Tag: technology

Schematic overview to understand the complexity of the Innovation Ecosystem (Infographic)

The Innovation Ecosystem

The Innovation Ecosystem is one of the most under-researched topics. One the one hand because policy researchers usually tend to focus more on polls, elections and international collaboration and business researchers usually tend to focus more on organizations and interorganizational collaborations. However, publisher Edward Elgar has repeatedly published interesting works on innovation policy, innovation systems and the like. An ecosystem of innovation could be described as, quoting Wikipedia, the flow of technology and information among people, enterprises and institutions [which] is key to an innovative process. It contains the interaction between actors who are needed in order to turn an idea into a process, product or service on the market. The Innovation Ecosystem is extremely important to the economy and welfare of a country or region. It is one of the main drivers of GDP. Over the past decades more research has been done on the dynamics behind these ecosystems and its subsystems. Below you'll find a schematic overview of the innovation ecosystem. It will take you to the download side of Innovative Dutch, where you can download it in full resolution.

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    System Dynamics within the Ecosystem

    The Innovation Ecosystem could be defined as a dynamical system. Dynamical systems are a theory first mentioned by Jay Forrester in the 50s and applied to a wide range of disciplines such as demography, ecology, evolution, economy and sociology. It suggests that systems contain complex feedback loops, causal links, flows, stocks, delays among the agents. Because these agents influence each other with complex logic, mostly non-linear, it is very hard to predict how the system will behave. Usually the basic feedback loops consist of positive loops, that will keep enhancing itself without limitation. However, the system also holds several negative loops, that will discontinue the positive processes. Systems usually continue switching between positive flows and negative flows, making fluctuations very common. For instance, in climate, is it common to have fluctuations per the hour, per the day, per the season, per the year and over long era's. The same holds for the economy or for rabbit populations. Complex dynamical systems can be mathematically programmed. The following example shows how a system with only two actors can even achieve chaos within a few cycles when there are small anomalies in its initial circumstances. This is called the chaos theory: imagine the long-term effect a small change can have. For instance, the effect of a local forest fire on the weather world-wide. Or losing a coin on the local economy.Innovation ecosystems work the same. There are many agents, that are influences by a wide range of actors. Imagine the effect of low-level corruption on a national ecosystem or the effect of a successful start-up on the world-wide ecosystem.

    Innovation subsystems

    There are many different subsystems of innovation, for instance:

    • National Innovation Systems: 'The network of institutions in the public and private sectors whose activities and interactions initiate, import, modify and diffuse new technologies' (Freeman, 1987). Another word that is used on a regular basis for NIS is 'Institutional Environment' – which describes the institutionalization of innovation policy in governments, research institutes, advisory boards and educational institutes.
    • Regional Innovation Systems: 'The regional innovation system can be thought of as the institutional infrastructure supporting innovation within the productive system of a region.' (Asheim & Gertler, 2005). While NIS focuses more on the institutional environment of innovation, RIS usually focuses more on soft factors, such as network characteristics, trust, identity, cosmopolitism, quality of life and culture. These factors are often the first things if we think about successful RIS such as Grenoble, Silicon Valley, Helsinki or Brainport.
    • Sectoral Innovation Systems: during the early zero's more attention has come to sectoral innovation systems. In contrary to NIS and RIS, SIS focus on globally active sectors that function independently of the institutional environment. For instance, the Dutch government now prolongs the Top Sector Policy, focusing on different global sectors. NIS and RIS are now mainly supportive to SIS in the Netherlands. The Top Sectors defined are Agri-Food, Chemicals, Creative Industry, Energy, High-Tech, Logistics, Life Sciences & Health, Agriculture and Water. Another institute, the EIT, is also focusing on these sectors (Climate, Digital, Health, Raw Materials and Energy).
    • Education Systems: these are the ecosystems that surround educational institutes, such as universities. This group is often referred to as the economics of education. An well-performing education system usually increases expenses because of increased income, increases in return on investments because of higher (company) incomes and increases in productivity. It enables academic inflation.
    • Macro-economical Systems: this system refers to basic economics: output and income (GDP, GRP), unemployment and inflation and deflation.
    • Start-up Systems: a startup ecosystem is a small-scale system that enables startups to arise. It involves aspects such as ideas, inventions, research, education, startups, entrepreneurs, angel investors, seed investors, mentors, advisors and events and is supported by universities, incubators, accelerators, facilitators, investors, coworking spaces and venture capitalists.
    • Innovation Management Systems: these refer to a cyclical view of turning ideas into innovation; I've wrote a post about that earlier.
    • Cluster or Science Park Systems: In 2000 Porter already wrote: 'Geographic, cultural, and institutional proximity provides companies with special access, closer relationships, better information, powerful incentives, and other advantages that are difficult to tap from a distance. […] Competitive advantage lies increasingly in local things – knowledge, relationships, and motivation – that distant rivals cannot replicate.' (Porter, 2000). Clusters usually go the four phases: emergence, growth, maturity and renewal. The reason why clusters seem to work well is proximity. Cooke et al. (2011) suggest 7 types of proximity, 1) Geographic proximity – referring to the physical distance between actors, 2) cognitive proximity – referring to the closeness in ways of thinking between the actors, 3) communicative proximity – referring to the closeness professional language between the actors, 4) organizational proximity – referring to the arrangements that organizations make to coordinate interactions and collaborate with each other, 5) functional proximity – referring to closeness in expertise in different industries/clusters, 6) cultural proximity – referring to closeness of cultural habits and virtues and 7) social proximity – referring to the intensity of trust-based social relations, such as friendship.

