission: Economic and
Financial Affairs, 2018). The governing bodies are responding and the private sector does
not have to wait for direction to be determined, as it is clear that the way the older
people live is affecting the patterns of mobility (Shergolda, Lyonsa, & Hubersb, 2015).
There is an open space for new business models, many of which could rely on AD and
shared services. Consequently, MaaS, which is described in section 2.1.3.7, is given an
additional boost.
In between those two sides of the age spectrum i.e. the millennials and older generations,
there is a gap filled by aspirations and increased environmental awareness. While the
initial hype and demand for EVs were aspirational (e.g. the case of Tesla), the growing
demand for EVs is driven by “green” attitude. It is also fuelled by the competitive costs of
these vehicles and their extended driving ranges. An issue of the current lack of charging
infrastructure follows these vehicles. The connectivity solutions are opening the space for
new business models and the possibility for the further evolution of the data
marketplace. The benefits for the end users are to result from the increased usage. In this

26
case, that is possible from non-diminishing convenience in terms of energy needs, as for
the EV drivers the first and foremost priority is to be able to drive. So, the success of new
models is based on services that satisfy the demand for electric energy. The simplest form
of convenience improvements could be fulfilled by charging stations at shopping centres.
Additional incentivisation is a secondary success factor on top of the convenience.
An additional driver of success is the “fun factor”. For some, vehicles have a lot more to
do with the experience and pleasure than with finances (Zembacher, 2019). For example,
to rent and drive a scooter for several hours might be an option for many that would
otherwise never wish to own such a vehicle. At that point, the financial aspect becomes
less relevant. Hence, new possibilities are created for inventive business models that aim
to cash in through offer of leisure related mobility. It is also the usage that delivers
benefits. The usage normally increases if there is a fun factor included. Some will use
standard products and services for personal satisfaction and that should be considered
when creating business models.
2.3.2. Competition
Competition within the automotive domain must be viewed on a global level, as the
country borders generally do not represent such a barrier for new business models. The
models that do not bear tangible assets are relatively easy to expand or copy in
comparison to the traditional business models, which are based on selling physical
resources. Two traditionally crucial trends of competition in global markets are the
reduction in differences among countries and more aggressive industrial policies (Porter,
Competitive Strategy: Techniques for Analysing Industries and Competitors, 1998). As the
purchasing power between citizens of developed and emerging economies is narrowing,
the characteristics of the global market are also changing. The competitive aspect is
further extended as the emerging economies compete in the technological field as well as
in the areas of new services and business models. So, the BRICS countries, for example,
are no longer the markets where EU OEMs are left to operate at their own will. These
countries have evolved the automotive industry of their own and are successfully

27
competing. As the purchasing power of the population of these countries increases, the
competition is no longer in the sphere of less expensive products and services. Instead,
the high-tech offerings are also at stake. This is especially valid for China, which is
becoming an automotive powerhouse and is the future e-mobility market, as it is about
to leapfrog the competition with its battery electric vehicles (KPMG Automotive
Institute, 2019). Hence the increased competition is deepening the need for more
innovative solutions.
Additionally, the new technologies are becoming more accessible to smaller players, as
the barriers to market entry are being lowered through customisation. For example, the
possibility of 3D printing components, or even complete vehicles is allowing SMEs to
compete with traditional organisations in niche parts of the market (Gregurić, 2019).
The increasing competition on several fronts is squeezing the existing profit margins.
Hence, there is a need for new methods of staying afloat. The game is no longer about
selling the metal, but about generating new services that increase the trust and keep a
healthy relationship with the end users. It even makes sense to subsidise vehicle sales to
maintain the link to the consumers. That link is a sustainable business opportunity i.e. not
a for short-term gain, but a long-term relationship.
2.3.3. Changing business environments
The world is already a global village. The markets are more liberalised and accessible than
they used to be. That is despite the transient glitches and threats posed by populist
uprisings around the world. This results with the increased competition, as described in
section 2.3.2. The business environment is also metamorphosing due to the reduced
ownership and increased sharing economies, as already discussed. This is manifested in
shrinking automotive manufacturing in Europe. Coupled with the emotional reactions to
political establishments and with no reasonable consideration of potential penalties, the
events such as Brexit are resulting with serious longer-term consequences on European
vehicle manufacturing industry through reduction and even closures of manufacturing
plants (Campbell & Tighe, 2019) (BBC Wales, 2019) (BBC Business, 2019) (Davies, 2019).

28
2.3.4. Market drivers in a nutshell
The decreasing vehicle ownership and the search for mobility alternatives are uplifting
MaaS as an umbrella term for a wide range of innovative business models. The benefits
include reduced cost for the end consumer (pay for what you use) and improved asset
utilisation through the vehicle sharing schemes. The long-term benefits include reduction
of urban traffic and improved air quality. Traditional business models are threatened by
customisation and the need for combined usages (e.g. infotainment in vehicles and other
HMI applications).
There is also a push for new services and sustainable relationships with the customers
due to competition on several fronts (e.g. geographic, technical, size and dynamics etc.).
The consequences of the transient events in the dynamic business environment cannot
always be absorbed by the industry and are creating a long-lasting impact.
In summary, users desire easy access and convenience (Zembacher, 2019) with some
additional value. They expect to be (financially) incentivised, or else, they will not engage.
Hence, business models must evolve together with the market.
2.4. Barriers
The underlying forces of industrial competition remained intact despite the altered
barriers to entry caused by the advent of the Internet at the turn of the century (Porter,
Competitive Strategy: Techniques for Analysing Industries and Competitors, 1998). One
could equally expect that foundations of the industrial rivalry will remain intact, despite
the potential changes in the way that some services are run. However, as the market
entry barriers fall (section 2.3.2), the competition will grow. Some of the main barriers for
the IoT entrants are considered in this section.
2.4.1. Security Concerns
A survey about connected and autonomous vehicles concludes that the greatest obstacles
to the acceptance of connected and autonomous vehicles are related to cybersecurity

29
and privacy concerns (63%) (Foley & Lardner, 2017). Hence, development efforts target
safe and secure integration of IoT into AD and Industry 4.0 (Veledar, et al., In Review). The
full potential is attainable if a certain level of trust is reached (Drobics, 2019). That
requires guarantees that cyber-security attacks would not compromise the functionality.
Also, secure solutions may not diminish performance. That must be integral to business
models, as maximisation of benefits demands the trust. Any deviations are likely to
negatively impact the acceptance of technologies and services.
Cyber-attack data contribute to mature solutions, as they inform algorithms about the
damage. Despite the advances, many new threats are still substantial (Sonic Wall, 2019).
These threats must be considered at the design stages, which is an issue, as many IoT
devices are not designed or maintained with security as a priority (Tannenbaum, 2017). At
later stages of the product lifecycle, continual certification must be used as a guarantee of
security (Drobics, 2019).
2.4.2. Privacy concerns
There is an incompatibility between the privacy and the IoT, as the privacy separates
things, while the IoT actively connects everything into a single ecosystem (Morrow, 2018).
Data privacy is also interlinked with security. Any breach of privacy is likely to negatively
impact the long-term trust and confidence, as there is a common interest in ensuring that
data is accessed for the right reasons (Hofheinz & Osimo, 2017). The potential issues
might be resolved, to a certain extent, by the EU’s General Data Protection Regulation
(GDPR), which regulates privacy under the EU law (Legislative act, 2016). The regulation
facilitates progress, but if it is misunderstood and not adequately implemented, it could
potentially become a market inhibitor due to introduced confusion.
2.4.3. Implementation difficulties and lack of regulation and standardisation
The source of IoT implementation difficulties (technical or organisational) is the chicken
or egg dilemma of IoT. These difficulties are market inhibitors with many aspects, such as
high cost of deployment, accountability and responsibility for the infrastructure,

30
transparency and incompatibility of technical solutions etc. The accountability is like a hot
potato passed around between the industrial partners and the governing bodies. To
create rapid results, a solution is that cities take over responsibility for the installation of
infrastructure (section 3.5). Such an option could be endangered by a lack of
standardisation and consequent incompatibilities of solutions between different
geographies. The importance is placed upon a communication that is simple, broadly
understood and widely accepted (Hofheinz & Osimo, 2017), but also standardised.
Gearing up all the stakeholders and aligning their interests to raise the whole ecosystem
at once is a utopian solution. Hence, standardisation must be one of the key steps, so that
different implementations offer a uniform environment for interactions.
2.4.4. Technology fragmentation
Organisations are implementing IoT solutions and generating positive outcomes.
However, many do not scale up. That creates the technology fragmentation, which is
strongly correlated to the lack of standardisation (section 2.4.3). That could be tackled by
the deployment of infrastructure, which is yet to be fully developed. The question of
accountability arises. The industry could contribute, but a direction should be defined. In
the digital world, infrastructure includes more than sensors, masts and cables i.e. it also
needs legislation and regulation.
2.4.5. Industrialisation and market rollout
Smart mobility is an attractive IoT application. Some models are market-ready. The arising
issue is linked to industrialisation and successful large-scale market rollout outside of
controlled environments. The existing standardisation topics should resolve the issue of
segmented developments. However, the markets are calling for the deployment of
products that cannot yet be reliably utilised with the existing technology (Zembacher,
2019). Hence, minimum viable products or intermediate alternatives might need to be
considered (e.g. G5) to conquer potential resistance through stakeholder engagement.
The feedback from the usage of intermediate solutions would aid the reduction of

31
existing inhomogeneity. Hence, offered products and services should be modular and
upgradable, so to assist the sustainable evolution of the IoT ecosystem.
2.4.6. Integrative service correlation
There are potential side effects resulting from the integration of novel smart services. The
value creation must be fashioned through the mixing of different methods and assets. It is
expected that aside from the monetary benefits, the rise of flexibility through the
amalgamation of services should be one of the major benefits of integration (Zembacher,
2019). One would desire mutually positive contribution of services and creation of
additional value. However, some of the benefits could oppose each other and cancel out
the value that is generated by each service individually. In the case of smart services, the
cities, which are seen as living and evolving organisms, could experience some diverse
effects. For example, the peer-to-peer ride-sharing decreases the need for parking spaces
in the inner cities, but it simultaneously increases participant numbers. The integration
topics may not be omitted from the planning of solution rollouts (Zembacher, 2019).
2.4.7. Solution financing
The financing contributions by stakeholders must be defined. The challenge arises from
joint integration of solutions, as partial deployment is unlikely to yield full potential.
There is reluctance from the major stakeholder groups to take control of the deployment
of large infrastructural solutions. The reluctance is causing glitches in performance and
slow uptake of solutions.
2.5. Relevant roadmaps and legislation
Cost-benefits study (C-ITS Deployment Platform, 2016) (C-ITS Deployment Platform, 2017)
indicates significant returns on investment in automotive connectivity if a coordinated
and accelerated deployment is assumed. Hence, the current public funds focus on
increased cooperation across sectors to promote potential solutions and to enhance
development. There are significant increases in EC’s ICT related funding targeting

32
automotive applications in recent years. The EC’s clear direction, in combination with the
heterogeneous and immature market, paves a way for leadership by example through the
development of commercial applications with the support of public funds. There is a
promise from EC’s side to bridge the gap between the currently limited infrastructure
and the network that will be required for a successful commercial deployment (C-ITS
Deployment Platform, 2017). The support for the applications is strongly connected to
the societal challenges identified under Horizon 2020 programme and can be summarized
under the umbrella of ‘Smart Everything Everywhere’, riding the next Internet wave by
integrating networked electronic components and systems in any type of product,
artefact or goods (ECSEL JU, 2018). The private sector is also pushing connectivity
developments. An extensive survey of global automotive executives identifies
connectivity as the single most important prerequisite for the provision of new services
(KPMG Automotive Institute, 2018).
The earlier focus on pure connectivity provision is
shifting towards “closing the loop” to use the generated data in a smart way and to create
benefits such as improved air quality, optimised resource efficiency and better
sustainable services at a lower cost (Thompson & Reimann, 2018). That is especially the
case with the start of the autonomous vehicle revolution (Ahopelto, et al., 2019)
(European Commission: Directorate-General for Research and Innovation, 2019).
The (public and corporate) roadmaps are determining the direction. The slow evolution
has already been taking place under the influences from the market and societal
expectations as well as from the technology push, as depicted in Figure 2. The possibilities
for exploitation of connectivity within the automotive industry are immense. It is the final
exploitation link that is needed to complete the chain. The technology providers will
sooner or later deliver mature connectivity to take the burden from the applications.
The traditional business models must be updated to support their core businesses or
else they might disappear. There is a need for new business models and a clear strategy
for their implementation.

