Open Innovation and the Internet of Things: Strategic Connections

Open Innovation
in the Era of the
Internet of Things
Synopsis for the “Advanced Innovation” course,
Aarhus University.
Marco Tirelli

Marco Tirelli, Aarhus University 2
INTRODUCTION
..............................................................................................................................................
3

PROBLEM FORMULATION, METHODOLOGY AND DELIMITATIONS...................................................... 4
THE
CLOSED
MODEL
OF
INNOVATION
....................................................................................................
5

THE DECLINE OF THE CLOSED MODEL OF INNOVATION....................................................................... 6
THE
OPEN
MODEL
OF
INNOVATION
.........................................................................................................
7

THE EVOLUTION OF THE “OPEN INNOVATION” CONCEPT .................................................................... 9
OPEN INNOVATION AND DYNAMIC CAPABILITIES ............................................................................. 10
DIFFERENT
FORMS
OF
“OPEN
INNOVATION”
.....................................................................................
11

USER-DRIVEN INNOVATION............................................................................................................... 11
OPEN COLLABORATIVE INNOVATION ................................................................................................ 11
“HYBRID” MODELS OF OPEN INNOVATION ........................................................................................ 12
OPEN
INNOVATION
AND
THE
“INTERNET
OF
THINGS”
...................................................................
13

CONCLUSIONS
................................................................................................................................................
17

WORKS
CITED
...............................................................................................................................................
19

Introduction
Over the last decade, the concept of “open innovation” has certainly attracted a vast interest
from both academic and business communities, gaining a growing popularity since its first
conceptualization elaborated by Henry Chesbrough (2003). Taking his work as the main point
of reference, the open innovation paradigm can be interpreted as the antithesis of the
traditional, “closed” and vertically integrated model based on the internal management of the
innovation activities and the distribution of the related products and services. In contrast, in
the “open” paradigm, internal innovation is fostered and accelerated by purposive inflows of
knowledge from the external environment, while simultaneously enhancing the external use
of internally developed resources through selective outflows. As a result, “open” model has
become a new paradigm to manage and organize innovation, so that the subject has appeared
in thousands of articles, citations and conferences. Today, many consulting companies base
their strategic plans on its principles; likewise, large and small firms have adopted a more
open approach to manage the innovation process and conduct their R&D activities. Even
public institutions have started to adopt (at least formally) an “open” model in their programs
and policies. However, popularity is always a double-edged sword. In fact, the multitudes of
interpretations that have accumulated and overlapped over the years have prevented the
achievement of a clear and solid consensus about what is actually meant by “open
innovation”. Despite taking Chesbrough’s perspective as the main point of reference, a
consistent formulation has still not been settled among both scholars and business
practitioners. In contrast, semantic subtleties and divergent definitions of the original concept
have spread across the literature, extending its theoretical scope to such disparate areas as
engineering, computer science, or social, natural and physical sciences. In other words, the
idea of “open innovation” has been re-constructed, re-interpreted, re-formulated and re-
applied in an ongoing process of “sense-making”, leading to a constantly growing body of
researches, citations, definitions, conceptualizations. Furthermore, the new “open paradigm”
for innovation management has not been exempt from harsh criticisms. While many observers
have applauded and celebrated its brilliant and groundbreaking insights, others (Trott &
Hartmann, 2009) have firmly criticized and questioned its inherent novelty. Therefore, it
would be far beyond the possibilities and reach of the present paper to thoroughly recapitulate
the theoretical complexity of such a controversial topic.
At the same, during the last decade another concept has acquired growing popularity, namely
the so-called “Internet of Things” (IoT). This terminology refers to a (more or less “open”)
ecosystem of connected services and products, through the use of the Internet and wireless
communication technologies. In other words, physical devices across different product
categories are able to “communicate” through their embedded sensors and to share massive
amounts of data through cloud-based technologies. Therefore, the IoT is based on a new form
of machine-to-machine (M2) communication, which is increasingly sophisticated in the light
of the possibilities offered by network and cloud technologies. The “smart” label applied to
many devices nowadays, in fact, derives from it ability to provide wireless, digital and
basically instantaneous connection between objects whose productivity, efficiency and
performance are significantly enhanced. In short, the technological advancements in wireless
connectivity, processing and computing power, miniaturization, energy efficiency and
information management are revolutionizing the way products and services are used and
evaluated. This means that the IoT is also bringing a tremendous change in terms of
companies’ business models. The increasingly interconnected ecosystem of physical devices,
software applications and embedded service content implies radically new forms to create,

distribute and capture value. As a result, the competitive dynamics are evolving in many
industries, while their boundaries are increasingly blurring. This, in turn, requires the
integration of new capabilities and the strategic redefinition of a company’s core
competencies in the light of a better external alignment with such a changing environment.
Problem formulation, Methodology and Delimitations
Considering the contemporary evolution and widespread popularity of both Open Innovation
and the Internet of Things, the present paper aims to analyze their relationship in the light of
common strategic issues. For instance, open innovation typically requires the integration of
internal and external resources into platforms, architectures and systems, as Henry
Chesbrough (2012) has clearly stated: “There’s more value in creating the architecture that
connects technologies together in useful ways to solve real problems than there is in creating
yet another technological building block”. Therefore, the architecture and system-integration
capabilities required by open innovation emphasize its theoretical connection with the
competitive dynamics and business logics of the IoT ecosystems of open platforms and
interconnected products and services. Moreover, the open innovation model stresses the role
and importance of business models in order to support this purpose. Likewise, as previously
mentioned, business models are being transformed in the evolutionary context of IoT, rising
both new opportunities and strategic challenges for companies in dealing with this
technological and business development. Therefore, the present paper tries to analyze this
theoretical link and discuss the following problem statement: “Is there a strategic
relationship between Open Innovation and the Internet of Things?”
For this purpose, the work has been based on a desk research and analysis, starting from the
theoretical framework provided by the Advanced Innovation course’s curriculum. These
selected articles are used as the main point of reference, which is integrated with additional
material searched through the Aarhus University’s library, external academic databases (such
as EBSCO, Business Source Complete, Wiley Online, Science Direct, ProQuest, SAGE
Knowledge, Springer Link, et al.) as well as specialized websites and magazines on the
Internet (as Harvard Business Review, Forbes, McKinsey, Wired, et al.). Thus, following an
essentially inductive approach, I have considered the particular case of the IoT in order to
discuss and to reflect on its possible strategic relationships with the theory and models of
open innovation, that constitute the basis and the fundamental framework of this paper.
However, this work is clearly limited in being only an illustrative and tentative analysis,
without any claims to achieve an exhaustive and conclusive examination of such a broad,
complex and continuously developing theoretical subject, which remains an open-ended
dispute in both the academic and the business communities. Moreover, as previously
mentioned, the IoT constitutes the “case” in the light of which the open innovation’s
principles are analyzed, without a specific mentor company used as main reference.
Therefore, the purpose of this paper is to discuss and reflect on some of the theories and
models provided during the course, aiming to find theoretical links and strategic implications
regarding the multifaceted, contemporary and continuously evolving phenomenon of the IoT.

