The Disruption of Electric Vehicles in the Car Industry

This article discusses the potential disruption of the traditional car industry by electric vehicles. It explores the S-curve of technology adoption and the concepts of competence-enhancing and competence-destroying discontinuities. The article also examines the strategies organizations use to protect their innovations and maintain proprietary advantage.

1 | P a g e
Question 2:
Shilling (2016) talks about how technology tends to be cyclical, creating S-curves that illustrates initial
periods of instability, followed by enhanced improvement, then by diminishing returns, until it is displaced
by a new technological discontinuity.
The S-curve illustrates the process of a technology measured by cumulative adaptors (e.g. users) and time
(e.g. development), where the upper-going curve of adaption is referred to as diffusion. The process of the
technological evolvement is fragmented into different phases of adaption and majority (Early Adaptors &
Innovators, Early Majority, Late Majority and Laggards), based on the diffusion respectively related to
cumulative adaption and time (Shilling, 2016).
The traditional car industry in the United States seems to be in the phase of Laggards, due to its maximum
potential of diffusion in terms of adaptors - where we find 113 million registered passenger cars in the US,
with a tendency of diminishing returns of fewer people buying cars; e.g. millennials lower rate of car
ownership and driver’s license that dropped from 92% in 1983 to 77% in 2014, not to mention new
alternative solutions of transport in terms of ride hailing in the cities (Sull and Reavis, 2018).
Simultaneously, the technological improvements of the combustion cars seem to be heading towards a
turning point, as several countries around the world are already starting to ban productions of diesel and
gasoline cars in the future (Coren, 2018), even though the technology is still being developed upon, merely
as a use of combining it with other technologies, such as hybrid technology (Bullis, 2007).
However, Shilling (2016) argue that technologies don’t always reach their full potential, but that they can
be rendered by new technological discontinuity building on new knowledge while fulfilling similar needs in
the marketplace. Anderson and Tushman (1990) defines this as “an order-of-magnitude improvement in
the maximum achievable price vs. performance frontier of an industry”.
In this case, the technological discontinuity is Electric Vehicles, offering a new technology that is, as any
other new technology, processing the relationship between time and adaption. This creates a new S-curve

2 | P a g e
shaping a path for this technology - what Dosi (1982) calls a technological trajectory, where sales of EV’s are
growing rapidly, with a 25% increase in sales in the United States alone from 2016/17 (Sull and Reavis,
2018). On top of that, automakers and related manufactures in the industry have incorporated EV,
batteries, charging stations etc. as a top priority in their business model; GM planning to offer a fleet of 20
EV models by 2023 and Ford announcing out-phasing of combustion models, while investing 11 billion USD
in hybrids and EV’s, BMW making 1.1 billion dollar contract with CATL to build a battery factory - not to
mention the governmental interaction offering credit taxes in terms of boosting adoption among
consumers (Sull and Reavis, 2018).
Anderson and Tushman (1986) distinguishes between two types of discontinuities related to competencies;
(1) competence-enhancing discontinuity that “build on know-how from the technology that it replaces”.
This can be certain improvements in performance or price, making alternatives for older technologies,
without requiring completely new competences.
(2) competence-destroying discontinuity that “renders the expertise required to master the technology that
it replaces”. This can be done by creating substitutes for existing products or inventing a new product class,
often making incumbents lose to new entrants consisting new knowledge.
On one hand, the technology of Electric Vehicles, as of today, can be seen as competence-enhancing, based
on the arguments that the technology is building on previous knowledge about car manufacturing, but
making it simpler, cheaper and more performative (like Anderson & Tushman’s (1986) example with
mechanic typing machines vs electrical ones), by using one-third as many components compared to
combustion cars, while having only one gear and yet improved performance with e.g. Tesla’s Model 3
acceleration from 0-60 miles in six seconds (Sull and Reavis, 2018), considering this being a ‘regular’ car for
the mass segment. At the same time the electric fuel is cheaper (0,11 USD pr. kWh). Using a Tesla Lithium-
ion Battery at 85kWh, that’s only 9,35 USD for a full charge (Sull and Reavis, 2018).
On the other hand, one might argue that Electric Vehicles is continuously becoming competence-
destroying, working as a substitute for an existing product (traditional cars), but with a different, but better
process of delivering and using that product; as Anderson & Tushman’s (1986) example with diesel versus
steam locomotive - or mechanical ice making substituted for natural ice harvesting. In all cases, the product
remains essentially unchanged, while the process of both manufacturing and using the product is altered
(Anderson & Tushman, 1986). As they furthermore argue, a competence-destroying discontinuity involves
discrete steps of continuously incremental changes, until it is so much better and/or cheaper that it makes
the old technology redundant and thereby eventually destroys it. This is basically what we see in the
development of EV’s, constantly changing and improving step-by-step, making things better and cheaper
continuously; going from Nissan LEAF’s 24-kWh battery in 2010, to a 75 kWh for Tesla’s Model 3 in 2017