33
2.6. Capturing value through emerging and evolving models
The legislation and standardisation are lagging behind the technical developments
(section 2.4.3). Also, business model developments are slow and defragmented. On one
hand, a business model is supposed to describe the rationale of how an organization
creates, delivers, and captures value using IoT in this particular case (Hofheinz & Osimo,
2017), but there is a consensus that organisations are building IoT products without a
tangible method of capturing that generated value. While organisations are searching for
the appropriate exploitation models, the regulators are also lagging in defining the rules
(e.g. lack of 5G regulation), especially at the global level (which highlights the need for
roaming setup). The combination of existing technologies and the new business models
in line with the shared legislation will define the progress.
Business values cannot be defined purely in financial terms. Organisations are forced to
look broader than in monetary bounds. Following own rules is possible only if there is no
competition. Otherwise, the market positioning seeks better quality, better customer
intimacy and ultimately, new business models that can sustain the competitive edge in
the field and consequently capture the value.
The IoT value is generally created through sensor data that delivers some insights, so to
respond to the environment the “things” are experiencing. However, innovation must
focus on the new proposed value. Neither, sensing and actuation nor the combination of
the two together with the control algorithms are the new concepts. What IoT delivers is
the chance to have continuous connectivity amongst “things”, so that the sensing, data
management and controlling can be performed on an ongoing basis with no need for
human supervision. For any new business model, to be able to capture and deliver value,
it must leverage the unique proposition of continuous connectivity to the customers’
devices.
Some prospective models are grouped, based on their suitability for the automotive
applications from section 2.1.3.

34
2.6.1. Subscription Model and/or Service Model
The continual connectivity offers itself for exploitation through a reoccurring revenue in
contrast to the one-time sale. That is already offered by the mobile phone network
providers and it is very likely that some of those will also offer the same option for IoT
devices. The final user compensates the service provider continually for continuous
value. To what level are the hardware and software solutions integrated into the service,
depends on the level of integration of the service provider into the IoT ecosystem. The
offer could be from the very basic connectivity, all the way to the full vertical service.
Aside from the continual monetisation, the subscription service also supports payments
for upgrades, but also the freemium model, which supplements the free basic services
with customer payments for the more advanced features.
This business model nurtures customer relations through continual interaction. Should
the privacy and data ownership issues be resolved, the model offers an opportunity to
exploit user-generated data for monitoring, maintenance and targeted advertising.
The applicability of the subscription model in the automotive world is possible by offering
instrument or vehicle monitoring as a service or predictive instrument or vehicle
maintenance as a service. Some examples of such a service require IoT devices to:
• monitor machinery and predict maintenance periods – monetisation is achieved
through the sale of a maintenance contract
• measure energy consumption in a smart building – data is monetised through the
sale of energy audit and through energy optimisation services
• monitor manufacturing floor to measure efficiency and throughput - monetisation
comes from consulting services aimed at optimisation of manufacturing processes.
2.6.2. Sharing model
There is a plethora of examples that showcase the product purchase with poor
utilisation. An ancient asset sharing solution is practised in cooperative agriculture. The
model maximises product utilisation across a multitude of users. The customers are

35
paying a reduced price, based on the usage level, while the service provider benefits are
seen in further and faster market penetration with fewer products.
The available mobility sharing platforms had begun with bicycles, but have evolved to
include sophisticated mobility solutions, which will be extended with AD. The additional
options involve instruments for users with either lower buying power or for the
instruments that are used at a reoccurring, but infrequent basis. The model might also
help drive standardisation, which in turn should offer further benefits to the service
providers through the economy of scale.
2.6.3. Outcome model
This model lets the manufacturers enter long term contracts with their customers, so the
users can determine whether the products should be purchased at some later stage. If
they do not wish to have depreciating assets on their balance sheets, they might opt for a
leasing contract. The connectivity is used for precise monitoring so that the users
financially compensate the OEMs for the precise usage of these products rather than to
pay a fixed leasing cost regardless of how much the product is in use. The business model
has already emerged within aviation where guaranteed flight hours are sold instead of
the jet engines. The connectivity also provides data for predictive maintenance.
2.6.4. The razor and blade model
As the name suggests, the model is based on the principle of offering a subsidised price
product (razor) with the promise in mind that the consumers will be purchasing the
complementary products (blades) continually in the future. The model is, naturally, more
profitable when the need for the auxiliary replacement products is more frequent. Should
the user run out of the complementary products, their main product immediately
diminishes in value, as it loses its value proposition. The connectivity plays a major role in
this business model, as the asset provider is automatically informed when there is the
need to replenish the auxiliary products.

36
Manufacturing and testing segments are susceptible to this model, as any part reordering
could benefit from the continual connectivity. The ultimate goal is improved customer
experience through the minimised possibility of interruptions to production.
2.6.5. Data monetisation model
Data monetisation opens the path to competitive advantage as it provides effective and
timely data conversion into financial benefits by increasing revenues or decreasing costs
(Mohasseb, 2015). As already elaborated (section 2.1), data are IoT’s main asset, as it
enables value generation through adequate responses to the results of the data analysis.
However, the number of connected devices is huge and the generated data is Big. Hence,
IoT is contributing to the already existing Data as a Service (DaaS) offerings. However, it is
not always clear how to exploit these data and capture the value and that process is no
longer within the scope of technology, but it becomes a business challenge (Mohasseb,
2015). It is often the case that the data are not categorised, so it is difficult to pinpoint the
useful data sets. If that barrier is removed, then the data monetisation model can rely on
three main market drivers (Elloumi, De Block, & Samovich, 2019):
• Selling data generates new revenue streams and improves the business case for
digital transformation based on IoT
• Buying external and fresh data complement internal understanding
• Learning through analysis yield a greater value if trained using a greater volume
of representative data
The market drivers show that the model begins with offering own data, but it also
considers generating new opportunities and opening new revenue streams through
analysis of external data that is acquired at the data marketplace. It is the data analysis
and the ability to generate appropriate conclusions and responses that are crucial to this
business model. Providing that some of those conclusions target automotive safety or
potential financial incentivisation, this model has a general public on its side as the
customers are prepared to share their data if it improves the safety features or makes
services more convenient and less expensive (Balasubramanian, et al., 2016).

37
2.6.6. Pay as you go
Pay as you go model is an attractive insurance option for the low mileage drivers, who see
financial savings in an option to pay a low base insurance rate and an additional pay-per-
mile cost (Parker, 2018). The daily vehicle mileage is securely shared with the insurers and
the costs are calculated. The model leaves a room for abuse if the insurers collect data
that is not necessary and perform analysis to “punish” drivers for their driving style.
2.7. Business models and data marketplace
Data is at the core of IoT. A trend is to make such data available through marketplaces. A
dictionary defines market or marketplace as “an open place or a covered building where
buyers and sellers convene for the sale of goods” (Dictionary.com, 2019). The crucial word
in that definition is ‘goods’ and if replaced with ‘assets’ and representative data is
considered as one of those, then one can resort to the external data in order to maintain
a competitive edge (e.g. AI training) (Tang, et al., 2018). Given the rise in usage of
connected devices, IoT data represents an opportunity not only in terms of own data
exploitation but also through the treatment of the data as a tradable commodity.
An immediate question related to vehicle generated data is concerned with its openness
and what form of controls are in place. The privacy issues might be resolved by GDPR
(section 2.4.2) through the development of common rules, policies and forms of
cooperation. However, vehicle data are not shared (yet). The driving data is growing an
unused gold mine. Should it become accessible, there is a huge potential for the
community to develop new applications and open revenue streams. The potential of
data-driven models is clear, but the challenge is in the technical field and in terms of
privacy.
The open access data would remove the power from the data-hoarding organisations and
create a level playing field that has the potential to let the smaller players flourish. In such
circumstance, it is the power of ideas and their execution that would prevail in terms of

38
value creation. The closest analogy in the technical world is that of the open source
software development versus proprietary software.
The marketplace also highlights the need to share and to define the rules of data usage
and exchange aimed to support a win-win situation through value creation and
maximisation of benefits through increased usage. The data overlap between unaligned
sectors is a reason to expect the unexpected. An open data marketplace offers an
opportunity to all to participate in this new form of economy. It benefits from the
tremendous potential for cross-fertilisation resulting from:
• the maturity gap among organisations in terms of data management and analysis
• inability to envisage causal correlations, as there is a lack of domain contact.
The number of relevant automotive contributors to the marketplace is huge. Some of the
most prominent ones are categorised by the application type and listed in Table 2.
Table 2. Significant Automotive-related Contributing Applications to Data Marketplace
Automotive data
marketplace
contributors
Potential exploitation / improvements / value creation / benefits
resulting contributions to data marketplace
Connected
infrastructure
AD, driving patterns, air quality, traffic control modelling, reduced
congestions, improved emergency response time, efficient parking
Energy
stabilised energy supply, reduced cost of energy distribution
EV charging
improved utilisation of renewable energy, low congestion
Autonomous driving
HMI improvements, health/wellbeing, improved road safety, sensor
development, CPS, increased productivity, improved road capacity,
lower fuel consumption, reduced travel times
Digital manufacturing
security, CPS, semiconductor manufacturing, streamlined supply
chains, logistics, warehousing, inventories
Connected testbed
security, safety,
low
emissions, instrumentation, training/education
Connected supply
chain
inventories, improved storage conditions, battery lifetime and
transport at ideal conditions
After
-
sales market
customer service, understanding of human
perception, HMI
Predictive
maintenance
instrumentation development, training/education, semiconductor
manufacturing
Insurance
targeted advertising
, road safety
Smart city
efficient utilisation or resources, improved quality of life, air
pollution control, traffic congestion
Smart home
EV charging, HMI, human perception, semiconductors
/
sensor
development
Wearables
health / wellbeing, HMI, CPS

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At a vehicle specific level, data falls into different macro-categories, as depicted in Figure
6 (Balasubramanian, et al., 2016). In terms of localities within the automotive ecosystem,
data can be further categorised as belonging to in-vehicle technologies, connected
infrastructures and back-end processes (Balasubramanian, et al., 2016). Such a
categorisation is relevant in terms of ownership and rights for data usage, which is an
integral question for the development of the data marketplace.
Figure 6. General Vehicular Data Categories
2.8. Business models in a nutshell
Connected driving solutions rely on data collection, processing and usage. The data
access is the key question for privacy, security and safety, which partially contradicts the
crucial component of business development i.e. data monetisation. The level of data
access defines the implementation strategy. If data is a freely available re-publishable
source, with no legal deprecations, then main gains are to be drawn from the mining of
conclusions that are hidden within the Big Data. That is crucial, as the focus of
organisations is shifting from manufacturing towards services (e.g. MaaS). Hence, data
processing algorithms are the ultimate benefits bearer, monetary or not.