The closed model of innovation
The discussion of open innovation could not start but from Henry Chesbrough (2003), the
almost unanimously recognized “father of open innovation”, who explicitly presents its
conceptualization in antithesis to the “old” vertically integrated model of innovation
management.
This “closed” model of innovation is essentially based on the accumulation of exclusive
resources and the internal development of new ideas, in order to come up with brand new
products before competitors. In this case, the basic assumption is that early discoveries and
inventions usually ensure a key first-mover advantage in the commercialization phase.
Namely, time-to-market can be greatly reduced by stimulating continuous in-house research
and managing its internal development through a “funnel” and stage-gate organizational
model (Cooper, 1990).

Thus, the “closed” label derives from the strategic orientation aimed to carefully protect these
activities against external imitation. This way of conducting internal innovation, in turn,
confers an “entitlement” to successfully launch new products early in advance, thus outpace
competitors and finally achieve market leadership. Moreover, according to Chesbrough, the
almost exclusive reliance on internal resources is also reinforced, in the closed model of
innovation, by the presence of a specific virtuous cycle linking the investments in internal
R&D to a higher rate of innovation. In other words, the theoretical assumption is that the
industry-leading companies, which are able to invest massive amounts of resources in internal
development, also tend to come up with technological breakthrough and, consequently, more
successful product innovations. This, in turn, confers them larger market shares and higher
profitability. Hence, companies can re-invest these profits into further R&D injections in
order to re-fuel the process even more.
In addition, besides being the source of strategic advantage, internal R&D also represents a
significant entry barrier for potential contestants in the industry. This is clearly a
fundamentally “atomistic” perspective, by which every individual company should strive to
aggregate as much resources as possible for themselves, while carefully protecting its own
ideas against external imitation. Thus, the “closed” label refers to this “individualistic”
orientation characterized by an essential mistrust towards external ideas (the classical “not
invented here” syndrome), a lack of integration and a scarcity of cooperative exchanges
between companies. In short, competitive advantage does not derive from collaboration, but
from the financial strength, the level of investments, the organizational efficiency and the
possession of exclusive, context-based and protected know-how.
The decline of the closed model of innovation
Chesbrough illustrates some historical erosion factors to describe the “paradigm shift”
inducing the substitution of the “old and closed” model with the “new and open” approach to
innovation. In fact, in his opinion, these fundamental changes in the contemporary business
environment have disrupted the formerly cited virtuous cycle of the closed model. First, the
larger access to education has somehow “democratized” knowledge as an available resource
disseminated in the environment. Universities, professional schools and institutions, for
example, have increasingly formed new scientists, engineers and highly skilled prospective
employees entering into the job market. This means that know-how is not “elitist” as it used
to be. In contrast, the higher number of independent, qualified and skilled workers in basically
every industry has progressively taken the “locus of knowledge” outside of R&D
departments. In other words, the average quality of external sources of knowledge available in
a firm’s environment has significantly risen. At the same time, the job market’s flexibility and
the consequent higher mobility of employees across companies has undermined the
exclusivity of firms’ know-how and the permanence of their competitive advantage.
Organizational replacements and transfers inevitably generate leakages of precious

knowledge, when the skilled personnel take a consistent share of the firm’s competencies
with them. A second erosion factor concerns the role of private venture capitalists becoming
much more relevant than in the past. This means that they increasingly fund and allow
innovative start-ups (often created by former R&D employees such as scientists and
engineers) to independently develop specific projects and commercialize their ideas. This
clearly poses a serious threat to established businesses, as in the emblematic case of Xerox
where many of its spin-off companies have ultimately overcome its own market value.
Finally, a third factor is related to the technological advancements registered in almost every
industry. Time-to-market has shortened for many new products, requiring more flexibility as
well as the management of parallel processes of development and, consequently, the
involvement of different actors. This is clearly in contrast with the long-term investments and
linear internal development of the closed model. Similarly, the lifecycle of many technologies
has shrunk, so that they tend to become obsolete much faster than in the past. This also calls
for more flexible investments, as well as for joint developments in order to minimize risks
and financial exposure. In short, investing in long-term internal research is not at all a
guarantee to achieve market leadership, even less the higher returns expected. Furthermore,
the widespread diffusion of the Internet and social media (Chesbrough & Bogers, 2014) has
greatly increased communication and interaction, as well as knowledge access, sharing of
ideas and their distribution across many different actors worldwide. Crowdsourcing initiatives
are just an example of the many possibilities of joint development allowed by the
contemporary technologies.
The open model of innovation
In “open” contrast to the closed model, the concept of “open innovation” essentially refers to
a more distributed, interactive and collaborative approach to innovation, which contends the
traditional and vertically integrated model calibrated on internal focus and R&D activities.
Therefore, in its own nature, the logic of open innovation starts from the assumption that
useful knowledge is widely distributed across the firm’s environment. Gassmann & Enkel
(2004) have effectively expressed this point claiming that “the locus where knowledge is
created does not necessarily always equal the locus of innovation - they need not both be
found within the company”. As a result, no single company could actually innovate in
isolation, in spite of the amount of resources invested in the process. The “open” label thus
implies a fundamentally interactive nature of this approach, that is a much more collaborative
stance for managing the innovation process. Clearly, alliances and collaborations have existed
long before the first idea of open innovation. For example, the aspects of interaction and
interdependence had been described by Edquist (2001) as the fundamental features in a
“system of innovation”, “where innovations are considered to be determined not only by the
elements of the system but also by the relations among them”. However, what has historically
changed compared to the past is the intentionality by which companies are now actively,
continuously and selectively scanning the external environment to find new connections and
opportunities. This underlies a subtle but important aspect, meaning that open innovation is
not at all a trivial, casual or improvised access to external and readily available opportunities.
It is not just something that every companies can achieve by simply being more collaborative.
On the contrary, it is a highly sophisticated and strategic process that requires specialized
skills, capabilities and expertise to search and evaluate key assets and resources, while
rejecting and discarding many others in the light of the strategic fit. This concept has been
effectively expressed in one of the most famous definition of open innovation, as “the use of
purposive inflows and out-flows of knowledge to accelerate internal innovation, and expand
the markets for external use of innovation, respectively” (Chesbrough, 2006). In this regard,