    Crises

    The above-mentioned (sub)systems of innovation are in fact 'positive loops'; meaning that they will positively influence each other in an endless loop. As explained earlier, dynamic, chaotic systems, are almost always also containing negative loops, that break the positive flow. These negative loops can turn around the whole mechanism and cause crises, for instance the economic crisis. In the innovation system there are four main negative loops that create discontinuity:

    • Labour market depletion: innovation not only creates new firms which in turn increase employment, innovation also creates more automated, efficient processes that in turn lead to less employment: labour market depletion. Take a look at the book stores for instance: digital innovation has caused the traditional book stores to adjust their business to the online world, closing down book stores and reducing the amount of employees.
    • Other new (disruptive) technologies: from an industry perspective, other new technologies can cause the whole sector to be superfluous. This term is identified as disruptive innovation. Think about how the mobile phone radically made landlines superfluous.
    • Imitation: rising profits within a sector also attracts new companies to the sector that will try to copy the products – at lower costs and without the initial investment. Especially sectors with low entrance barriers are receptive to this, such as software, app development and low-tech products.
    • Policy failures: a various number of reasons can cause policy to fail. The most common ones are bureaucracy, corruption and short-term thinking.

    Innovation Policy

    The innovation policy regarding RIS and NIS involves many different aspects. One way or another, the institutional environment tries to (positively) influence the main industrial innovation system. A few of the soft factor that policy usually to focus on are:

    • Smart infrastructure: this characteristic is about all kind of infrastructures that the region has to offer. This includes hard infrastructures, soft infrastructures and technological infrastructures.
    • Quality of life: according to Sternberg and Arndt (2001) the quality of life is created by: labour quality, housing amenities, and leisure amenities. All of these factors attract highly qualified people to the region, but moreover, they also make people stay in the region.
    • Cosmopolitanism: this aspect refers to any form of feeling that is evoked by the region. The characteristics of this factor are for example attractiveness for highly educated personnel, a world-wide reputation, a good atmosphere, a shared purpose, and highly motivated people (Whitley, 2002).
    • Talented human capital: Micheals et al. (2001) describe that attracting talent, educating talent, and keeping talent is of high importance to the region. They focus on managerial talent, but they explain that technological, engineering and business talent also must be part of a regional strategy to win the war for talent.
    • Creative cultural environment: a well-developed entrepreneurial climate is attracting and exploiting personal talent and is reinforcing the strong culture of the community. Hofstede, more than 25 years ago, received worldwide praise for constructing four – although years later a fifth one was added – dimensions to characterize cultures of different nations: power distance, uncertainty avoidance, individualism, and femininity (Hofstede, 1980).
    • Trust: there is considerable evidence that a trusting relationship creates greater knowledge sharing. In a trust-based relationship, people are more willing to share useful knowledge. Trust promotes social and emotional ties on the one hand and promotes professional collaboration on the other hand, both facilitators of knowledge sharing (Chowdhury, 2005; Tsai & Ghoshal, 1998; Mayer, Davis, & Schoorman, 1995).
    • Identity: scientists claim that knowledge is more effectively generated, combined and transferred by individuals who identify with a larger collective goal. The individual members then share a sense of purpose with the collective. Ultimately, this will lead to lower network costs, and more trust and commitment (Kogut & Zander, 2003; Dyer & Nobeoka, 2000; Orr, 1990)
    • Diversity: this characteristic of knowledge refers to the extent to which a variety of knowledge, know-how, and expertise is available in a network. New opportunities and resources will be discovered more quickly with access to diverse knowledge and knowledge diversity therefore directly stimulates creativity and innovativeness of the actor in the network. (Galunic & Rodan, 2004; Galunic & Rodan, 2002; Rodan, 2002).