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A key success factor of MaaS is an end-to-end service as the segmentation causes low
user acceptance. Hence, the ownership issue must remain in the background. The
ownership is likely to be a mixture of public and private partnerships and it requires deep
engagement due to ranging aims and interests. An additional level of complexity comes
from the mixture of multimodal transport and subscription models. The geographic
limitations will also draw the question of roaming options.
A review of the business models reveals the prevalence of one-to-many services and
these are predominantly customer-service orientated. However, there is a drive to exploit
current market knowledge and edge in the direction of open data sharing. The attraction
comes from the high-quality data that potentially hides the secrets of new value to be
captured through the advancement of new services. The level of ‘openness’ and the
amount of controllability is yet to be determined. There is no clear winning IoT business
model of the future, but the most relevant ones fall into four groups:
• ‘Anything’ as a service (mobility, data, etc.)
• Multi-sided market
• Partnerships / barter / reciprocity
• Freemium
Many (large and industrialised) cities appear to be the driving stakeholders as they could
potentially experience huge benefits from data sharing (Zembacher, 2019). The cities
could become considerably more efficient in maintaining themselves at reduced costs.
In conclusion, a business model with a slight advantage over others at the moment is the
one that is responsible for data storage. If one considers the data as an asset that could
be potentially the source of the next gold rush, then the platforms that store that data
are the gold mines that are yet to be exploited. However, when it comes to maximisation
of benefits and value creation, it is not enough to mine the data, but it also has to be
properly analysed and the conclusions from these analyses must be acted upon.

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3. Analysis
This chapter analyses connected applications and related business models. Some basic
questions are accompanied with benchmarking of past events and with Porter’s five
forces analysis. The consolidated results are processed and evaluated in chapter 4.
3.1. Five Basic Questions
The five basic questions are gauging the ecosystem’s prospects and how the proposed
solutions relate to the general trends from the viewpoint of the research question.
3.1.1. User Demands
User expectations and demands are evaluated (Table 8 of Appendix A). Key challenges for
the connected vehicles are the trustworthiness and resistance to the new technology. As
stated in section 1.2, change lies within the core of digitalisation. However, change often
faces ubiquitous resistance (section 4.4). Hence, demands that support trustworthiness,
help conquer the resistance. These include, but are not limited to:
• Transparent and realistic added value
• Provision of security and guaranteed privacy protection
• Improved safety (not only AD-related)
• Improved sustainability, inclusive of low environmental impact
• Standardised solutions (especially in terms of infrastructure)
• Convenience
• Choice
• Efficiency etc.
3.1.2. New solutions
User demands from section 3.1.1 call for technical solutions. The most relevant ones are
presented in section 2.1.1. At a specific level, the most talked about and most convincing
technical applications with the potential to contribute to benefit maximisation are:

42
• Increased utilisation of sensors (aiding data collection)
• Connectivity (IoT, M2M, 5G, etc.) / internet expansion into the physical devices,
• CPS as a crucial factor in connecting the physical and digital worlds, but also as a
driving force behind the utilisation of assets (especially infrastructure)
• Data analytics (application to the new challenges)
• The amalgamation of automation and data exchange
• Convenient and intuitive applications (e.g. mobile Apps)
• Modular production that aids customisation and distributed manufacturing
• HMI, Industry 4.0, AD, Edge computing, cloud services etc.
A review of developments is based on the IoT ecosystem (Figure 4), which interlinks
technical aspects into a circle that delivers sustainable value. That value extraction is also
propped by activities that maximise the usage of technical solutions, e.g.:
• Business model innovation to effectively deliver new services
• Openness towards technical changes (from the development side) and their
application to future business (on the implementation side)
• An integrated approach to vehicle development, so that vehicles no longer
symbolise transport devices, but integrated systems
• Access to external data, in combination with the own sources, as needed for the
move from manufacturing towards service provision.
Connectivity’s crucial outcomes are efficiency improvements and eased operations
management, and hence improved services. The new IoT data enhance insights into user
behaviour and expectations, hence contributing to the planning of future services. This
additional feedback loop is what aids the connectivity when responding to user demands.
3.1.3. Trends
As discussed in chapter 1, digitalisation is a major business revolution and a mega-trend
that is reshaping the future of the automotive industry. Equally, connected driving is
identified as a major technological trend (Figure 2). Further technical progress (Veledar,
et al., In Review) is depicted in Figure 7.

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Figure 7. Major Trends in the Automotive Domain
The major automotive trends are digitalisation, industry 4.0, connectivity, MaaS,
connected vehicles, AD, sensor developments, customisation, edge computing, cloud
services, Big Data, 5G, M2M and electrification. These trends are further complemented
by the urbanisation, which leads to higher pressure on reduction of vehicle ownership
and energy transition, which is providing an additional pull towards the removal of silos
between mobility options, energy management and data marketplace.
3.1.4. Improving market position
The potential improvements in the current market position depend on the perspective.
Considering the simplified representation of the future automotive supply chain in Figure
8 (modified from (Schagerl, 2019)), there are few immediate arising questions:
• Who will fill the void in the supply chain to create sustainable options for mobility
services?
• Should OEMs consider such an option, as they have the power and skills for it?
• What is the future role of engineering solution providers? (Schagerl, 2019)
• Is it in the interest of solution providers to support the digital transformation?
• What will be the relationship between the MaaS providers and OEMs?

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Figure 8. Simplified Automotive Supply Chain
Crucially, when aiming to strengthen market position, then the primary question is that of
a perspective i.e. who is the party whose position is to be strengthened? The following is
a list of identified measures that ought to contribute to the attractiveness of the offered
assets and should improve the market position of stakeholders, in general:
• Data – should be analysed, conclusions should be made together with a detailed
plan of actions and these must be implemented; data utilisation is to:
o provide knowledge and to aid quality
o rely on improved sensors to enhance information and improve the
potential for the understanding of user needs
o provide direct and continual user feedback
o contribute to the improvement of customer relations
o establish a sustainable relationship with customers
• Change – must be embraced, especially when considering:
o digitalisation/business transformation
o implementation of new business models – especially MaaS
o breaking of silos between different domains
o satisfy the changing customers

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o provision of business models that disregard vehicle ownership
• Connectivity should not be used to invent the next new big thing but should be a
basis for the new business models, so to improve the existing assets, making them
ideally more attractive to the end user
• User experience is to be enhanced through:
o usage of infotainment
o improvements in human perception (especially for AD)
o implementation of efficient and intuitive HMI
o new solutions around convenience for the end user
o developing solutions with “fun factor” in mind to entice usage
• Individualism is to be supported through design that enables modular and
customisable assets
• Efficiency is to be supported through
o AD (more effective use of time)
o new processes for Industry 4.0
• Trustworthiness contributes to increasing acceptance by:
o Improved security, safety and privacy related issues
o Reduction of downtimes
o Just in time maintenance services
• Lifecycle management improves quality
This is an assortment of key universal contributors that should be considered when
developing new business models to secure new value streams.
3.1.5. Cost position
The cost position links advances in productivity, investment intensity, processes, the
complexity of users and products. The following are key factors for cost improvements:
• data must be monetised, through either trading or analysis
• processing algorithms must deliver applicable results/decisions, some of which
should reduce redundancies and minimise wastage

46
• Industry 4.0 is to deliver efficiency improvements and streamlined supply chains,
which should be reflected in lowered production costs
• cross-fertilisation and combined solutions deliver multifaceted assets
• unification of services creates simpler and transparent cost schemes
• the compartmentalisation of manufacturing processes contributes to utilisation of
specialised centres and hence better quality and efficiency
• MaaS applications aid efficiency improvements and cost reduction, which reduces
vehicle ownership; the critical mass of users aids economies of scale
• long-term sustainable contracts are to be favoured over the one-time sales as they
contribute to greater long-term satisfaction and improved monetisation potential
3.2. Cookbook analysis
There is a certain level of common understanding, compiled into a cookbook for
connected mobility in very broad terms. The identified pattern consists of four stages
(Elloumi, De Block, & Samovich, 2019):
• selection of an optimal infrastructure i.e. acquiring appropriate technical
solutions and utilising existing IoT platforms to facilitate testing of the solution
using simple and predictable use cases
• integrating internally available data and if feasible, then the acquisition of
external data to complement the existing resources, while aiming to capture
additional value beyond the initially anticipated scope
• proactive integration of external data and extensive usage of analytics to
generate better-informed decisions
• full marketplace participation, which enables leveraging on the data from
multiple domains and sources.
3.3. Benchmarking past promising technologies
A broad benchmarking analysis of some past successes and failures is presented in
Appendix B. The main identified success mechanisms connectivity are:

47
• There is a need for openness
• Convenience
• Modularity
• Customisation
• Innovations
• (Early) stakeholder engagement
• Response to user feedback
• Assemble fast to learn from mistakes
• Use lessons learned to improve quality
• Investments into long-term customer relations
• Focus on narrow, rather than a general field of application
• Discussion and ideation are likely to contribute to improved implementations
3.4. Porter's 5 forces
The intense competition in an industry is neither coincidence nor bad luck (Porter, How
Competitive Forces Shape Strategy, 1979). The improvements in market position are
highly associated with positioning within the supply chain (section 3.1.4). An imminent
consequence is presented in the blurry definition of the relationship between different
stakeholders i.e. who is competing with whom and who is sharing common interests?
Hence, neglecting these complex associations and focusing on defined forces, helps
examine the behaviour of a whole industry and aids the creation of a strategy for further
operations. In this case, the intention is not to examine the wellbeing of either whole
automotive sector or the complete connectivity ecosystem. The underlying intent is to
provide an insight into the combination of the two and how their interactions could
potentially evolve.
3.4.1. Jockeying for the position (high)
There is strong competition in automotive and communication domains. The two form
the core of the connected mobility. The situation is similar for other providers from the