the interactive dynamic of the open process can be understood as a double movement of
exchanges between all the different actors involved. The “outside-in” direction is probably
the most obvious, meaning that the firm should actively scan its environment looking for new
ideas, resources and technologies that can be brought inside, assimilated and integrated into
its value chain. This search can follow different ways such as scouting activities,
crowdsourcing initiatives, IP acquisition, as well as by harvesting and absorbing new
knowledge through spillovers, as in knowledge-based clusters, as well as through specific
partnerships and collaborations (Mudambi & Tallman, 2010). The “inside-out” flow is less
common, instead, since it refers to the free disclosure and/or commercialization of internal
assets to external players. The relative novelty of this process underlines the change
represented by open innovation compared to previous models. Whereas know-how and
technologies used to be almost exclusively directed towards internal processes, fearing
imitation from competitors and erosion of distinctive advantages, this inverse and “outbound”
direction has gained much popularity in the last decade. The number of companies willing to
“open” their boundaries is consequently increased. In fact, this process allows the firm to
exploit and capitalize on unused assets such as patents, inventions, technologies, scientific
discoveries, projects, intrapreneurial initiatives or skunk works. For instance, Wallin & Von
Krogh (2010) point out that companies should define organizational rules to out-license
unexploited product technology and unsuccessful projects, while simultaneously releasing
time and resources for internal R&D employees to deal with external innovation. Moreover,
the social impact of this knowledge sharing resides in the possibility for ideas to spread and
multiply in the external environment. As a result, the diffusion of specific technologies can
foster the adoption of related products, as well as inducing developments in terms of
legislative framework and complementary infrastructures in the industry. The recent decision
of Toyota and Tesla to grant free access to their fuel cell patents (Ziegler, 2015) could be
interpreted as a strategic move in this sense. Therefore, it is clear how the “open” firm can
gain a huge benefit in the long run, when further ideas, new forms of knowledge combination
and improved technologies will generate innovative opportunities for the company itself. In
fact, innovation often consists of leveraging and improving the inventions and discoveries of
others, where the whole combined and integrated value is much higher than the simple sum of
the individual and separate contributions. As far as the inside-out process is concerned,
Gassmann & Enkel (2004) underline the importance of a company’s “multiplicative
capability”, in order to successfully codify knowledge and transfer internal resources to a
strategic selection of partners that are also able to multiply them in the business environment.
This aspect is particularly crucial to commercialize ideas and exploit unused resources.
Moreover, the authors have added the “coupled process” as a third component to express and
describe the necessary integration of in-flows and out-flows of knowledge along the whole
value chain of a company. Strategic networks, joint ventures and alliances with
complementary companies are the main mechanisms for this purpose, emphasizing the
importance of building, maintaining and developing relationships with the strategic partners.
Moreover, the coupled process is particularly critical in the context of industry innovation, for
example in terms of joint development and establishment of a common technological standard
or product design. This may concern the emerging industries, but can also be relevant for
more mature sectors where common standards and shared technologies allow higher
economies of scale and scope, better product compatibility and deeper integration. However,
these relationships can also assume many other forms, such as consortia of competitors,
networks of suppliers and customers, partnerships with universities and research institutes,
formal and informal collaborations with start-ups or independent actors such as scientists,
entrepreneurs or engineers. Procter & Gamble’s “Connect + Develop”, for instance, is a
notorious model of integration with re-sellers and distribution networks aimed to develop,

commercialize and distribute new products through cooperation and joint exploitation of
complementary competencies. As an example of the level of sophistication, skills and
expertise required to successfully deal with open innovation, P&G has instituted a full-time
dedicated department (named “Global Business Development”) in order to scan the
environment, interact with potential partners and enhance this open and collaborative strategy.
The point is that all these forms of cooperation deeply enhance mutual learning through an
intensive process of knowledge sharing, which has usually a long-term horizon and a high
content of tacit and implicit knowledge that could not just be bought in the marketplace.
Moreover, companies are able to accelerate internal innovation by accessing external
intellectual properties while simultaneously sharing those developed internally, as far as the
more codified and explicit knowledge is concerned.
The evolution of the “open innovation” concept
During the last decade, different definitions and interpretations of “open innovation” have
accumulated from the multitude of articles written on the subject. Henry Chesbrough’s most
recent definition is the following: “a distributed innovation process based on purposively
managed knowledge flows across organizational boundaries, using pecuniary and non-
pecuniary mechanisms in line with the organization’s business model” (Chesbrough &
Bogers, 2014). Compared to previous conceptualizations, this definition adds and underlines
two important elements: the role and importance of a company’s business model and the
presence of non-pecuniary mechanisms of knowledge flows. With regard to the latter,
Dahlander and Gann (2010) have provided an important framework, where they identify non-
pecuniary mechanisms in the activities of “sourcing” and “revealing” respectively for
inbound and outbound processes. The first refers to the fact that, besides acquiring IP and
other valuable resources through market transactions, firms can also tap into externally
available sources of knowledge, in a more informal manner, without monetary expenses.
Similarly, companies do not necessarily have to sell their unexploited patents. Revealing
concerns the act of selectively disclosing internal assets without an immediate financial
remuneration, in order to achieve higher-level and more indirect benefits in the long run.
The other aspect of open innovation that deserves particular attention is the importance of the
company’s business model. This can be interpreted as the firm’s strategic “architecture” that
integrates external and internal sources of knowledge into the process of value creation. Even
though the business model can actually be more or less explicit in a firm’s strategy, this point
relates to the previously mentioned importance of strategic management for both inbound and
outbound purposive processes of innovation. In other words, the firm’s business model is the
fundamental framework to select, evaluate and decide which key resources should be brought
inside and/or revealed outside. In this sense, the business model has a double role. From an
outside-in perspective, it determines how value is captured and integrated into the firm’s
knowledge base. In the light of the strategic fit, it serves as a fundamental filter for external
ideas and technologies. From an inside-out viewpoint, it highlights how value is created and
delivered, thus clarifying which resources are critical for the firm’s value chain and which
others can be let flow outside without eroding core competencies and competitive advantage.
Therefore, the firm’s business model is also the frame of reference to identify the specific
mechanisms in order to commercialize internal resources and successfully transfer them to the
external environment. Moreover, in its very nature, the business model is not fixed, but
should be adapted in the light of evolving circumstances in the environment. In this sense,
facing changed dynamics in the industry, the firm can better “fine-tune” the strategic fit of its
innovation process, thus creating and capturing more value, by renovating and better aligning
its business model. A clear example is the revaluation of the so-called “false negatives”,
namely ideas and projects that had been previously discarded in the light of different