3 | P a g e
(Sull and Reavis, 2018), while prices on the batteries drops by 80% in the same time-period (Sull and Reavis,
2018 - Exhibit 10).
Besides that EV’s are more performative and cheaper than combustion ones, they also seem to be creating
a whole new network of connectivity, possibilities and trends among society; connecting vehicles with 4G
and 5G acting as virtual assistants, sensing the environment around them and interacts with other vehicles
and entities - making it possible to upgrade your car through online digital software, not to mention the
16.000 public charging stations and 43.000 individual charging connectors in the US (Ibid.). On top of that,
there is an intense development in autonomous driving, where we see new entrants as Apple and Google
participating and electric transportation concepts evolving around the world; Zipcar, Lime etc. (Ibid.).
It is hard to distinguish if EV’s are competence-enhancing/-destroying the car industry in the US - and if
they going to disrupt the industry. An article wrote by Warren and Shankleman (2017), argues that the
complementary network of electric cars is going to disrupt the traditional car industry, just like Apple did
with the mobile phones, which correlates with the concept of competence-destroying. But at the same
time, a research made by Bergek et. al. (2013), argues that the technology of EV’s are making incumbents
perceive the potential of these new technologies rather than new entrants disrupting and destroying them,
which correlates more with competence-enhancing as well with earlier arguments of well-established
organizations in the industry adapting into the EV technology.
Christensen (1997), who created the concept of disruption defines this concept as innovations being
discontinuous from existing patterns of product and service development in an industry, derived from
exploratory developments, that eventually changes the value proposition by a least an order of magnitude
and simultaneously endangers the existence of incumbents. Seen from the arguments and perspectives of
competence-destroying there is very direct parallels to Christensen’s (1997) definition of disruption.
However, seen from the perspective of competence-enhancing, it has correlations, but slightly
disagreements on the endangerment of incumbents.
Looking at the S-curve of EV’s, one might argue that it is has not even reached the early majority phase; e.g.
Tesla just recently introduced their EV (Model 3) for the majority segment, nevertheless is has a high
diffusion rate. Theoretically and historically EV’s could seem to be at the edge of disruption, where
traditional norms and products from the industry is overruled; comparing with other examples, e.g. when
horse-wagons was discontinued by combustion cars - that disruption process took only 13 years (Hewitt,
2018).
The exploratory evolution and discontinuity of Electric Vehicles seems to be changing the previous
conception of the car industry in more than one aspect and are growing at a fast rate. Whereas combustion
cars are diminishing, EV’s are offering a cheaper, more connected and more performative product.