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IoT ecosystem (Figure 3). Sensor development is largely a part of the very competitive
semiconductor industry, while the data platforms and the application providing services
are relatively new phenomena that gather a huge amount of competition. Their
environment is very dynamic and a lot more open for sharing ideas and cooperation than
it is a case for the traditional industries. That is a result of the generational divide. Now,
the competition is not only based on price and quality, as much as on the key features,
ideas and general perception. That leads to a conclusion that many stakeholders are still
in the game of generating a critical mass of “followers” in hope that either the platform
effect or the quality of their solutions is going to be their differentiating factor.
In the midst of intense competition, there is a slow but progressive movement of ideas,
which is helping the shift from traditional business models towards the new ones, such as
MaaS. It is unimaginable that this progress could be stopped, so either it continues at the
current pace, or a major player in the field could cause a step change and shake up the
market. Either way, spearheading the progress while observing the actions of others,
should prepare any organisation for a business transformation.
From the perspective of fixed costs, in many cases the business development can keep
these relatively low, considering that new developments are not looking for a new idea,
but for ways of improving the existing products and services. The reliance on the existing
assets, which are already in use, helps keep the fixed costs low.
Differentiation could become an issue, as many players in the field are seeking similar
solutions. However, if the idea of the distributed network effect and the open data
prevail, then the created benefits would be shared across the board.
The intermittent capacity increase should not represent a challenge to the traditional
manufacturers. However, that could be an issue for smaller and emerging contributors.
The stakeholders that are basing their operations on digital assets can relatively easily
adapt to the situation. However, smaller stakeholders have no ability to contribute to
large infrastructural developments. They lean on sound relationships and sustainable
contracts with corporations that are able to absorb transient changes.

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The smaller stakeholders that are interested in business development based on the digital
application have very low barriers to exit. Conversion of their experiences into future
ventures could be relatively straightforward, which is not the case for hardware
orientated manufacturers. Changes applied to processes in the manufacturing of either
semiconductors or vehicles are financially cumbersome. This could be also one of the key
reasons for the industry not to be creating a step change, but moving at a slow and steady
pace. Hence, one would expect that a stakeholder that provides good quality and
sustainable solutions would not need to consider exiting the ecosystem. The cost of exit is
dependent on position within supply chains.
Considering the rivalry of the existing stakeholders, the following are the main points that
should be considered:
• Competition should be idea-based, i.e. benefits are sourced from the open sharing
and gathering of input from all the interested parties
• Minimum viable product (MVP) should be used for early engagement
• Acting through interest groups that spearhead the digitalisation is an apt
groundwork for changes in the business environment, especially if open and
distributed data marketplace prevail as main solutions
• Rather than wasting resources onto the next big thing, organisations should
enhance existing products and services
• Maintain strategic relationships and prepare contracts with stakeholders that
could provide infrastructure
3.4.2. The threat of new entrants (high)
The new entrants are everywhere. However, there is some differentiation. As described in
terms of barriers to exit in section 3.4.1, the situation is also similar for the barriers of
entry. It ranges from very low barriers for the dynamic start-ups with a low need for
investments into either manufacturing or infrastructure, to large barriers on the other
end of the spectrum for the device providers. An analysis results with the following:

50
• Economies of scale are difficult to reach due to the lack of a critical mass attached
to a single solution. Some organisations (e.g. Uber) exploit the platform effect. It is
to be seen if they resist competition and survive the potential rise of distributed
network effect that could eliminate their existing competitive advantage
• Product differentiation is weak (section 3.4.1)
• Brand identification is difficult to achieve, but with persistent marketing strategies
(e.g. Tesla), a sustainable presence is achievable
• Access to distribution channels is simple if the models rely on digital deliveries
• The latest technology is accessible, but the implementation is a key differentiator
• Experience and learning effects are crucial for remaining at the forefront of the
novel business applications, which demand feedback and knowledge transfer
Regulations and standardisations are somewhat puzzling. There are two sides of the
argument: one which declares that standardisation prevents creative progress and the
other one which desires clarity defined by standardisation. From development’s point of
view, standardisation is desired as it simplifies some solutions, but it should be performed
at a global level, or else functionalities become geography-dependent (Schagerl, 2019).
The greatest threat posed by any governmental action is tightly related to the frequency
allocation and certain technologies, such as 5G for the reasons defined by either finances
and power (Woyke, 2018), politics and protectionist trade wars (The Daily Show with
Trevor Noah, 2019), health (Meyerowitz-Katz, 2019), or any other motive, which risks the
technology split (FT: The Editorial Board, 2019).
In summary, the following points constitute adequate responses to new entrants:
• Collaborations are a base for cost advantages through the economy of scale
• There is a need for brand identification, based on the establishment of a unique
value proposition and support of creative elements
• Strong digital presence enables access to the end users (distribution or relations)
• Maintain an arm’s length distance with the latest technological developments to
be able to respond in the dynamic environment

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• Openness and constructivism should be utilised to contribute towards
improvements of continually evolving solutions
• Standardisation activities on a global level guarantee an equal functional
performance of assets in all relevant locations
• Observe global affairs and be prepared to respond to an eventual (unlikely)
technology split (if necessary)
3.4.3. Bargaining power of buyers (moderate)
Potential buyers are either institutional or individual. The institutional buyers have
relatively high bargaining power due to the high choice of emerging solutions and
possibly low switching costs (subject to application). If the offered solution proves to
positively contribute to the buyers’ profitability over time, then one could exploit this
positive outcome for further integration of services and development of new assets. On
the other hand, the purchasing power of individual buyers is low, as this is the game of
numbers. Individual buyers are contributors to the necessary critical mass, so their
influence is more significant in terms of advertising by word of mouth than in terms of
their loyalty to the service or product. Considering the increasing number of competitors
and low switching, loyalty is unreliable – subject to the service type. Hence, the
recommendations are to:
• Establish a strategic sustainable relationship with institutional buyers to safeguard
stability for all stakeholders and hence maintain the level of trustworthiness
• Maintain good customer relations with the individual buyers and incentivise
advertising through word of mouth.
3.4.4. Bargaining power of suppliers (moderate)
Just like the with the bargaining power, the roles are exchanged and hence the bargaining
power of smaller suppliers is frail due to their abundance. However, that is not the case in
terms of the stronger partners (e.g. OEMs). The argumentation for the switching costs is
relatively similar to that for the buyers (section 3.4.3) and the conclusions are also alike:

52
• Establish a strategic sustainable relationship with suppliers as they greatly affect
the quality of the results and stability must be safeguarded (trustworthiness).
3.4.5. The threat of substitute products/services (high)
Exhibition halls presenting new smart mobility businesses reveal many substitutes. These
can be replaced relatively easily at a low cost. That is to a great extent dependant on the
type of business model and the application. The higher the dependence on infrastructure
and on customised hardware, the higher the switching cost and the lower the number of
substitutes is. Hence, this part of the analysis is inconclusive for the ecosystem as a
whole, but in general terms, one should be aware of developments that occur elsewhere.
3.4.6. Complementary providers (extremely high)
The addition to Porter’s Five Forces is a result of a business symbiosis and the mutual
impact of products and services already in the market (Kenton, 2019). Considering the
complexity of the IoT ecosystem and the lack of ability to fulfil all the roles necessary for
the creation of a complete vertical implementation (section 1.3), the connected mobility
business models must rely on holistic implementations that provide a holistic experience
to the end user. The user should never experience the multitude of components that
complement each other in the background and the offered product or service must be
presented as a single entity.
3.5. Expert Interview Summary
Some concluding remarks from expert interviews of Appendix E are:
•
As described in section 3.1.4, questions are being raised in terms of the shape of the
future automotive supply chain and the roles within. The digital transformation is an
ongoing and unstoppable process. The question is about the willingness of the
stakeholders to evolve together with the industry and also what will be their final
position and their mutual interactions.

53
•
Data marketplace seems to be heading towards being overpowered by the platform
effect. The distributed network effect is possible, but only if strictly regulated and
supported by the standards.
•
Financing of the infrastructure (Smart City, AD, Smart Motorway etc) is a sensitive
topic as considerable investments are required. The answers are not simple, but they
tend to point at the cities being prepared to define their rule of the game and
inclusion of different players through regulations. Different cities/regions/states
should work on standardisation of the rules in cooperation with each other.
•
The keys to success are incentives and trust.
•
Practical deployment and testing of solutions in the field is required to be sure of their
correlation, as the benefits might contradict each other.
•
The question of standardisation is fairly polarising. The debate and the answers range
from “there is no standardisation whatsoever” to “the standardisation is turning into
progress prevention”. The position tends to vary with the type of organisations,
responsibilities, intentions and the maturity of the field of implementation. The shape
of the curve in Figure 9 summarises the general feeling in terms of standardisation.
Figure 9. Standardisation Needs for Data Revolution

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4. Results
The verdicts from chapters 2 and 3 are amassed into the SWOT analysis and the resulting
options are presented using the Ansoff matrix. A high-level proposal is created for two
realistic business models and the evaluated options are put forward as suggestions for
potential implementations. The models are followed by a change and risk management
section.
4.1. SWOT analysis
The SWOT technique is used to condense and organise the strategic findings from this
work. The contributions to the four components are detailed in Table 9 (Appendix C) and
then summarised in Table 3.
4.2. Evaluation of Options
Based on analyses, the results of which are summarised (SWOT) in Table 3, a proposal for
the evaluation of options in the order of perceived importance is presented in the Ansoff
Matrix in Table 4.
4.3. Specific models
Two realistic business models are aligned to this work’s findings. One model is based on
breaking the silos between an EV charging infrastructure, a MaaS within a Smart City and
the Data Marketplace. The other model is orientated towards practical data collection
and exploitation of analytics for a range of data-based improvements.
4.3.1. Business model: EV charging infrastructure
This multi-purpose model is based on the EV charging infrastructure. The profitability of a
pure EV charging model is doubtful unless realised by either an energy distributor or with
sizable financial backing. The model is summed by stakeholder mapping in Table 5.