competitive assumptions. Florén and Frishammar (2012) cite the example of Xerox to
underline the importance of internal and external alignment to capture value from
innovation: despite the state-of-the-art technology and massive investments in its “Palo Alto
Research Center”, the company stunningly failed to recognize the value of many projects that
were assessed according to the internal lenses of the current business model. This case is even
more emblematic considering that many of these spin-offs, as previously mentioned, actually
ended up achieving a higher overall market value than Xerox itself. Moreover, the authors
suggest a framework that emphasizes the importance of a firm’s strategic alignment with both
the internal and external environment in order to successfully perform product innovation.
The internal perspective refers to the new idea’s fit with the firm’s strategy and core
competencies, for example through the products portfolio’s management. The external
perspective deals with the need to consider competitive product offerings and technological
developments in the industry. In short, most companies tend to actively screening and
continuously refining new ideas and “false positives” following the classical “stage-gate
model”, but in doing so they also tend to overlook the equally relevant false negatives.
Open innovation and dynamic capabilities
In contrast, the firm’s ability to modify, reconfigure and align its business model has been
precisely included as an example of dynamic capabilities by Teece (2007), who also claims
that open innovation motivates firms to “sense” and “seize” external opportunities. As
previously mentioned, the idea that an individual firm cannot innovate in isolation induces it
to scan the environment and look for external opportunities. In other words, this strategic
orientation essentially broadens and shifts the focus of innovation from the internal resources
and technological competencies to the wider sources of knowledge distributed across the
business environment. This evidently elicits a higher sensibility from the innovating company
and stimulates a greater “openness” towards external contributions. It encourages the adoption
of best practices, the development of specialized skills and expertise, the establishment of
dedicated teams, the allocation of investments aimed to integrate external and internal
technologies. New forms of collaborations are inspired, while companies increase the level,
frequency and content of communication and intensify their interaction. With a reference to
some of the most cited slogans in the literature, we could claim that the corporate strategy is
induced to replace the atomistic “not invented here” kind of mentality with a more
cooperative, interdependent and realistic “not all the smart people work for us”.
However, despite recognizing and emphasizing the value of external resources, open
innovation does not discard at all internal R&D activities. It would be a clear strategic
misunderstanding to think that companies could “delegate” and completely outsource
innovation efforts, researches and investments to the outer network. No company can actually
stay competitive while relying almost exclusively on the discoveries and innovation practices
of others. For instance, the outsourcing of production activities has been described as a
potential cause of competencies’ erosion and innovation capabilities’ destruction over time
(Kotabe, Mol, & Ketkar, 2008). In short, external exploitation requires internal expertise. The
internally developed know-how and innovation capabilities are the necessary prerequisite in
order to selectively assess, correctly evaluate and strategically integrate external know-how
into the specific firm’s business model. Therefore, the “open” nature of innovation is not a
theoretical justification for its disaggregation or subcontracting. On the contrary, internal
R&D maintains a crucial role for the firm’s “absorptive capacity”, defined as the ability “to
recognize the value of new, external information, assimilate it, and apply it to commercial
ends” (Cohen & Levinthal, 1990). This means that the open approach does not imply a
replacement, but instead a strategic combination of internal and external knowledge.
Moreover, Chesbrough (2012) underlies the importance of “creative abrasion” for successful

knowledge transfer between companies. The term has been coined by Jerry Hirshberg (1999),
the founder of Nissan Design International, to signify the creative energy and innovation
potential aroused by the conflicting and constructive interaction between people with different
ideas, perspectives, values, skills, attitudes and predispositions. This is particularly evident in
the case of knowledge integration between external and internal sources. In this sense, the
open innovation’s dynamism is based on the proactive, continuous and face-to-face
collaborations between people moving across different organizations. Therefore, companies
should enhance and favor this dialectic process through specific mechanisms and
organizational figures such as the so-called “T-shaped managers” (Hansen & von Oetinger,
2001) and other knowledge-integrating specialists. In short, all these points contribute to
highlight the fundamental theoretical connection between open innovation and the dynamic
capabilities of a firm, defined as “the sensing, seizing, and reconfiguring skills that the
business enterprise needs if it is to stay in synch with changing markets” (Teece, 2010).
Different forms of “open innovation”
An important theoretical distinction concerns the ambiguity and possible misinterpretation in
the light of other and similar concepts related to the “open” approach to innovation
management. For example, Baldwin and von Hippel (2011) have emphasized the distributed
nature of innovations, more than their exclusivity and commercial exploitation by the firm. In
particular, with regard to the innovation’s design (defined as “the set of instructions that
specify how to produce a novel product or service”), they contend the traditional producer’s
dominance in its development. In fact, according to the authors, the classical “producer
innovator” can be challenged and even displaced in the eyes of potential customers, because
of two main alternative forms of innovation processes: by individual users and through open
collaborative projects.
User-driven innovation
According to the authors, “user-driven innovation” refers to individual agents (firms or
people) that are directly involved in the process, meaning that they expect to benefit from the
use of the innovation, regardless of its commercialization. The difference is that traditional
firms are not interested in the “utility” of innovation in itself; instead, they aim to achieve a
profit by selling innovation-related products to customers. In other words, they only indirectly
benefit from the invention, through the monetary filter of a market transaction. In contrast, the
reason to innovate for single users is not the financial reward, but the immediate advantage
implicit in the solution itself. Typical examples are the creation of a new item, machinery or
industrial tool, as well as the ideation of a better manufacturing system by a small firm that
wants to directly enhance its production efficiency. Similarly, user-driven innovation may
concern a private individual who combine existing products and components, in order to
create an innovative, homemade and specific solution for his needs (as a sport equipment).
Open collaborative innovation
The second alternative form to producer innovation is the “open collaborative innovation”,
which is based on the concepts of “shared work” between different contributors and the
“free revealing” of its outcomes. In particular, the innovation-related information has a
fundamentally “public” nature, meaning that it is non-rivalrous and non-excludable: the open-
source softwares’ development is the archetypal example in this sense, even though is not
always as such. The non-rivalry profile of the participants is, in fact, the prerequisite for their
collaboration. They are interested in the shared development and innovation of the design, not
in its commercialization. This point underlines a subtle but fundamental difference compared
to Chesbrough’s interpretation of “openness” as organizational “porosity” and “permeability”