4 | P a g e
However, EV’s can from some perspectives be seen as competence-enhancing, while other perspectives
hint that EV’s are becoming more and more competence-destroying, through continuously incremental
changes and improvements and hence becoming disruptive in the future.
Question 3:
Shilling (2016) talks about appropriability, which she defines as the determination by how easily and quickly
innovations can be imitated by competitors. In other words, Teece (1986) defines it as the capacity of an
organization to hold any added value creation for its own benefit.
Such appropriabillity can achieved and maintained through intellectual property rights; such as patents,
trademarks and copyrights; but also by complex knowledge bases build upon unique interactions, talented
competences and complementary goods (Shilling, 2016). The strategies organizations choose to use, in
terms of their appropriability, affects their proprietary advantage. In this case Shilling (2016) made a figure
which distinguishes between wholly proprietary and wholly open systems.
She describes wholly proprietary systems as technologies that are completely protected through e.g.
patents, secrecy and copyrights, while wholly open systems are technologies that is not protected by such
intellectual property rights. However, firms often find themselves in between wholly proprietary and wholly
open, utilizing varying degrees of control (Schilling, 2016). Patents is expensive, especially for start-ups, so
organizations must carefully consider how to approach the appropriability of the innovation (Shilling, 2016).
Tesla is an example of an innovative tech-company who have been very keen on protecting their innovation
through intellectual property rights, more specifically through patents. According to their Annual Report
(2012), it is stated that their proprietary technology includes everything from cooling- and safety systems,
to battery engineering and customized motor design. Tesla have felt a need to protect these technologies,

5 | P a g e
through patents, out of concern that big car companies would imitate these and use their substantial
resources to outcompete Tesla (Musk, 2014). In fact, in December 2012, Tesla issued 117 different patents
with more than 258 pending patents (Annual Report, 2012).
However, Elon Musk, the CEO of Tesla, announces in June 2014, that all of their patents have been
removed, with the argument of; spirit of open source movement, for the advancement of electric vehicle
technology. A strategy that is very different, from their previous one - regarding intellectual property rights.
But why? And how does this affect their proprietary advantage?
Teece (1986) addresses that innovations win, and hereby improve proprietary advantage, not necessarily
through patents - but through regimes of appropriability and complementary assets. He argues that generic
assets, (such as machines), and co-specialized assets (such as repair facilities) strengthen the position of
innovative organizations and makes it possible to use those assets to outperform competitors. Tesla has
strong generic complementary assets, just to mention their 5 billion dollar Gigafactory, producing their own
batteries better, cheaper and faster than anyone else, not to mention the volume of weekly cars of 5000
units, using extremely high AI technology automated throughout 5.5 million square feet (Sull and Reavis,
2018). Besides the factory, they have achieved co-specialized complementary assets in terms of 111 Tesla
stores in 26 states in the US, 76 service centers, not to mention 1300 charging stations and 11.000
superchargers (Sull and Reavis, 2018).
(Cohen et. al., 2000) argues that an effective appropriability mechanism are keeping knowledge internally
within an organization through secrecy and tacit knowledge. Tesla is a good example of an organization
with a lot of secrecy and tacit knowledge, by the way that they are manufacturing their products differently
than others, using AI technology and choosing to in-source 80% of their manufacturing; keeping this
internally (Sull and Reavis, 2018). (Cohen et al., 2000) furthermore argues that lead-time is a way of gaining
advantage in an innovative process. Since Tesla came up with the initial idea of making electric vehicles
until the point where they choose to open their patents, they have generated a high diffusion, as analyzed
and discussed in question 2. This has created a position that is very hard and extremely resourceful to
imitate and catch up with.
One might argue that while being protected by patents; having wholly proprietary advantage, Tesla have
reached a position where they have gained a strong position through appropriability of complementary
assets, secrecy and lead-time - that imitators hardly can outperform. This correlates with arguments from
the learning objectives in Lecture 2, that patenting can enable start-up firms to signal competences and
reveal complementary capabilities (L2, PP; s. 28). Tesla don’t feel threatened by the big competitors like