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Table 3. SWOT Analysis (Short Summary)
STRENGTHS:
Reusing existing technologies and
assets
Strong technical developments
building onto the existing skills and
experience
New solutions (connectivity and
feedback) supporting new models and
value streams
Improved efficiencies and asset
utilisation
Modular and distributed
manufacturing
Sensors – awareness of the
environment
Amalgamation domains/removal of
silos
Development of human resources
Ability to customise
Improved road safety
Sustainable solutions
Transparent added value
Convenience and choice
Ease of implementation and usage
Sense of direction
OPPORTUNITIES:
Focus on mission
Implementation is decisive
Digitalisation aids new business models
Early engagers define the future
Early stakeholder engagement
MVP
Provider unification/end-to-end services
Cross-fertilisation
Streamlined supply chains
Gamification, convenience, customisation,
infotainment, mobility services, safety,
urbanisation, electrification, low downtimes
Emerging economies
Sharing economy
Data marketplace and data monetisation
External data complements understandings
A firm link between data collection, processing and
usage
Trustworthiness leads to sustainability
Lifecycle management
Additional opportunities through constructive
openness
Exploit and improve existing assets
Distributed networks
Brand identification
WEAKNESSES:
Dependence on technology providers
Security, safety, privacy
Uncertainties (5G)
Lack of skilled teams
Low usage (i.e. poor benefit
maximisation)
Standardisation
Customisation not addressed by all
Financing infrastructure
Non-optimised value capture
THREATS:
Fragmentation
Resistance and low acceptance
Security, privacy, trust
5G failure to deliver
Increased competition
Lack of sustainability
Emerging economies
Mutually negative influence of solutions
Poor industrialisation
Inadequate digitalisation
Lack of cohesion (worldwide)
Loss of decision-making skills
Destabilising socio-economic effects
Decreasing vehicle ownership
Inability to adapt
Poor differentiation

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Table 4. Ansoff Matrix - Considered Options
PRODUCTS
Existing
New
MARKETS
Existing
Reuse assets
Streamline supply chains
Shared economy
Data monetisation
Branding
Incentives
Demonstrate
Gamification
Lifecycle management
New technologies
Trustworthiness
Open innovation
Diversified value sources
Stakeholder engagement
Customisation
Flexible and adaptable
Convenience
Easy implementation
End to end service
Develop new skills
One-point installation
New sensors i.e. data
Road safety
Choice
Clear direction
Distributed networks
Standardisation
Differentiation
Unified providers
Cross-fertilisation
New
Outsourcing
HR development
Emerging economies
Partnering
Data marketplace
Strong brand
Customisation
Acquisitions
Strategic alliances
Outsourcing
New business models
Distributed networks
Standardisation
R&D
Table 5. Business Model - EV Charging Infrastructure: Stakeholders
Stakeholder Basic relationship/activities
Power stations Little possible influence/observe general trends
Grid operators
Collect data; optimise charge/discharge cycles through smart applications; gain from
differences in peak/off-peak electricity costs; with growing “relevance” for the grid,
attempt to improve agreements; offer results of data analysis i.e. monetise data;
Small
generators
(renewables)
Establish an agreement, as with the grid operators, but leveraging on a greater
bargaining position; consider developing own/investing into renewable energy
(subject to finances and infrastructure)
Standardisation
bodies
Follow and actively participate to influence other stakeholders with greater
bargaining power
Regulators Follow the rules/influence
(Smart) City Collect data; install charging stations (together or own); monetary agreement linked
to infrastructure financing
Network
provider
Little interaction – secure the best contract subject to own size and usage of
communications
Data platform Reciprocal data sharing/buying and selling; data collection; data analysis; utilisation
of results
OEMs Little bargaining power – attempt to influence standardisation of charging
infrastructure
EV drivers
(private)
Create a financial agreement based on charging optimisation; incentivise with
convenience
MaaS providers Financial agreement as with private EV drivers
Advertisers Offer advertising space at charging stations

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As the energy sector is going through digital transformation, this model has a good
acceptance potential. The effect of data is judged in a sense how far the energy transition
can be facilitated. To aid that facilitation and data sharing, the model relies on
connectivity and CPS.
A positive contribution of the model is seen in cross-domain implementation i.e. MaaS –
Energy - Smart City. As the energy needs cannot be predicted, there is already
decentralisation drive for energy generation and distribution. Communication between
partners and sectors removes some uncertainties, to minimise disturbances. These are
further reduced by optimised charging i.e. vehicles aid the grid stability. That is enabled
by the infrastructure and the charging stations, while the data platform facilitates
smartness.
Financial gains are possible only through high usage. The question is if the targets are
within a reach. So, in essence, there is an attempt to maximise the number of sources
that deliver revenue and to rely on collected data to deliver the real value. The data could
be simple e.g. it could be utilised to optimise the charging cycles of 100 EVs parked across
a small city overnight. This flexibility is crucial when charging and discharging, as the
vehicles (subject to contract) could also be used to support the grid if necessary. The
additional value is possible through data exchange at the data marketplace, which is
instrumental for any data-driven business model. The process can be controlled via a
smartphone app. The type of charging cycle that is chosen by the driver is used to
calculate financial flows.
A business model canvas of Table 6 provides further details describing the presented idea.

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Table 6. Business Model Canvas: EV Charging Infrastructure

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4.3.2. Data based Business Model
A simplified depiction of the business model is presented in Figure 10.
Figure 10. Data-based Business Model: Principle
The driving data is collected and analysed for the purpose of added value creation.
Incentives are offered to the drivers in the form of:
• Gamification: there is a competition challenge based on efficient/green driving
and teams could compete against each other
• Insurance: mileage is collected if the vehicles are privately owned
• Improved driving: style tips are sourced from analysis outcomes
• Time-saving: the optimal route is suggested by the traffic flow management in
Smart Cities
Once there is a sufficient usage, the data is either anonymised and provided at the
marketplace to Smart Cities for control of traffic flow, or millage can be used by the
insurers for calculation of the insurance costs (“pay as you go”). Algorithms also deduce
vehicle calibration improvements and the results are offered to the OEMs, or air quality
monitoring is used by Smart Cities either for traffic flow control aimed at air quality
improvements or as a trigger of a warning signal to citizens with respiratory issues, which
are highly susceptible to air pollution. The relevant Business Model Canvas is provided in
Table 7.

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Table 7. Business Model Canvas: Data Analytics Model

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4.4. Change and risk management
A challenge faced by many technology-related entities is based either on pure survival
amidst digitalisation and sinking barriers to entry or on the very closely related seizure of
opportunities posed by the very same digitalisation. Either of the two comes hand in hand
with the change (section 1.2) and expected cultural transformation. As these are not
incremental adjustments, but radical transformations, Kotter’s Accelerate approach is a
suitable method to follow when going through the organisational changes of such kind i.e.
The Big Idea (Kotter Inc, 2019) (Kotter, 2012). In order to succeed, the institutional
changes must transform new ideas into assets, rather than treat them as a danger to the
organisation (Mazzucato, 2013).
Change management is not concerned with targeting ourselves but society in general.
The reason for that is that the digitalisation task is such that no entity is capable of
conducting the change on its own, as the task is too complex in nature. It is also not only
about developing and deploying the infrastructure and the related products, but about
increasing the acceptance and usage, as it is only through increased usage, that real
benefits can be achieved. It is difficult to see how a single vertical provider could
influence usage increase on its own. Even if considering organisations that have re-
shaped the world in the past through introduction of new products (e.g. Apple), one must
note that it is their leadership that was essential for the change, but they did not and
have not and probably will have not succeeded on their own.
As the two business models (section 4.3) are generic and could be implemented by a
range of organisation, the focus is placed on the technological changes. These are suitable
for experimentation, continual interactions with the customers and inclusion of feedback
into further development. Hence, these changes could be performed using agile methods
and should leverage on the advantages of MVP. When implementing changes of that
kind, the key is in showing up and demonstrating successes (Kanter, 2013).
Change also convey risks. These are amplified by complex combinations of new business
developments and radical new technologies. The (obvious) risks are posed by the level of
infrastructural financing and the risk of failing new technologies, especially 5G, which has

62
no alternative and is finding itself to be a subject of a current trade war. The other major
risks are seen in the lack of trained personnel and lack of agreement across the board,
that could be eased through better standardisation. Searching for the skilled human
resources in this area often feels like looking for unicorns, while the standardisation is
simply the matter of resolve to agree. Once again, the change is not so welcome and this
lack of standardisation could potentially be a sign of resistance; the indecisiveness caused
by the fear of mistakes.
4.5. Expected Benefits
A general list of expected benefits to be provided by the connectivity in the automotive
industry is presented in Table 10 (Appendix D). In general terms, the main and the most
common benefits are seen in satisfaction for the full range of stakeholders, especially the
end users. That, of course, comes at a cost.
In summary, digitalised businesses improve interactions and through the usage of data-
based smarter services, products and processes overcome the resistance to change. The
resulting improvements in acceptance deliver benefits, such as improved efficiency,
enhanced life quality, revenue improvements (reduced costs and increased profits),
reduced environmental impact, safer roads, secure communication, privacy prevention,
convenience, choice etc. The type and the range of benefits are such that the
stakeholders of all kinds are able to feel sustainable impact resulting from business
development.

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5. Discussion and Recommendations
Considering all the “after you” effects, the early movers are to benefit from automotive
connectivity despite all the risks. That is already becoming evident through the fact that
successful business models are the ones that are closely related to the data platforms.
Further extensions within the ecosystem are to deliver benefits, for the users, but also for
the providers of these additional solutions. The current state of the affairs is such that
early engagers are more likely to be followed than contradicted, as very few are prepared
to publicly present a direction that should be followed. This chapter sums up the
presented work within the bounds of the research question and it also encompasses
certain suitable recommendations.
5.1. Summary
Digitalisation, as a process of the digital transformation of a business or industry, is
indebted to connectivity and the resulting data. The benefits, mostly seen in efficiency
and quality improvements, are only possible if propped by progressive technical
developments. As the technology is maturing, so is the resistance to digitalisation losing
its strength and the businesses are edging towards the inevitable change. That change is
brought by the external factors, such as societal expectations and market pull, but also by
the (internal) technology push. The expectations and intentions are, unfortunately, not
always fully matched with the investments (time, finances and effort). However, as the
increasing usage is maximising the benefits, it is expected that more executives will
embrace the foreseeable industrial evolution, that is acting as an enabler of the business
revolution. The resulting strategic reorientation is expected to embrace the connectivity
and the future of the automotive industry is unlikely to ever look back, as the reinvented
methods to create and capture value are projected to sustain equilibrium between
financial benefits and the societal impact.
Automotive connectivity is one response to the changes in consumer behaviour in terms
of mobility, as it drives technical developments (autonomous driving) and a societal

64
reorientation away from vehicle ownership. The success will depend on overcoming
barriers, such as security and privacy concerns, lack of geographic uniformity in terms of
regulation, technical difficulties and accountability for financing necessary infrastructure.
However, as the studies predict significant returns on investment, one may assume that
the deployment efforts are likely to accelerate the progress of new business models and
to enhance strategies for their implementation.
While there is a myriad of technical and business model combinations that could be
grouped by a plethora of providers and supporting stakeholders, it must be made
adamantly clear that whichever product or services are presented to the end users, they
must be offered in a unified form. The simplicity is a key incentive, together with the
convenience and the possibility to select for oneself. The solutions that lie in the
background are likely to be based on reuse of the existing assets, monetisation of data,
shared economy and a general search for efficiency improvements that contribute to
progress in terms of life quality and personal satisfaction.
5.2. Challenges
There is a range of challenges that are discussed in this work. In general, they could be
grouped into technical, implementation, regulatory, societal and financial. Some of the
major challenges are described below.
The main technical challenge is related to 5G developments. As the automotive
connectivity is based on the flow of huge amounts of data, the attractive component of
new communication technology is sourced from the promise of large data rates (be it 5G
or some other technology). The other technical glitches are possible in the development
of solutions that deal with security, safety, privacy, maintainability etc.
Compartmentalised development, aimed at solving such challenges, could result with
technology fragmentation.
The technical fragmentation links to the next set of challenges i.e. the ones that are
related to implementation. A success of potential solutions may not be measured by their