in terms of inflows and outflows of knowledge across firm’s boundaries. Firms, however, are
businesses and, as such, ultimately remain interested in the profits and economic benefits (for
instance, an acceleration of internal R&D) deriving from open innovation. According to
Chesbrough, in other words, users and firms play complementary roles. Lead users are not
involved in the entire innovation process, but essentially in the early stage, when they can
bring about a significant contribution in terms of ideas and invention. However, in the
subsequent phases, the firm assumes the leading role in the management and control of the
innovation process, in order to develop the project further and achieve the necessary “critical
mass” to ensure profitability (Chesbrough & Bogers, 2014). In particular, the firms’ business
models are the specific mechanisms that allow them to perform this task: they are the
necessary means to create a profitable value chain and to obtain the financial resources and
capital investments needed to increase the scale of the project. Moreover, Chesbrough points
out that the other fundamental element for this purpose is represented by the intellectual
properties, which are still necessary even in the case of open source projects. In short,
Chesbrough clearly underlines that “open” does not mean “free”.
In Baldwin and von Hippel’s interpretation of collaborative innovation, in contrast, the
“open” nature refers to free sharing between contributors. The most evident benefit, in this
sense, is the possibility of leveraging over each other’s inputs, suggesting feedbacks,
stimulating participation and working together towards shared improvements. In other words,
the improvements are typically related to the speed and efficiency in which problems are
detected, analysis formulated, solutions suggested, modifications implemented and feedback
distributed across the open and collaborative community.
“Hybrid” models of open innovation
Finally, there are also many “hybrid” innovation models that combine, in different forms,
specific elements and features of the three main categories mentioned by Baldwin and von
Hippel. Crowdsourcing, for instance, is cited as a combination of open collaborative
innovation (because of the contributions and the shared effort coming from external actors
that often do not profit in terms of financial rewards) and producer innovation (since the
output are owned and usually commercialized by the sponsor company). Because of this latter
feature, crowdsourcing can actually be interpreted as a form of “closed” collaborative
innovation. Chesbrough, for instance, highlights the fact that the company starting the
crowdsourcing initiative is not supposed to completely disclose the process or to reveal all of
the outcomes (which is an example of “selective revealing”).
Similarly, innovation platforms can be cited as another example of hybridization. In this
case, a united product’s architecture may be subdivided and transformed into a new
combination of large and small components. This modularized design integrates the
producer’s innovation (with regard to the main and stable platform) with external
contributions (for the single, smaller and variable modules) through open collaborative
projects or even from individual users. In this regard, Chesbrough (2012) indicates that the
higher value resides in the architecture that connects technologies together rather than in the
additional “building block” in the system. In other words, the crucial capabilities concern the
system integration knowledge, especially in the light of increasingly “modularization” in
many industries. This “semi-open” form of innovation allows, for instance, the creation of
personalized and specific interfaces based on the same technological standard. In fact, the
usual (but not exclusive) context for this type of innovation is that of services’ innovation.
Moreover, this form of innovation is particularly useful and effective when entry costs are
low (in terms of resources and know-how required to participate) and, at the same time,
network effects are high (in terms of exponential benefits coming from the multitude of
contributions). Android Wear and Tizen, respectively Google’s and Samsung’s operating

systems for smartwatches and other “wearable devices”, are examples of “hybrid innovation”,
as they are open-source models with closed-source components. In this way, both companies
maintain control over the core elements of the software, while allowing personalization from
independent developers and users. Similarly, in the case of social media networks such as
Facebook and YouTube, users create the personalized content based on a common
technological platform, likewise on Amazon, eBay or Tripadvisor.
Open Innovation and the “Internet of Things”
In spite of the widespread and contemporary popularity of the “Internet of Things” (IoT), this
phenomenon is older than one would usually imagine, as it dates back to 1999. In fact, its first
definition is unanimously attributed to the British technology pioneer Kevin Ashton, co-
founder of the Auto-ID Center at MIT, who envisioned the “revolution” that inter-connected
devices would have represented for both technologies and businesses.
In particular, the expression “Internet of Things” condenses the two main features of this
relatively new phenomenon. On the one hand, the Internet is the interconnecting technology,
which is the fundamental mechanism that allows the interactive and seamless information
sharing that constitutes the essence of this new concept. However, it should not be considered
as its main element. On the other hand, the “things” are usually identified with the growing
number of “smart” products and devices that, with their constantly changing nature, represent
the true innovative aspect of this phenomenon. In fact, the Internet technology has been
available for decades already. For example, in the early 1980s, programmers at Carnegie
Mellon University were able to check the availability of Coke drinks from an Internet-
connected distributor, which is considered the first ancestor of modern Internet-connected
devices. (Doctoroff, 2014) Conversely, the new “smart and interconnected” products
constitute a quite radical innovation in terms of both performances and competitive dynamics
in the industry. The higher quality and the more sophisticated functioning of these products
are just the most evident and flamboyant features, but their value reside in their innovative
capabilities to interact, communicate, generate data and share information. This is precisely
what makes these products “smart”. In short, in order to deal with this type of innovation,
companies must consider the evolution in the competitive logics and dynamics in the related
industries, besides the advancements (in terms of speed, data transmission and efficiency) of
the Internet technology in itself. This latter only represents the means, but the real value
derives from what it allows. Using a metaphor, we could say that the value of a speech is in
its meaning, not simply in the mouth itself.
Moreover, the Internet is only the most evident technology involved in this new model, but is
definitely not the only one. In fact, the “IoT paradigm” is the result of the convergence of
many other contributions, such as those coming from wireless technologies and
microelectromechanical systems. For example, sensors, circuits, microcomputers and batteries
are increasingly smaller and cheaper, but at the same time more efficient and characterized by
higher performance. They can be integrated into products or components that, in turn, are part
of other devices. Other technological advancements concern the increasingly lower costs of
wireless connectivity, the enhanced efficiency and security of communication protocols (as
the IPv6) as well as the possibility of storing, elaborating and transmitting larger amounts of
data. In other words, the current technological environment is the result of a multitude of
different innovations: these, in turn, have produced synergies and mutual improvements that
have allowed, at the same time, the physical, technical and economical feasibility of the
innovative, interconnected and smart devices at the base of the IoT. Obviously, this
technological convergence is not the result of chance or improvisation. Instead, it is the
“concerted” outcome of countless cooperative initiatives, massive joint efforts, mutual