6 | P a g e
they used to, due to the position they have achieved; as Elon Musk realizes, and talks about in his
statement; electric car programs only consist 1% of major manufacturers vehicle sales (Musk, 2014), which
I interpret as Tesla feeling confident about their position.
As mentioned earlier, patents are expensive (Shilling, 2016) and getting rid of their patents while
benefitting from their complementary assets, lead-time, diffusion and internally manufacturing, service
centers, stores and R&D locations, is a trade-off that one might argue could be partly the reason to why
they opened their patents.
However, even though they have reached a stronger position, it can be argued that the proprietary
advantage has become wholly open rather than wholly proprietary as they released their patents. This
indicates that there is certain dynamics of this openness, that Tesla can benefit from - which is
simultaneously the remaining part of this decision.
Chesbrough (2003) distinguishes between the organizational benefits between closed and open
innovations.
He argues that closed innovations are supported by in-house activity and substantial investments in
internal R&D with a main focus towards bringing a certain product into the market as fast as possible, while
strictly controlling intellectual property rights. This correlates with the methods used by Tesla before they
opened their patents.
However, most importantly; he furthermore argues that open innovations contribute in a way that firms
are able to commercialize external ideas by implementing outside pathways to the market, while
simultaneously commercializing internal ideas through channels outside of their current businesses to
generate value. According to Chesbrough (2003), this has the benefits of creating faster technological
development and mobility of knowledge.
This correlates very well with Elon Musk’s statements, made in his announcement about the release of
their patents, where he admits that it would be impossible for Tesla to build electric cars fast enough, to
address the carbon crisis - while also admitting that the real competition is the gasoline cars that is pouring

7 | P a g e
out of the worlds factories every day (Musk, 2014).
Tesla opened their patents because they have acknowledged a need for more dynamic and broader
research to fulfill future goals through open innovation, while simultaneously feeling confident enough with
their position to make this decision, realizing a smaller threat from competitors. Tesla’s proprietary
advantage have been released from being wholly proprietary to now being wholly open - however they
have reached appropriabillity elsewhere, through complementary assets, secrecy and lead-time.
Question 4:
Delgado (2018) emphasizes that locational attributes are fundamental considerations in terms of
implementing strategies within firms and regions.
Regarding this matter, Delgado (2018) presents the aspects of industrial clusters, which she defines as;
“geographical concentrations of related industries and firms”.
One of Tesla’s major (5.3 million square foot) R&D and manufacturing plants are located in Fremont,
California, a part of Silicon Valley (previously owned and used by GM and Toyota (Sull & Reavis, 2018).
Almost all of the bigger car companies are located here, including Tesla, VW, BMW, Mercedes, Toyota and
several more (Coren, 2017). This is in line with the definition made by Delgado (2018) of Clusters and is
actually argued to be one of the biggest IT-clusters in the world (Wang and Zhao, 2010).
In extension to these clusters, Delgado (2018) talks about external agglomerations which are defined; “as
centrifugal forces that drive firms to disperse activities geographically in search of the best clusters”
In relation to this, she argues that within these clusters of external agglomerations organizations both
cooperate and compete with each other, and how organizations are connected through linkages of skill,
knowledge, technology, supply and demand. First of all, these arguments made by Delgado (2018),
correlates with Tesla being located among their major competitors. Second of all, an article wrote by
Donato-Weinstein (2014) analyses that Tesla are corroborating with critical partners within this
agglomeration; Asteelflash (a multinational electronic contract manufacturer), who is one of their suppliers.
They are located right next to them, which furthermore seem in line with the arguments Delgado (2018)
brings of external agglomerations.
Excluded from the plant in Fremont, Tesla eased an assembly plant in San Joaquin County city of Lathrop,
just 51 miles from Fremont, not to mention their 350,000-square foot Palo Alto headquarters just across
the bay from Fremont (Donato-Weinstein, 2014). It is furthermore argued, that this is a part of an emerging
industrial ecosystem, that is growing around Tesla, where Tesla in collaboration with their suppliers secured