65
technical perfection, but by the ability to be scaled-up and accepted. While the
organisations are providing technical results, they require infrastructure before any usage
increase can take place. The questions remain over the form of the data infrastructure i.e.
are we likely to witness another case of platform effect, where the winner takes it all or is
it realistic to expect the emergence of decentralised and networked platforms?
An answer to the platform versus network dilemma might come from the regulatory and
standardisation bodies. Unfortunately, amidst the lack of clarity related to the shape of
future technologies, these bodies are somewhat dragging their feet and are not too swift
in terms of making definite decisions. It is not even clear yet if the data is going to be
open (with some form of access rights regulation) or if it is destined to be under stricter
regulations. The market barrier caused by the lack of clarity is restricting data flow
between different stakeholders and that is a challenge that requires immediate attention.
It is difficult to solve the enigma of how to motivate mobility operators to share data and
their services. Regulation could act as one possible solution, but as there is a move from
private to public services and ownership, another motivator is seen in the increase of
customer numbers resulting from data sharing.
The main societal challenge comes in the form of acceptance of new assets and might
require a certain amount of incentives.
While technology and its application are the main building blocks needed for the creation
of conditions for maximisation of benefits, there is also a need to consider infrastructure.
That challenge requires financing. In terms of Smart City applications, it is likely that the
cities themselves are taking the main regulatory and some financing role. However, the
question remains open at a wider geographic level.
5.3. Recommendations
Results and the evaluation of chapter 4 inform about detailed approaches to the relevant
business development. The following is a set of accompanying general recommendations,
that could be expanded further, but also be open to debate.

66
• Focus: The questions to ask are: “what is the mission that is to be achieved?” , “is that
mission good enough for a convincing business case?” and "what value does it
create?". If the answers are conclusive and promising, then one should commit to the
mission and focus on application, as diverse solutions with no clear intent are unlikely
to be successful.
• Demonstrate: There is one simple act of transgression that can rob the status quo of
its power and that is the demonstration. Demonstrations are the best inhibitors of
resistance to change. Hence, demonstrations should be exploited for an increase in
acceptance and for the generation of new ideas through feedback.
• Be smart: IoT is not about finding the next new big thing. It is about improving existing
ideas and doing old things in a smarter way.
• Collaborate: The automotive IoT challenges are too big to be solved on one's own.
Open research and collaborations are highly recommended and should be treated not
as outsourcing, but as capacity development. These should then result in shared data
schemes.
• Apply: The best incentive for data sharing are safety improvements, convenience and
cost lowering. The key is not in data collection, but in its usage.
• Learn: The data contains invaluable feedback and one should use the digitalisation to
learn and be educated.
• Engage: Early-stage impact mapping and stakeholder engagement are crucial for
success and formation of future assets through feedback.
• Make it be modular and flexible: Connectivity should not be focused on the provision
of traditional vehicle functionalities but also on additional services.
• Be sustainable:
a. Simplicity: Solutions must be simple, transparent, intuitive, interoperable,
instant, scalable, transactive, secure and flexible. Services should offer one-
stop end-to-end solutions, so that the end user does not have to switch
between the suppliers and service providers, no matter what occurs in the
background.

67
b. Trust:
There must be a choice so that the end users can select products and
services that fit their needs. If adequately accompanied by incentives, the
choice is likely to be a bearer of trust. Trust is often not related to technology
but is centred around sustainable relationships.
c. Neutrality: Business models that desire to remain “neutral” could choose not
to provide any physical services. They should refrain from making exclusive
commercial deals. The long-term benefits win over the short-term advantages
of exclusive partnerships.
• Invest: If a return on investment is expected, the approach to the deployment of
business models related to automotive connectivity should be coordinated and
accelerated.
• Offer data: Data should be catalogued if it is to provide value. Such data can be
discovered (by third parties as well) and understood.
• Market it: It is possible to have the best product, but if it is not marketable, it means
nothing. The marketing must be aligned with the strategy.
• Share: The exploitation of the sharing economy offers farthest and faster market
penetration with fewer products.
• Manage the risk: Rather than shying away from the risks, embrace the uncertainty
and innovate.

68
6. Conclusion
IoT is a technology that happens to find itself in the unconventional territory of being
totally entangled together with a business revolution. The resulting expectations fluctuate
and are subject to the direction that they are coming from. A truthful view is that IoT is
not about finding the next new big thing, but about finding a new way of doing the old
things in a better and a more efficient way and capturing the value as a result of the
performed activities. It is not about working hard, but about finding a smarter way to
generate the same or even better results. The core of that initiative is the data. However,
just collecting the data is not enough. One must leverage this valuable asset to improve
operations, processes, products, services, relationships etc. One must also change from
within and learn that the traditional business models are no longer a sustainable option in
the ever so more digitalised world.
Business models aim to rationalise value creation. However, there is a lack of tangible
methods of capturing that value by the organisations that rely on IoT as a tool of staying
in step with the unavoidable digital transformation. Hence, there is an explicit need for an
update of the traditional business models to support core businesses, or else
organisations must face an evolutionary extinction. The evolution must be supported with
strategic measures. It is the implementation of those measures that is crucial. The
creation of impact seeks passion, conviction and (smart) dedication.
This work explores a range of potential business models that are appropriate for the
exploitation of IoT in the automotive world. There is no such thing as the best business
model. The most promising one is the one that fits the situation and the needs of the
organisation. Whichever business model is selected, it can deliver promised benefits only
if it is supported by the open and constructive collaboration in the new era in which the
connectivity and sharing are a norm.

69
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Improvement of Autonomous Driving. 26th EuroSPI Conference. Edinburgh: EuroAsiaSPI.
Veledar, O., Macher, G., Damjanovic-Behrendt, V., Jaksic, S., Thomos, C., Schmittner, C., . . .
Drobics, M. (In Review). Safety and Security of IoT-based Solutions for Autonomous
Driving: Architectural Perspective. IMBSA 2019: 6th International Symposium on Model-
Based Safety and Assessment. Thessaloniki.
Wang, L., Törngren, M., & Onori, M. (2015). Current status and advancement of cyber-physical
systems in manufacturing. Journal of Manufacturing Systems, 517-527.
doi:10.1016/j.jmsy.2015.04.008
Wikipedia. (2019, Jun 21). Starlite. Retrieved from Wikipedia:
https://en.wikipedia.org/wiki/Starlite

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Wikiquote. (2019). Henry Ford. Retrieved from Wikiquote:
https://en.wikiquote.org/wiki/Henry_Ford
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means/
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Veledar, Interviewer)
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is-it-really-mobilitys-great-hope/573841/

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A. Appendix: Potential Customer Demands
Table 8. Five Basic Questions - Potential Customer Demands
Application
Industry 4.0
efficient supply chains and operations, revenue growth, reduced production
costs, quality (products and services), reliability, integrity, maintainability,
relevance, modular production, customisation, low environmental impact,
no downtimes
production side
increased revenues, modular production, simplified design
user side
comfort, infotainment, convenience, low operation cost, eased operations,
low environmental impact, reduction of road accidents, no fatalities,
simplicity, life quality
Fleet
management
reduced fuel costs, efficient asset management (including human resources),
reduction of road accidents, no road fatalities, no downtimes
EV
stable power supply, predictable energy supply, reduced maintenance cost,
low environmental impact, convenience, performance, charging speed,
battery lifetime, standardised infrastructure, esteem, financial
incentivisation
Platooning reduced fuel cost, efficient traffic flow, low congestion, reduction of road
accidents
low operating cost, efficient management of assets (including human
resources), maximised utilisation, reduction of road accidents, the
unification of services
reduced fuel cost, convenience, reduction of road accidents, simplicity,
choice customisation, infotainment, additional services, adequate service,
life quality improvement, efficient service, unified applications, low cost, no
need for vehicle ownership, incentivisation, pay as you go
Data
Marketplace
Demand
everyone
AD
Potential customer
OEMs, logistics,
everyone
cities, MaaS operators
everyone
all of the above … and more: opportunities to learn, reuse, rediscover, cross-fertilise, simplicity
in terms of cooperation and data exchange
MaaS
public
added value
security
safety
availability
adaptability
privacy
trustworthiness
reliability
sustainability
standardisation
inituitive HMI
low-
environmental-
impact
MaaS operators,
logistics, emergency
power generation and
grid operators, cities, EV
vehicle owners/users
logistics, motorway
operators

77
B. Appendix: Benchmarking Analysis
The fire was a success. As a species, we benefited from its culinary usage, which
guaranteed the energy needed for the evolution of the human brain. It just happened
that fire was ‘open source’. Similarly, the greatest invention of all times, the humble
wheel was easy to replicate, so that it could generate an immeasurable impact till the
present day. The needle, the second greatest human invention, without which cold
Europe would have never been conquered, was also easy to copy. These are ancient
discoveries and it is simple to claim that changes on that scale are not possible in modern
times. However, the Internet, which is one of the greatest inventions of modern times
also happens to be open to all. The theme of openness is what creates a fast and
sustainable impact on a large scale.
Another key factor for uptake and increased usage (which is what ultimately brings the
benefits) is convenience. Smartphones revolutionised society and impacted almost every
aspect of human life extremely fast. A major contributing factor for this change was that
there was no need for incentivisation for usage. The uptake was sudden, as these devices
made it noticeably easier to perform mundane tasks that were already part of our lives.
A flashback into childhoods recalls the memory of LEGO’s modularity. The manufacturing
process of LEGO toys represents modularity at its best. LEGO does not have an individual
production line per product and is basing its production on standardised building blocks,
which are then gathered into different packaging options. The resulting individual
products are fully customisable. It is this modularity that contributes to customisation
and the success of LEGO that has lasted for generations. The modularity is a key for
leveraging onto benefits of the economy of scale and is also an enabler of
customisation. Hence, incorporation of modularity is expected to reduce the operating
costs, while improving the user experience through the provision of customisation.
The Microsoft versus Apple saga will continue for years to come. This well-documented,
rivalry carries lessons of a dual kind. A careful examination of the cookbook recipe from
section 3.2 combined with some level of “reading between the lines” would result with a

