marco tirelli - open innovation in the era of the internet of things

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


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