8 | P a g e
tons of square feet of industrial space in the region (Ibid.). This connects with the work of Marshal (1920),
who Delgado (2018) uses in her research about clusters and external agglomerations, where it is
highlighted that supplier-buyer linkages are an associated performance in such agglomerations.
Donato-Weinstein (2014) furthermore explains how this offered massive job-opportunities with a staff of
more than 6000 employees and co-creation of ideas with their partners, that would not be possible if they
were separated in terms of location (Ibid.). As the Vice President of Asteelflash, Don McCormick says in the
article: “If Tesla says, ‘Can you jump this high’ we can pull everyone into a meeting here and make things
happen”, further arguing that this would be much more difficult if they were located in Asia or Mexico, for
example. This also applies to Delgado (2018) and Marshall (1920) arguments, as they furthermore
emphasize the important elements of labor market pooling and knowledge spillovers in external
agglomerations.
Overall, it seems rather clear that Tesla have located themselves in a related (IT and Automotive) industry
cluster of external agglomerations, as there are direct parallels between the theoretical assumptions made
by Delgado (2018) and Marshall (1920) regarding clusters and external agglomerations, and the case
material related to Tesla’s locations where we find evidence of the company being located in a cluster
among several of their competitors as well as partners, competing, co-creating links and knowledge and
developing skills and labor occupations.
However, Alacer and Delgado (2016) also discusses the relevance of internal agglomerations, which they
define; “as centripetal forces that drive within-firm co-location”.
Besides Tesla’s locations around Fremont, they have built a Gigafactory in the Nevada desert, big enough to
hold 33 football fields with ambitions of filling over 10 million square feet, producing 5,000 Model 3 cars a
week and two battery packs a minute (LeBeau, 2018). This factory is located very differently, in terms of
agglomerations having no related industry cluster around. This is applying more with Alacer and Delgado’s
(2016) definition of internal agglomerations, where they are furthermore arguing that internal
agglomerations are same-firm facilities that can be located within one big facility.
It has been identified that Tesla has become much more internally orientated - with the production of all
Model 3’s components to be made 80% in-house (Sull and Reavis, 2018). A decision, that is made based on
previous bad experiences with external suppliers, not living up to the quality in previous models, e.g.
dissatisfaction in seat production (Ibid.). This is in line with arguments in Alacer and Delgado’s (2016)
research as they stress how internal agglomerations, and hereunder with-in firm co-locations, enhances
organizations ability to control (Giroud, 2013, Kalnins and Lafontaine, 2013).

9 | P a g e
According to the VP of production, this is a decision that has resolved in Tesla being more vertically
integrated than any other company in the industry since Ford Rouge plant in the 1920’s, now producing
own batteries, power electronics, drive-train systems, cables, displays and fuses themselves (Sull and
Reavis, 2018).
At the same time, Tesla seems to be merely focused in developing talented employees internally rather
than externally, not employing individuals from other car-companies - instead strictly screening potential
employees concentrating ability to solve complex problems (Dyer and Gregersen, 2015). They strategically
use a qualitative over quantitative internal employment process, allowing them to communicate, solve
problems and initiate faster than their competitors (Ibid.). With just three qualified designers sitting next to
its engineering counterparts, they were able to design the S-model, compared to other bigger automakers
using 10-12 designers who are siloed organizations who communicate slowly (Ibid.). This correlates with
arguments included in Alacer and Delgado’s research (2016), made by Chandler (1962) and Henderson and
Ono (2008), that activities are better coordinated among the value chain of organizations, which improves
the performance within these organizations.
Summing it all up, Tesla are exploiting a combination between internal and external agglomerations in the
United States, with R&D and manufacturing locations in both Automotive, IT and related industry clusters
as well as internal undertaking enterprises. These choices of location can correlate and support each other -
as they can work towards the same goal when a firm is already located in a strong industry-related cluster
(Cohen and Levinthal, 1990).
Question 5:
Based on the article written by Greeks New Agenda; it has been identified that Tesla is going to start a new
R&D team in Greece with the total numbers of ten specialist engineers. The scope of the R&D team is to
introduce and design an effective electric motor technological innovation. N.C.S.R. Demokritos R&D team
has been developed by Tesla, so that the talent of Greek engineers can be properly utilized
(Greeknewsagenda.gr, 2018). Due to increasing technological opportunities, it has become easier for
entrepreneurs to develop their business in Greece (Chatzoglou et. al., 2016).
Audretsch and Feldman (1996), examines a link between knowledge spillovers in industries and innovative
activity clustering, where knowledge externalities are more dominant in industries where new economic
knowledge is an important element. They argue that; “new economic knowledge is captured by industry
R&D, university R&D and skilled labor”. In this case, the arguments made by Audretsch and Feldman (1996)
applies to how Tesla is trying to capture new economic knowledge by taking advantage of Greek talented