78
suggestion to implement automotive connectivity using the standard principle of retailing
industry i.e. “cut your losses quickly”. In this case, the idea is not to abandon the
investment, but to learn from each new experience as IoT is largely an uncharted
territory (technology and business wise). Therefore, each implementation carries a
lesson to learn. Going back to the case of Microsoft, which enabled amalgamation of
components from different providers in order to assemble a functioning PC very fast, it
was possible to deliver rapid progress. Hence, Apple was on the losing side in the 1990s.
However, focus on innovations resulting from user feedback and improving quality from
the custom-built system brought exponential value to Apple at the later stages. It is also
a tight relationship with the customers that are guaranteeing sustainability.
On the other hand, when considering the past strategic marketing failures, such as
Segway, Google Glass or Amazon Fire Phone (Hartung, 2015), it takes us back to the
basics of project management theory (Figure 1), which advocates that with no usage,
there are no benefits. These products failed because they lacked the focus and were too
general in terms of application. That is also what is happening with IoT right now. As a
technology, it is spread across the boards. However, it is up to the business developers
and the solution providers to narrow the field towards individual applications. Once the
benefits are obvious, the usage is likely to increase and spread into other fields.
The master of technological failures is Starlite (Wikipedia, 2019). It is a material of
incredible and unbelievable properties that never got to the mass-manufacturing stage
simply due to the lack of trust. In many cases, it is not the ideas that win, but their
implementation. Hence, it is understandable that there is some level of closeness in
terms of confidentiality. However, through the sharing of the ideas, solutions become
better, as differences tend to work well together.
It is clear from the above that one may not lose the focus i.e. one should ask the question
“what business issue needs to be solved?” and one should then concentrate on solving
that issue rather than floating in many directions.
The summary of the benchmarking analysis is presented in section 3.3

79
C. Appendix: Detailed SWOT Analysis
The SWOT deduction process is presented in Figure 11 and the detailed combination of
components is shown in Table 9.
Figure 11. Detailed SWOT Matrix
Table 9. Detailed SWOT Matrix
Strengths
Technology
Technology developments, targeting
automotive applications (TR, 5BQ)
Connectivity supports existing automotive business models (BR)
Experience in data analytics (TR, 5BQ, CB, P5)
Experience in AI techniques (TR)
Efficiency improvements (5BQ)
Increased sensor utilisation
(aiding data collection) (5BQ)
Connectivity (IoT, M2M, 5G, etc.) (5BQ)
CPS bridging the gap between the physical and digital worlds (5BQ)
The amalgamation of automation and data exchange (5BQ)
Distributed manufacturing (5BQ)
(Re)use
Number of
wearable devices in use (BR)
Building onto the existing products through reuse (TR, BM, P5)
Reusing knowledge related to the existing HR (TR, BM, P5)
Maintenance knowledge reused while enhanced by the new data (TR)
Vehicle engineering
experience to be reused and strengthened (TR)
User-orientated
Modular development aids required customisation (BR, 5BQ, BM)
Improved safety (not only AD
-
related) (5BQ)
Improved sustainability, inclusive of low environmental impact (5BQ)
Provision of transparent and realistic added value (5BQ)
Increased convenience and the offer of choice (5BQ, BM)
Convenient and intuitive applications (e.g. mobile Apps) (5BQ, BM)
Modular production that aids customisation (5BQ, BM)
Real
-
time feedback on the needs and desires of the end users (TR)
Goal
Clear support and setup of roadmaps from the governing bodies (BR)
Connectivity agreements (BR)

80
Weaknesses
Tech.
Dependence on technology providers (TR)
Security,
privacy protection, safety issues (5BQ)
A certain level of uncertainty in terms of progress (TR)
HR
Not possible to have the skills for all roles within the ecosystem (TR)
Lack of human resources, especially data analysts (TR, BR)
Use
A myriad of
solutions, but low acceptance and usage (TR, BR)
Development lacking application focus and value capturing models (BR)
Outlook
Lack of regulation and standardisation balance (TR, BR, 5BQ, CB, P5)
The general discrepancy between investment and
expectations (BR)
Less dynamic (large) enterprises falling short of customisation (BR)
Inability to foresee future security issues (TR)
Stakeholder misalignment in terms of privacy issues (BR, P5)
GDPR misinterpretations (BR)
Lack of
European agreement on wide infrastructural financing (BR, CB)
Opportunities
Technical
5G radically improves performance (TR)
AI and Edge computing improve decision
-
making (TR, BR)
Cloud services miniaturise wearables (TR)
Low
-
cost sensors
lead to new ideations (TR, 5BQ)
Data improve assets, processes and costs (TR, BR, 5BQ, CB)
Braking silos (mobility, connectivity, data, electrification) (BR, 5BQ, P5)
Energy transition (5BQ)
HMI improvements (5BQ)
Non-Technical
Digitalisation modifies the competitive game and value chains (BR)
Connectivity supplements business models (BR)
The reluctance of others to engage early opens the market (BR)
Early technical development opens the leadership position (BR, P5)
Unsustainable status quo in terms of the business environment (BR)
Ability to influence through early standardisation activities (TR, BR, P5)
Early stakeholder engagement and feedback loop (TR, BR, 5BQ, BM, P5)
Eliminate inefficiencies through the
unification of providers (BR)
Reduction in manufacturing downtimes (TR, BR)
Gamification, convenience, customisation, infotainment (BR, 5BQ, BM)
Services and products for emerging economies (BR)
MVP
–
assemble, (fail) and learn fast (BR, BM, P5)
Sharing economy aids market penetration with fewer products (BR)
Safety, incentives and fun factor entice data sharing (BR, 5BQ, BM)
Data management disparity creates data market opportunities (BR)
Poor cross
-
domain contact/lack of clarity on correl
ations (BR, CB)
Data monetisation (BR, 5BQ, CB)
External data complements internal understandings (TR, BR, CB, P5)
Algorithm training on volumes of representative data (TR, BR, 5BQ, CB)
The firm link between data collection, processing and
usage (TR, BR)

81
End
-
to
-
end services (BR)
Internet expansion into the physical devices, hence Big Data (5BQ)
Trustworthiness through better security, safety, privacy, reduction of downtimes, timely
maintenance at no additional cost (5BQ)
Establish a strategic sustainable relationship with institutional buyers to safeguard
stability for all stakeholders and hence maintain the level of trustworthiness (P5)
Long
-
term customer relations incentivise advertising (BM, P5)
Strategic
sustainable relationships with suppliers (P5)
Lifecycle management aids quality (5BQ)
Streamlined supply chains and lowered cost of production (5BQ)
Cross
-
fertilisation (5BQ)
Openness creates additional opportunities
-
ideation (BM, P5)
Define mission and focus on applications (BM)
Enhance existing products and services (P5)
The distributed network might replace the platform effect (P5)
Brand identification through persistent marketing strategies, unique value proposition
and support of creative elements (P5)
Reduction in vehicle ownership (5BQ)
Urbanisation (5BQ)
Technologies are important, but implementation is crucial (P5)
Grouping operations with partners to exploit the economy of scale (P5)
Tracking technical
developments for the dynamic changing business environment (P5)
HR development (BR, P5)
China / BRICS (BR)
Threats
Technology
Fragmentation due to the lowered barrier to entry (BR, TR)
Resistance to the new technical developments (TR, BR)
Security issues (TR)
Privacy breaches (TR)
Failure of 5G to deliver at a technological level (TR)
Business Environment and Outlook
Competition from non
-
automotive data analysts (TR, BR)
Lack of trust in solutions (TR, BR)
Superficial
'wrapping' into a 'digital layer' (TR)
BRICS/emerging economies remove differentiation potentials (BR, P5)
The negative correlation between services (BR)
Not acting (aiming to be an integrator) creates a vacuum (BR)
Refusal to (digitally) evolve ca
uses extinction (TR)
Misalignment in terms of regulations between countries (TR, BR)
AI diminishing human skills (TR, BR)
Political populism (BR)
Decreasing vehicle ownership (BR, 5BQ)
The inability to adapt and supporting core
businesses (TR, BR, 5BQ)
Weak product differentiation (P5)
Global instabilities causing technology split (e.g. trade wars) (P5)

82
D. Appendix: Expected Benefits
Table 10. Expected Benefits - Detail
Deliverable Usage Benefits
Digitalised
businesses
Efficiency / lowered costs:
increased usage of the
existing and new products
Improved life quality through better quality
and more efficient services
A better
exchange
between staff,
products,
operations and
customers
Improved feedback
enhances the usage of new
products as they become
better aligned to the user
expectations
Improved user experience and satisfied
customers as their expectations are met
IoT solutions Increased usage of products
and services based on IoT
Improved user experience, better life quality
Better product
and increasing
service
efficiency
resulting from
real-time data
Increased usage of services
and products
More efficient task completion, more time
for leisure activities
Increased data
rates and low
latency (5G)
Increased trust into AD
improves acceptance and
market uptake of products
Improved mobility and efficiency
(multitasking while driving), road safety
M2M enabled
devices
AD, smart anything Enhanced life quality through better quality
decisions
AI empowered
devices
Increased product usage due
to improved decision-making
abilities
Reduces road accidents, better business
decisions, reduces (human) error, improved
trust
Cloud services Miniaturisation and energy
efficiency at the device level
increases device uptake
Reduced environmental impact (battery
lifetime), improved decision making (higher
computation)
Edge devices AD, smart anything Faster and better-quality decisions lead to a
safer road environment, improvements in
efficiency, savings in the supply chain, longer
shelf-life, improved logistics etc.
Integrated low-
cost sensors
Better sensitivity and
awareness leading to an
increase of trust
Trustworthiness conquers resistance to
technology and in turn, creates efficiency
savings

83
Smarter
solutions
Increase of usage subject to
the convenience, the savings
and the fun factor
User satisfaction and improved trust
Predictive
maintenance
service
Improved service contracts Trust and increased revenues (lower
maintenance costs, no down times, better
efficiency, feedback improves products)
Fleet
management
service
Deployed fleet management
services / improved
contracts
Improved: efficiency, waiting times,
downtimes, congestion, contracts, operative
glitches, predictability of operations
Connected EV More EV on public roads Convenience: smart charging
Platooning
algorithms
Logistics and freight
organisations utilising the
platooning services
Reduced costs (lower fuel consumption,
improved flow and congestion), fewer
accidents, driver satisfaction
MaaS
applications and
related business
models
Increased usage of MaaS Satisfied stakeholders (Reduced: operative
and maintenance costs, ownership,
congestion, insurance costs; Improved:
efficiency, utilisation, sustainability,
deployment, air quality, revenues, customer
relations, advertising, resource utilisation;
market penetration with fewer products)
New business
models
Increased usage of
intelligent and adaptive
products
New modes of value creation, improved
efficiency and user experience at lower cost
Lifecycle
management
Prolonged and improved
contracts
Increased trust, improved efficiency
Customisation Increased usage of MaaS and
products
User satisfaction (choice, trust,
transparency, shaping products)
Improved
infotainment
Increased uptake of modern
services
Multitasking, improved time utilisation,
personal satisfaction
HMI focused
CPS
Increased usage of AD Personal satisfaction, deepening of trust,
efficiency advances, savings in the supply
chain
Security and
privacy
protection
Improved uptake of products
and services
Satisfied end-users through improved
trustworthiness
Marketplace
sourced data
Increased uptake of products
and services as they cross-
correlate different domains
Win-win for all and improved value creation
through cross-fertilisation