    Triple Helix

    Over the last decade we've heard a lot about the triple helix. More recently also the quadruple and quintuple helix have been introduced. Moreover, also Open Innovation and Co-creation have been growing over the years. What they have in common is that these theories try to integrate the different actors in the traditional dynamical view of ecosystems with each other. In that case, it won't be a 'flow' and it will therefore reduce the time delays within the flow. Simply said: deep integration between goverments and industry could result in quicker innovation. As does deep integration between education and industry; or different industries with each other, et cetera. The triple helix is a modern, 3D, view on system dynamics in the innovation ecosystem.

    Games: simulation of complex dynamical systems

    Games are a very common way to let actors in the network know how complex dynamics works. These games let you play with a few of the 'agents' in the ecosystem to experiment with the effects to better understand long-term behavior of ecosystems. Innovative Dutch creates these kinds of strategic simulation games for governments, companies and higher education; they created this infographic for their newest game; please take a look at their website.

    The 3 Phases of Responsible Innovation

    Over the last few month, the phrase “Responsible Innovation” has been booming on scientific social media. It has emerged from Corporate Social Responsibility as a topic that researches the effect and consequences of innovation on the long term. This could be technological effects, antropological effects or ethical effects.

    The fundament of this research topic lies in the Collingridge Dilemma:

    The Collingridge dilemma is a methodological quandary in which efforts to control technology development face a double-bind problem: an information problem – impacts cannot be easily predicted until the technology is extensively developed and widely used – and a power problem – control or change is difficult when the technology has become entrenched.

    The way to start innovating in order to enhance responsible innovation is three-fold:

    1. Value-consciousness in design, research and development: this aspect means that design or R&D should start with a clear answer to the ‘why of innovation’. In other words: does this idea or design provide a solution to one of the grand challenges that we are facing in 5, 10 or 30 years? Values are the key to those answers.

    2. Ethical Parallel Research: every step of the innovation management funnel  should be taken with the influence of ethical researchers and if possible, also researches from other parallel industries. This way, the impact that the innovation has on the long term can be easily addressed and tackeled early stage.

    3. Constructive Technology Assessment: innovation teams shouldn’t be monodisciplinary, but multidisciplinary. That way, early-stage innovation (ideas) can be assessed and tested upon. Multidisciplinary teams form the basis of Open Innovation.

    If you are interested in the material, take into account the following material:

    Responsible Innovation: Managing the Responsible Emergence of Science and Innovation in Society

    Science and innovation have the power to transform our lives and the world we live in – for better or worse – in ways that often transcend borders and generations: from the innovation of complex financial products that played such an important role in the recent financial crisis to current proposals to intentionally engineer our Earth’s climate. The promise of science and innovation brings with it ethical dilemmas and impacts which are often uncertain and unpredictable: it is often only once these have emerged that we feel able to control them. How do we undertake science and innovation responsibly under such conditions, towards not only socially acceptable, but socially desirable goals and in a way that is democratic, equitable and sustainable? Responsible innovation challenges us all to think about our responsibilities for the future, as scientists, innovators and citizens, and to act upon these.

    The Importance of Responsible-Innovation and the Necessity of ‘Innovation-Care’

    This study deals with responsibility as part of innovation. By nature, innovation gives birth to development for the organization and can only be at the core of any strategy within an ever-increasingly global economic context. However it also raises new questions stemming mostly from the impossibility to forecast the success of the innovations. More precisely, the questions raised by innovation also concern its consequences on society as a whole. Today, the innovator should understand his responsibility, the consequence of each innovation.

    Moreover, common acceptance of the word ‘responsibility’ raises some questions about its use and how it should be understood. What does ‘responsibility’ mean? Who is responsible and for what? Through the notion of ‘care’, we aim at providing an evolution of responsible-innovation. The concept of ‘innovation-care’ is centered on people and more precisely focuses on taking care of them. The purpose of innovation-care is indeed to innovate and keep up with the level of productivity necessary to any organization while taking into account the essential interdependence between the status of the innovator and that of the citizen.