10 | P a g e
special engineers, who has a reputation of being specifically skilled within the area of electric motor
technology, as Greece is educating engineers specifically within this field from the National Technical
University of Athens (Greeksnewagenda.gr, 2018).
Alacer and Chung (2007), argues that technically advanced firms are attracted to locations with high level of
academic activity, while avoiding industrial activity; leading firms to avoid competition - locating themselves
geographically different from competitors as a mechanism to protect their technical capabilities, thereby
gain competitive advantage. It seems that Tesla is in line with these arguments, as they opened a R&D lab
in Greece, away from competitors. Simultaneously, they are taking advantage of academic benefits, as it is
argued by one of Tesla’s spokespersons; “Greece has strong electric motor engineering talent and technical
universities”, with tailored programs offering extraordinary skills for electric motor technology
(Greeksnewagenda.gr, 2018). This correlates with the arguments made by Alacer and Chung (2007) of
academic activity. Furthermore, Electrek Reports stresses; “that Tesla has opened this R&D lab in Greece in
order to tap more into local engineering talent”. It has already proved to be advantageous for Tesla to use
the well-educated and skilled engineers from the National Technical University of Athens, considering that
their current three top electric motor designers, who have been working in their US departments, all
graduated from this university. By introducing the R&D lab in Greece, Tesla aims to retain and enhance
these technological scientists in the company, opening up for prospects of Greek scientists who live in the
country (Greeksnewsagenda.gr, 2018), which is one of the reasons why they opened this R&D lab. The
reasons behind this decision can be explained and connected and with arguments made by Cantwell and
Mudambi (2011), who claims that organizations can create value by entering local clusters to incorporate
new knowledge, enabling them to engage in diffusion processes of local knowledge. They furthermore
argue, that relational advantages of organizations can be achieved by differential capacities to participate in
business networks, ensuring access to channels for knowledge discovery. If the talents from Greece can be
involved in the R&D and manufacturing process, it can be beneficial for the company, in order to develop
their strong position in the global industry (Beauchemin et al. 2016). These benefits can be used following
the assumptions made by Cohen and Levinthal (1990), of absorptive capacity, which they describe as
abilities created by organizations who use prior related knowledge to identify the value of new information,
and incorporate this to commercial ends. This contributes to understanding why Tesla have opened their
R&D lab in Greece; as Cohen and Levinthal (1990) furthermore argues, research show that organizations
who establishes their own R&D are enhancing their ability to use external information and benefit from it.
However, Cantwell and Mudambi (2011), distinguishes between knowledge inflows; and outflows, which is
knowledge that is either gained internally to the benefit the organization and knowledge that is being

11 | P a g e
leaked. Tesla must be keen on controlling their flows of knowledge, in order to protect their intellectual
properties, especially now, that their patents are open.