84
E. Appendix: Expert Interviews
Several expert interviews were conducted with the aim of complementing the findings of this
work with the ideas of others who are dealing with the relevant topics in practice. The aim of is to
assemble qualitative insights rather than quantitative values. The most valuable parts of the
interviews are referenced in the document. Several conclusions are presented in section 3.5.
__________________________________________
Interviewee: Georg Zembacher, Department manager: Smart City, AVL List
GmbH, Regensburg, Germany (Zembacher, 2019)
• The industrialisation of increasing volumes of IoT solutions related to the market rollout
of smart mobility services
There are many IoT solutions targeting smart mobility, some of which are market-ready. The
common question is concerned with the industrialisation of such applications. The existing
standardisation topics are heading in direction of resolving the issue of segmented developments.
However, the market needs are calling for the possible deployment of products that cannot yet be
reliably utilised with the existing technology. For example, it is debatable how far the existing
solutions could be supported by 5G at this moment. Hence, intermediate alternatives might need
to be considered (e.g. G5). However, the nature of the offered products and services ought to be
modular and upgradable (the sustainability grounds).
• The consequences of system integration of smart services into smart cities
Integration of smart services into smart cities might have unexpected consequences. The
immediate positive effect is the rise of flexibility i.e. combined mobility of people and goods. The
bonded services should be mutually beneficial for each other and jointly work towards the
creation of additional value. However, some services could end up having a negative effect on
each other and this effect caused by the integration of services should be studied prior to solution
rollouts.
The integration also hangs onto technical solutions and their segmented financing and
implementation time frame. The financial contributions from all stakeholders must be defined.
The challenge arises from collaborative integration of proposed solutions, as partial deployment is
unlikely to yield full benefits.
• How to motivate mobility operators to share data and their services?
There are start-ups (Helsinki and Vilnius seem to be at the forefront) that combine all mobility
providers under one roof (not only public ones but also private taxis etc). The city provides open
access, but anyone wishing to participate needs to provide own customer profile. So, the city
decides on how to manage the resources i.e. they shape the mobility services based on the
available profiles at the time. It is defined who can use the ecosystem and a mobility platform is
created on another layer - the two are connected so that the mobility services can be operational.
This merging is also the source of mobility data. The insights usage of services enables
adjustments. The city MUST define the rules: “if you would like to play in my playground, you
must provide me with your open source mobility options and you also share it with others”. The
city can re-plan how to reuse the mobility options. The public spaces (analogue or digital) must be
regulated, as it has a direct effect on people's lives. The same is true in terms of energy
distribution (i.e. EV charging).
• How to offer incentives for improved usage?

85
The key is ‘convenience’. Money is an enabler, but it is not an efficient convincing method for
improved usage. If users need to do something, they will do it, but the question is how they will
do it. Convenience drives people. So, if a service is installed it can support data acquisition and
analytics. If the message is that the usage of the service is good for the city, the response is going
to be poor. If the message is that it is good for the users, the responding question is “how/why?”.
The additional difficulty is that many services are promoted through marketing initiatives and
these are often impossible to fight. The way to deal with them is to beat them with convenience.
__________________________________________
Interviewee: Gerhard Schagerl, Senior Product Line Manager Data
Intelligence, AVL List GmbH, Graz, Austria (Schagerl, 2019)
• Organisations are developing technology and business models, but they do not seem to
be scaling up. The consequence is that the digitalisation is creating a fragmentation.
How would you tackle the issue?
The issue appears to be somewhat deeper and not necessarily technically orientated. The greatest
challenge is caused by a change in business models from existing to whatever new form they
ought to take e.g. from selling software licences to selling the conclusions generated through data
analysis (or from CapEx to OpEx). Once the need for this cultural change is better understood and
accepted, then the drive towards technical solutions and their scaling up should be eased…
• Some would suggest that the “infrastructure” is one response to scaling up.
Unfortunately, the infrastructure deployment is slow and not so decisive. The question
is who does it? Industry, governments, cities, all stakeholders together…?
Standardisation is imperative, regardless of who is accountable for the implementation! The
solution must also be globally agreed (or at Least Europe-wide). The lack of agreement would
potentially result in vehicles not communicating with infrastructure in specific regions. There is
also a need for publicly controlled/regulated services (cities or governments).
o Who should implement the infrastructure?
The cost of sensors is not low and recuperating investments would be difficult for an industrial
stakeholder. Hence, financing of Smart Cities or smart motorways must come either from the
cities or from governments. One solution is through the already existing road-tax systems
(Vignette).
• Is there a lack of standardisation or too much standardisation when it comes to data
and infrastructure? How does this impact business development?
There is a serious lack of standardisation. For development point of view, it would be easier if
crucial aspects are standardised to reduce the efforts needed for solving issues that have either
already been solved or that are less relevant but still required for deployment.
• Platform versus distributed Network effect?
So far, the practical experience shows that everything converges (e.g. Uber) and the winner still
takes all. The effects of the distributed network do not seem to be that evident in practice yet.
The question would be “how would you force to stop the platform effect in an open market?”. If
the data becomes fully open, suddenly there would be other forms of payments for the Internet
and email services?!

86
__________________________________________
Interviewee: Mario Drobics, Thematic Coordinator, Internet-of-Things (IoT),
Center for Digital Safety & Security, AIT Austrian Institute of Technology
GmbH, Vienna, Austria (Drobics, 2019)
• Surveys of potential users suggest that the greatest obstacles to the acceptance of
connected and autonomous vehicles are related to cybersecurity and privacy concerns.
How would you conquer this resistance?
The issue is much broader, but if narrowed down to security and privacy only, there is a need for
certification and quality labels that enable trust. EU is on a good track to achieve this, but this
must be extended globally. Also, a crucial point is that this is not a one-off process, but the
certification must be continuous. The implementation difficulty is of an organisational nature.
That should at the end ensure trust, but also differentiate the EU industry on the global market.
On the other hand, the issue of acceptance is viewed through trust in the implementation of AD.
One concern is related to the predictability of the system. For example, vehicle sensors could
detect a specific scenario and react according to its algorithms that prioritise efficiency higher
than the user perception. The consequent vehicle reactions to the scenario could be perceived as
not safe for the road users and could create some discomfort. The challenge is to make the
decision mechanisms transparent so that the users trust the system.
• There is a reluctance to finance infrastructure. The technology fragmentation is causing
an even greater barrier. How would you deal with the infrastructure development and
how should it be financed (public, industry, together, cities….)?
The gap is not huge at the moment, but there is a need for upgrades of the existing and for the
integration of new services. There are two distinct use cases: Smart Motorways and Smart City.

The motorway infrastructure is already in place, but it requires a connectivity and ICT
upgrade (together with telecom operators).

The Smart Cities have a tendency to have some public services enabled by the city. If
there is interest from the city, the services can be enabled for wider exploitation. A part
of the financing is covered by private companies, so the solution is in combined financing
of infrastructure.
Majority of the infrastructure is communication-related and the question is what needs to be
rolled out. The AD is autonomous anyhow and only exploits communication to other vehicles and
some updates from infrastructure. That might change once we have a high real-time link to
vehicles so that edge computing is in use. This is conceptual for now and new development will be
needed. That must come hand in hand with new business models.
The important aspects for the users are the convenience, increased efficiency and safety. Hence,
if these can be satisfied, the cities are willing to invest. A challenge is seen in standardisation.
OEMs will not adjust vehicles subject to what specific cities decide to implement. The solution
could be in the examination of what it is that needs to be communicated and should that
communication be direct to vehicles. If traffic light data is openly communicated to all, the city is
utilising the IoT on its own terms and then the slow integration will become reality as the usage is
increasing. This development is already evident with certain public transport services where more
and more cities are offering open data – free parking spots are one of the early implementations.

87
• There is a lack of standardisation in terms of handling data in the data marketplace.
How would you deal with it i.e. should the data be offered through huge platforms and
data brokers or a network of smaller distributed platforms or some other option?
There are respectable proposals in terms of marketplace solutions that contain no data but offer a
possibility for data exchange that could be used for the provision of services. It brings together
expertise from different areas (providers, users etc). The access to data is controlled. An obvious
prerequisite is a form of standardisation.
__________________________________________
Interviewee: Jelena Ilic, Senior Data Scientist, Mango Solutions London, UK
(https://www.mango-solutions.com/) (Ilić, 2019)
• Organisations are developing technology and business models, but they do not seem to
be scaling up. The consequence is that the digitalisation is creating a fragmentation of
technology. How would you tackle the issue?
Establish standardised processes.
• Is there a lack of standardisation or too much standardisation when it comes to data
and infrastructure? How does this impact business development?
There is probably a lack of standardisation. The freedom in the data and infrastructure was
needed and desirable at the initial stages of the data revolution so that different ideas and
solutions could be developed and the best ones could crystallise. But, a point is reached where
different vendors are reinventing the wheel producing slightly different technologies, which
makes difficult for businesses to choose the most appropriate one.
• How would you regulate access to data?
That is purely dependant on data classification. When it comes to personal and company data, the
importance of privacy is crucial and the data should remain closed. However, any data that is
important for scientific research should be open.
__________________________________________
Interviewee: Eric Armengaud, Project Manager R&D / Innovation Manager,
AVL List GmbH, Graz, Austria / EC External Expert ICT programms / Guest
Lecturer FH Joanneum, Graz, Austria (Armengaud, Project manager R&D /
Innovation manager, 2019)
• How to make the Data Marketplace successful?
IoT capability to create business through new services is relying on the exchange of data. For that
to happen, there is a need to access data, transfer it, process it and there must be a customer
ready to pay for that service. Aside from the operational aspects of the data marketplace,
incentive mechanisms are needed to create data, which also draws in an issue of user-acceptance
of the new technology i.e. do I trust broker that they manage my data properly and that I get
financial benefits for it.
From the perspective of data broker, success demands a large amount of data. That requires
infrastructure and data governance. It is still unclear how this is likely to look like in the future i.e.
if platform effect is there (the winner takes all) or if there is a distributed network effect and also

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how far will this data be domain specific, or could it be very disparate in terms of topics. To have a
single large platform is challenging due to the lack of flexibility and agility. So, the handling of the
data in the marketplace must be agile. It is yet to be seen if a monolithic winning platform is to
emerge, or there if the winner is a business organisation with many smaller agile platforms that
cooperate with each other. The idea of many independent businesses creating a distributed
network effect is utopian and the fact that this is not happening in any aspect of digital businesses
might mean that is simply cannot happen in practice (open market).
Standardisation is there to accelerate the uptake, but that is only possible if there is usage. Hence,
business uptake should be there before the standardisation. The business uptake is a function of
services provided to the users and their willingness to purchase those services. As of today, this is
very limited and the scaling is not taking place. The reasons are unclear, but a lack of trust could
be the main one. Hence, this restriction in terms of data provision prevents the thriving of the
new data-based services.
• What is needed to succeed?
This is about new businesses and future society. To upscale IoT business world, there is a need for
a strong change from within the society.
• If you wanted to make IoT fail, what would you do?
Remove the trust i.e. privacy, security and confidentiality.
• How do you define success?
There are two main aspects:
1. Amount of business and the amount of added value (financial or not) that could be
created
2. The amount of data exchanged combined with its quality and content, as well as the
coverage of the system
• How would you regulate data?
It depends on the case by case basis.
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Section 3.5 contains some concluding remarks resulting from the expert interviews.