    Enhancing Socially Responsible Innovation in Industry

    This thesis presents a study that aims to explore to what extent corporate researchers in the field of industrial Life Science & Technology (LST) can consider social and ethical aspects of LST innovation to improve their Research and Development (R&D) practices. Innovators, particularly those working in controversial scientific and technology fields such as industrial LST, are encouraged to adopt socially responsible innovation methods. This requires that researchers, who work in such fields, consider the broader social and ethical context of their R&D activities.

    The presented study explores first how corporate researchers can integrate such aspects in their daily work and how this could improve their work. Second it investigates whether such integration leads to a quantitatively assessable improvement of the quality of R&D. The results indicate that integration is possible, and leads to a measurable improvement of the quality of R&D work. In addition, researchers see a number of improvements in their R&D work, e.g. in the quality of communication and cooperation, and how to link their own work to corporate strategies and marketing. This thesis can be useful for innovators who wish to enhance socially responsible innovation practices, as it presents a tool for R&D management that allows for the operationalisation of socially responsible innovation and improved R&D performance.

    First annual conference Responsible Innovation

    Top 10 Best Articles on Open Innovation in 2013

    Based on the rankings of the SSRN database, we are able to create a ranking of the best – most downloaded – Open Innovation and related topics articles that have been published in 2013 so far. Therefore, this is a list of brand new theories, recent case studies, preliminary results and pioneering research.