sharing and amalgamation of knowledge across a large range of different industries. Open
innovation strategies have had a crucial role, for example, in the combination and
improvement of existing product categories to create new ones, in the enhancement of
compatibility between different categories as well as in the establishment of new and common
technological standards across different industries. Wi-Fi, Bluetooth or USB are typical
examples of technological “bridges” across different product categories as well as industries
(as in the case of cars and home appliances communicating with smartphones). For instance,
today is possible to start phone calls or answer messages while driving by sending vocal
commands to the electronic system of the car. In short, the exchanges of knowledge through
coupled processes of inflows and outflows of resources between companies (in the same as
well as different industries) have allowed them to pursue joint developments and to greatly
integrate their capabilities. From an overall perspective, these collaborative efforts have been
aimed towards common, synergetic and mutually reinforcing technological advancements in
terms of miniaturization, productive efficiency, energy saving, interconnectivity, data
management, privacy and security systems, etc. In this sense, open innovation continues to
represent the fundamental framework for the constant evolution of the IoT.
Similarly, the “things” are not limited to objects and devices, but may also comprehend
people and animals, for instance when they have interconnected sensors installed into them:
high-tech surgery (such as cardiac monitors and implants) or biochip-enhanced farming are
just two examples of these new areas of technological development. In any case, what
characterizes “smart things” is the presence of a sensor, which gathers and assesses data, then
transfers them to other sensors. This link between sensors and devices is the essence of the
IoT, whose value resides in the collection, elaboration and transmission of data. This, in turn,
implies the existence of an infrastructure that allows to receive, interpret and utilize data in
real time and the consequent interconnected leveraging of information. Cloud-based
applications interconnected by Internet and wireless technologies have a crucial role in this
sense, for their capability to manage massive amounts of data “outside” from the specific
devices. For example, cloud-based technology is frequently employed in the weather
monitoring systems, where sensors gather data about temperature, pressure, humidity, air
quality, UV levels, etc., and share them wirelessly across an interconnected network of
applications that provide the related information directly to their users. In short, cloud
platforms are the fundamental “bridges” that connect applications and smart devices to the
(multiple) networks that constitute the essence of the IoT. This highlights an important
difference compared to “traditional” machine-to-machine (M2M) communication, in terms of
the massive amounts of data manageable and, consequently, the quantity of information that
can be potentially generated. In contrast with the limits of traditional M2M communication,
in the IoT context data are collected and stored into cloud databases, then the results are
visualized in real-time through the cloud-based applications that provide the final (and
personalized) information to users (Bahga & Madisetti, 2014). This allows big data analytics
as well as the use and management of unprecedented amounts of information, stored in the
“cloud”, from the remote devices that represent the nodes of the interconnected network.
Furthermore, machine-to-machine “smart” communication has clearly a distinctive value
in terms of the accuracy, efficiency, complexity and punctuality of the information provided
by the smart devices. This translates into higher service quality and incomparable savings of
costs, time and resources. Clearly, these innovative technological possibilities generate new
opportunities in the “Era of Information”, that could not be achieved through simple human-
to-human (H2H) or human-to-machine (H2M, such as in the case of manual data entry) forms
of communication. For example, Siemens is able to foresee and prevent expensive failures in
its customers’ facilities (such as industrial machines, IT management systems, computer
networks, etc.) as well as to perform remote-maintenance service through its “common

Remote Service Platform” (Holm, 2010). This infrastructure allows the company to
automatically manage, elaborate and utilize such a high quantity of data that it is not only able
to serve thousands of customers simultaneously, but also to predict potential problems in their
facilities. In other words, maintenance is both remote and proactive. Likewise, new
technological improvements have allowed to create “smart cement” through piezoelectric
composites that are used as sensors on roads and bridges to monitor traffic load and
congestion, prevent potential structural failures and transmit precise and real-time information
to the connected central system (Zhang et al., 2015). Similar sensors in the roads’ concrete
could also detect the presence of ice and communicate this information wirelessy to the
driving cars. Moreover, if the driver does not decrease the speed, the electronic system of the
car could take control and slow down in his place, as it already happens in the case of
technologies such as the Electronic Stability Control (ESC) and Emergency Brake Assist
(EBA). In this case, data are captured through the sensors and transformed into information,
which is then translated into action without human interaction, that is a decision based on the
simultaneous elaboration and analysis of data. One of the many examples that could be cited
in this field is the growing area of “structural health monitoring” (SHM), where sensing
systems have greatly been enhanced by recent technological developments. These have
already generated vast improvements and promise to create massive opportunities for future
progresses in terms of infrastructure reliability, customers’ safety and the overall system
performance. (Ceylan, 2013) For instance, Ericsson has developed a cloud-based platform
named “Connected Traffic Cloud” that support sharing of information between connected
vehicle, drivers and public authorities in order to make road traffic more efficient and safer.
This system, which allows to prevent collisions and traffic congestions sharing real-time data
through mobile connectivity, is the result of a joint effort in terms of open innovation. In fact,
the result has been produced by the collaboration and knowledge integration between
specialized companies and experts from numerous areas, such as cloud service management,
software and applications development, data and connectivity management, microelectronic
components, GPS and wireless technologies, cyber security, systems integration services…
and the exhaustive list would certainly be much longer. Moreover, besides the exchange of
information between road infrastructures and the remote maintenance systems, new
possibilities are envisioned also for consumer markets. In the automotive industry, for
instance, Volvo has developed a cloud-based service aimed to provide car-to-car
communication. A pilot test has been already launched in Norway and Sweden, where
interconnected cars are able to detect the presence of dangerous road conditions through their
sensors and to immediately notify near-by drivers (Saran, 2015). In addition, Volvo has also
developed a cloud-based safety system that prevents road collisions with cyclists, through a
GPS-connected app that sends information directly to the cyclist’s helmet. (Murphy, 2015). It
is clear, thus, how reciprocal “contaminations” and improvements across different (but
increasingly “smart” and interconnected) industries can foster both product and service
innovation, creating entirely new industries. The “smart” label has already been applied to the
most diversified industries, from wearable devices to cars, from home appliances to industrial
machineries, from energy to control systems, from public infrastructures to entire cities.
Inevitably, though, these technological evolutions not only promise new opportunities for
safer, smarter and value-enhanced consumption of products and services, but they also face
new threats, challenges and criticisms. “Car hacking” and “cyber attacks” (Hutchinson, 2014),
for instance, have been indicated as the other side of the road security medal. However, it is
reasonable to expect that these potential downsizes will be taken into consideration by
enterprises involved in many industries and, ultimately, they will further stimulate the
technological improvements in terms of data protection and security systems. As far as
information technologies are concerned, for example, Microsoft has established numerous

partnerships with complementary companies (such as security specialists, networking
operators and Internet service providers), as well as with its own competitors (in areas such as
firewalls, anti-virus and spyware systems), in order to build common security platforms and
co-develop personalized solutions for users in a context of open innovation (Chesbrough,
Vanhaverbeke, & West, 2014).
The strategic moves and financial investments made by the biggest multinational corporations
in many industries corroborate the importance of the link between open innovation
approaches and the multivariate area of the Internet of Things. In the summer 2014, Samsung
has announced the $200 million (Tilley, 2014) acquisition of SmartThings, an IoT-leading
and open platform for apps-controlled home appliances and consumer electronics. This
strategic move appears to be a clear effort from Samsung to acquire capabilities in the IoT
context. In fact, the value of SmartThings is its capability to sustain and connect an open
ecosystem of partners, researchers and developers specialized in producing smart devices and
cloud apps. Moreover, even though the company will formally continue to operate as an
independent entity, it will also become part of Samsung’s “Open Innovation Center” (OIC)
that has the role of creating, developing and integrating services’ innovation into Samsung’s
physical devices, but also of encouraging common standards in the industry. For this purpose,
the center actively cooperates with a network of complementary companies, startups,
independent experts and entrepreneurs. These are also supported through the so-called
“Samsung Accelerator”, which is a program that enhances specific projects and provides
resources, capital and technical support to sustain software and services’ innovation as well as
their integration with Samsung products.
Similarly, Google is following a strategy that aims to combine elements of IoT and open
innovation. Besides the internal development of products such as Google Glass and services
such as Google Now, the company has clearly demonstrated its intentions to significantly
invest in this direction with some key strategic choices, such as the $3.2 billion purchase of
Nest, a company specialized in smart home appliances, and the following acquisitions of
other IoT-leading companies and startups, such as Dropcam and Revolv. Moreover, Google
actively supports specific organizations and platforms aimed to foster the IoT environment,
such as the ambitious and experimental project “The Physical Web”. In this open and virtual
community, developers and entrepreneurs are joining forces and ideas to enhance interaction
between users and smart devices in a continuously expanding number of circumstances and
contexts. Another project supported by Google is the “Thread Group” open alliance, which is
an industry group that gathers and stimulates cooperation between many leading companies in
the IoT development, such as Samsung itself. Between the goals of this initiative there are, for
instance, the setting of new and common standards in order to avoid compatibility problem
and, at the same time, enhance reliability, integration and security between smart devices
from different brands and industries. For instance, the Thread Group has declared1
to aim at
developing a new wireless protocol in order to increase the compatibility, scalability and
energy-efficiency of its IoT network. Likewise, many other open alliances and industry
consortia have risen to foster the IoT development through cooperation and common efforts.
Sometimes these consortia overlap, sometimes deal with complementary aspects of the IoT.
In any case, these initiatives typically aspire to enhance connectivity between smart devices,
improve compatibility between different product categories, integrate complementary systems
and harmonize technological standards by stimulating open innovation activities and
supporting open source communities. An example is the “AllSeen Alliance”, a non-profit
open source consortium recently joined by Microsoft and Bosch, that is explicitly dedicated
“to driving the widespread adoption of products, systems and services that support the
1
http://bit.ly/1cWtycd

Internet of Everything with an open, universal development framework that is supported by a
vibrant ecosystem and thriving technical community” (as stated in its official website).
However, in spite of its capabilities in terms of design and engineering, Google’s strategic
choice of buying another company such as Nest highlights the extreme difficulty of
developing a IoT-based business model through internal development and organic growth. In
this sense, according to Gordon Hui (2014) the main problems derive from the need of
“designing for convergence” and “scaling an ecosystem” of connected products and services,
linking software and product development capabilities over an increasing number of
intercommunicating devices. In particular, this requires three main strategic challenges. First,
visions must be linked and developed in the light of real customer needs, in order to avoid the
potential trap of technological overshooting: it is still a matter of providing a solution to users,
instead of just following excessively ambitious and futuristic conjectures. Second, physical
and digital development must be integrated, which requires specialized skills in order to
create interconnected user experiences. And finally, also different business models should be
integrated and “bridged”, since physical and digital products imply different value chains and
ways of achieving profitability. In other words, revenues and costs deriving from the service
component provided by the software and the devices’ manufacturing production must be
combined and assessed in an integrated perspective. In conclusion, these extremely
problematic aspects in integrating different business models for the IoT ecosystem could also
represent its crucial hindrance for further developments and the main limitation for the short-
term impact of IoT devices.
Conclusions
In conclusion, many technological developments across different industries have converged
and produced new ecosystems of “smart” products and services. These, in turn, generate new
opportunities for further developments in terms of enhanced performances and productivity,
as well as for the emergence of entirely new industries. As a result, this self-reinforcing
virtuous cycle is profoundly changing the competitive logics and dynamics of many business
contexts. The consequence is that their boundaries are increasingly blurring, in the light of
new combinations of functions and values. This widespread business reconfiguration,
enhanced by the Internet and wireless technologies in the IoT framework, means that the
competitive arena where a company used to excel or simply operate can now change much
faster and in more radical ways. Therefore, companies need to envision and delineate strategic
reconsiderations of their competitive advantages and capabilities, in the light of the new
opportunities (and threats) provided by both open innovation and the IoT. In fact, traditional
value chains are increasingly challenged, threatened and even disrupted, so that companies
need to strategically redefine how they create value and which are the capabilities that ensure
and sustain their competitive advantage, in the light of this interconnectivity. Put differently,
the ecosystems of smart devices implies a substantial evolution regarding the nature of
competition itself, in terms of new strategic ways to create, deliver and capture value (as well
as to cooperate with external partners). As a result, business models must be reformulated,
adapted and aligned to these changed circumstances in the business (but also socio-economic)
environment. In particular, the problems in terms of integrating service-based and product-
based business models suggest that the preferred strategic growth in a IoT context, even for
leading and giant companies such as Google, Samsung or Microsoft, is through acquisitions,
networking (as in industry consortia) and strategic partnerships such as those of Google with
LG, Motorola or Asus with regard to its Nexus smartphones and tablets. This clearly
highlights the new opportunities offered by the open model of innovation.

Moreover, large amounts of data necessitate innovative and different management practices
in order to be transformed into precious information, depending on how they are handled,
elaborated and integrated into the existing value-chains. This also implies a reconsideration of
how relationships with partners and other strategic actors in the network are established,
managed and leveraged, as they need to be reinterpreted and reformulated in the light of
changing (and often overlapping) business contexts. Clearly, since industry boundaries
evolve, the current competitive advantages and core competencies are challenged, which
means that the role, profile and ultimately leadership of specific companies mutates too. The
interconnectivity of smart devices means that the increase in their functions and the
improvements of their performances can be theoretically exponential. This point holds
consequences in terms of both the nature of capabilities and the value of core competencies in
current industries. Depending on the specific markets in which a company operates, the
blurring boundaries between many product categories may translate in both new opportunities
but also threats for competitive advantage (and even more for market leadership).
Consequently, the assumptions and logic of open innovation acquires even more relevance
with regard to the (potentially) revolutionary changes that the IoT is inducing in the business
environment of many industries. As previously described, the company’s business model has
a crucial role in open innovation: for example, concerning the alignment with both the
internal processes and the specific external context, as well as the capabilities to integrate
(internal and external) knowledge and define how value is created, delivered and captured
from innovation activities. Moreover, the innovative, interconnected and complex systems in
which data are stored, transferred and shared implicate equally different ways to obtain,
aggregate and use information. This, in turn, suggests a strategic reconsideration for
companies about how knowledge can be achieved, developed and ultimately integrated. In
other words, the new possibilities of data sharing in the IoT context influence and greatly
enhance a consistent part of the inflows and outflows of knowledge in the open innovation
framework. In short, we are just glimpsing the dawn of entire new industries that may emerge
in the future, because of new opportunities to create value from powerful hi-tech
advancements. These span from the improved productivity of current products and services to
the creation of entirely new categories of products and services in many different areas. For
instance, according to a McKinsey (2013) report2
, the potential economic impact of the IoT is
expected to amount between $2.7 and $6.2 trillion per year by 2025, with applications in a
vast range of sectors and industries from healthcare to control systems, from manufacturing to
energy efficiency, from urban infrastructure to remote security and control, from public
transportation to agriculture. In particular, the “leveraging effect” of the IoT context for open
innovation derives from its potential to create an increasing number of combinations between
products, services and technologies, that is an exponential growth in terms of synergies,
mutual learning and interrelated improvements across industries. The more “smart” products
and services interconnected in the “open environment”, the higher the value they can provide
to customers and users, the higher the network effects of their interconnectivity. At the same
time, because of the parallel evolution of increasingly “smart” industries and their reciprocal
improvements, the speed itself of technological developments will be consequently increased.
2
http://bit.ly/1bh3LqV

Works Cited
Bahga, A., & Madisetti, V. (2014). Internet of Things (A Hands-on-Approach). VPT.
Baldwin, C., & von Hippel, E. (2011). Modeling a paradigm shift: from producer innovation
to user and open collaborative innovation. Organization Science , 1399–1417.
Ceylan, H. (2013). Highway Infrastructure Health Monitoring Using Micro-
Electromechanical Sensors and Systems (MEMS). Journal of Civil Engineering and
Management , 19 (1).
Chesbrough, H. (2006). Open innovation: A new paradigm for understanding industrial
innovation. New York: Oxford University Press.
Chesbrough, H. (2003). Open Innovation: The New Imperative for Creating and Profiting
from Technology. Boston: Harvard Business School Press.
Chesbrough, H. (2012). Open innovation: where we've been and where we're going.
Research-Technology Management , 55 (4), 20–27.
Chesbrough, H. (2011). Open Service Innovation. San Francisco: Jossey‐Bass.
Chesbrough, H., & Bogers, M. (2014). Explicating open innovation: clarifying an emerg-ing
paradigm for understanding innovation. In: Chesbrough, H., Vanhaverbeke,W., West, J.
(Eds.), New Frontiers in Open Innovation. Oxford: Oxford University Press.
Chesbrough, H., Vanhaverbeke, W., & West, J. (2014). New Frontiers in Open Innovation .
Oxford: Oxford University Press.
Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on
learning and innovation. Administrative Science Quarterly , 35 (1), 128-152.
Cooper, R. (1990). Stage-gate systems: A new tool for managing new products. Business
Horizons , 3 (3), 44-54.
Dahlander, L., & Gann, D. (2010). How open is innovation? Research Policy , 699-709.
Doctoroff, T. (2014). Twitter is Not a Strategy: Rediscovering the Art of Brand Marketing.
London: Palgrave Macmillan.
Edquist, C. (2001). Innovation policy – a systemic approach. The Globalizing Learning
Economy , 219-237.
Florén, H., & Frishammar, J. (2012). From preliminary ideas to corroborated product
definitions: managing the front end of new product development. California Management
Review , 54 (4), 20–43.
Gassmann, O., & Enkel, E. (2004). Towards a theory of open innovation: three core process
archetypes. Proceedings of the R&D Management Conference, (pp. 1–18). Lisbon.
Hansen, M. T., & von Oetinger, B. (2001). Introducing T-shaped managers: Knowledge
management’s next generation. Harvard Business Review , 79 (3), 106-116.
Hirshberg, J. (1999). The Creative Priority: Putting Innovation to Work in Your Business.
New York: Harper Paperbacks.
Holm, T. (2010). Using Service Challenge-based Evaluations for the systematic innovation of
proactive remote services — An industry case study. Emerging Technologies and Factory
Automation (ETFA) (pp. 1-4). Bilbao: IEEE.

Hui, G. (2014, January 22). So You Want to Build an Internet of Things Business. Harvard
Business Review .
Hutchinson, S. (2014, September 17). Car hacking: The security threat facing our vehicles.
Retrieved from Phys.org.
Kotabe, M., Mol, M. J., & Ketkar, S. (2008). An evolutionary stage model of outsourcing and
competence destruction: A Triad comparison of the consumer electronics industry.
Management International Review , 48 (1), 65-94.
Mudambi, S., & Tallman, S. (2010). Make, Buy or Ally? Theoretical Perspectives on
Knowledge Process Outsourcing through Alliances. Journal of Management Studies , 47 (8).
Murphy, M. (2015, January 5). CES: Volvo unveils collision prediction technology for
cyclists in Vegas. Retrieved from TechWorld.
O'Donnell, B. (2015, February 10). Business Models For The Internet of Things (IoT).
Techpinions.com .
Saran, C. (2015, March 2). Volvo develops car-to-car social networking for road safety.
Retrieved from ComputerWeekly.com.
Teece, D. J. (2010). Business Models, Business Strategy and Innovation. Long Range
Planning , 43, 172-194.
Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of
(sustainable) enterprise performance. Strategic Management Journal , 28 (13), 1319-1350.
Tilley, A. (2014, August 14). Samsung Acquires SmartThings, A Fast-Growing Home
Automation Startup. Retrieved from Forbes.
Trott, P., & Hartmann, D. (2009). Why 'Open Innovation' is Old Wine in New Bottles.
International Journal of Innovation Management , 13 (4), 715-736.
Wallin, M. W., & Von Krogh, G. (2010). Organizing for Open Innovation: Focus on the
Integration of Knowledge. Organizational Dynamics , 39 (2), 145–154.
Zhang, J., Lu, Y., Lu, Z., Liu, C., Sun, G., & Li, Z. (2015). A new smart traffic monitoring
method using embedded cement-based piezoelectric sensors. Smart Materials and Structures
, 24 (2).
Ziegler, C. (2015, January 5). Toyota opens its fuel cell patents in bid to make hydrogen cars
happen. Retrieved from The Verge.

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