    1. The Theory of Crowd Capital; Prpic, J., & Shukla, P.
      Abstract: We are seeing more and more organizations undertaking activities to engage dispersed populations through Information Systems (IS). Using the knowledge-based view of the organization, this work conceptualizes a theory of Crowd Capital to explain this phenomenon. Crowd Capital is a heterogeneous knowledge resource generated by an organization, through its use of Crowd Capability, which is defined by the structure, content, and process by which an organization engages with the dispersed knowledge of individuals – the Crowd. Our work draws upon a diverse literature and builds upon numerous examples of practitioner implementations to support our theorizing. We present a model of Crowd Capital generation in organizations and discuss the implications of Crowd Capital on organizational boundary and on IS research.
    2. Leveraging External Sources of Innovation: A Review of Research on Open Innovation, West, J. & Bogers, M.
      Abstract: This article reviews research on open innovation that considers how and why firms commercialize external sources of innovations. It examines both the “outside-in” and “coupled” modes of Enkel et al. (2009). From an analysis of prior research on how firms leverage external sources of innovation, it suggests a four-phase model in which a linear process — (1) obtaining, (2) integrating and (3) commercializing external innovations — is combined with (4) interaction between the firm and its collaborators. This model is used to classify papers taken from the top 25 innovation journals identified by Linton and Thongpapan (2004), complemented by highly cited work beyond those journals. A review of 291 open innovation-related publications from these sources shows that the majority of these articles indeed address elements of this inbound open innovation process model. Specifically, it finds that researchers have front-loaded their examination of the leveraging process, with an emphasis on obtaining innovations from external sources. However, there is a relative dearth of research related to integrating and commercializing these innovations.
      Research on obtaining innovations includes searching, enabling, filtering, and acquiring — each category with its own specific set of mechanisms and conditions. Integrating innovations has been mostly studied from an absorptive capacity perspective, with less attention given to the impact of competencies and culture (including not-invented-here). Commercializing innovations puts the most emphasis on how external innovations create value rather than how firms capture value from those innovations. Finally, the interaction phase considers both feedback for the linear process and reciprocal innovation processes such as co-creation, network collaboration and community innovation.
      This review and synthesis suggests several gaps in prior research. One is a tendency to ignore the importance of business models, despite their central role in distinguishing open innovation from earlier research on inter-organizational collaboration in innovation. Another gap is a tendency in open innovation to use “innovation” in a way inconsistent with earlier definitions in innovation management. The article concludes with recommendations for future research that include examining the end-to-end innovation commercialization process, and studying the moderators and limits of leveraging external sources of innovation.
    3. The Golden Circle of Innovation: What Companies Can Learn from NGOs When It Comes to Innovation, Spruijt, J.P., Spanjaard, T.G.S. & Demouge, K.
      Abstract: This paper examines the lessons that companies can learn from NGOs when it comes to the why, the how and the what of innovation. It explains innovation from the inside out: why is it important and what are the grand challenges? Followed by the how: in what way can innovation be managed and how does the innovation process look like in a modern economy?
      This introduction is elaborated on with two case studies within NGOs in The Netherlands, Fair2 and Liliane Foundation. It leads to several conclusions and hypotheses for further research.
    4. Sustainability-Oriented Innovation, Hansen, E.G. & Grosse-Dunker, F.
      Abstract: Sustainability-oriented innovation (SOI): the commercial introduction of a new (or improved) product (service), product-service system, or pure service which – based on a traceable (qualitative or quantitative) comparative analysis – leads to environmental and (or) social benefits over the prior version’s physical life-cycle (‘from cradle to grave’).
    5. Open Innovation and Organization Design, Tushman, M., Lakhani, K. & Lifshitz-Assaf, L.
      Abstract: Abernathy’s (1978) empirical work on the automotive industry investigated relationships among an organization’s boundary (all manufacturing plants), its organizational design (fluid vs. specific), and its ability to execute product and/or process innovations. Abernathy’s ideas of dominant designs and the locus of innovation have been central to scholars of innovation, R&D, and strategic management. Similarly, building on March and Simon’s (1958) concept of organizations as decision making systems, Woodward (1965), Burns and Stalker (1966), and Lawrence and Lorsch (1967) examined relationships among organizational boundaries, organization structure, and innovation in a set of industries that varied by technology and environmental uncertainty. These and other early empirical works have led a diverse group of scholars to develop theories about firm boundaries, organization design, and the ability to innovate.
    6. Managing Crowd Innovation in Public Administration, Collm, A. & Schedler, K.
      Abstract: Governments all over the world have discovered the world of social media, for better or for worse. Whereas some of them are making every effort to prevent the unhierarchical and therefore uncontrollable (dissident) opinion-forming process in Web 2.0, others are looking for ways of putting the potentialities of this new opening-up of communication to use. One approach that is increasingly being tried out is opening up innovation processes in government. However, this opening-up of innovation processes is anything but trivial. It requires a thoroughly thought-out strategy and thus confronts government systems with extensive challenges if it is not to suffer the same fate as other unsuccessful attempts at reform in the past. In our essay, we reflect on the consequences of these challenges for public managers.
    7. Adopting Open Innovation to Stimulate Frugal Innovation and Reverse Innovation, Hossain, M.
      Abstract: Frugal innovation and reverse innovation have very recently emerged as interesting concepts. Frugal innovation is based on cost constraints to serve low-income customers in developing countries. When frugal innovation comes to developed countries and becomes commercially successful it is considered as reverse innovation. Recently, many companies, such as GE, Siemens, Procter and Gamble, etc. have engaged heavily in frugal innovation and in reverse innovation. Open innovation, on the other hand, has not been considered in the context of low-income customers in developing countries. We argue that using open innovation concept in developing countries may boast frugal innovation and reverse innovation. Consequently, quality product with low-income will be widely available not only in developing countries but also in developed countries. Hence, western companies need to change their long hold business strategies and reshape their business models. This study aims to illustrate why western companies need to be aware of and take step to become successful in the turbulent business world.
    8. The Impact of Visibility in Innovation Tournaments: Evidence from Field Experiments, Wooten, J.O. & Ulrich, K.T.
      Abstract: Contests have a long history of driving innovation, and web-based information technology has opened up new possibilities for managing tournaments. One such possibility is the visibility of entries – some web-based platforms now allow participants to observe others’ submissions while the contest is live. Seeing other entries could broaden or limit idea exploration, redirect or anchor searches, or inspire or stifle creativity. Using a unique data set from a series of field experiments, we examine whether entry visibility helps or hurts innovation contest outcomes. Our eight contests resulted in 665 contest entries for which we have 11,380 quality ratings. Based on analysis of this data set, we provide evidence that entry visibility influences the outcome of tournaments via two pathways: (1) changing the likelihood of entry from an agent and (2) shifting the quality characteristics of entries. For the first, we show that entry visibility generates more entries by increasing the number of participants. For the second, we find the effect of entry visibility depends on the setting. Seeing other entries results in more similar submissions early in a contest. For single-entry participants, entry quality “ratchets up” with the best entry submitted by other contestants previously if that entry is visible, while moving in the opposite direction if it’s not. However, for participants who submit more than once, those with better prior submissions improve more when they can not see the work of others. The variance in quality of entries also increases when entries are not visible, usually a desirable property of tournament submissions.
    9. Digital Scholarship: Exploration of Strategies and Skills for Knowledge Creation and Dissemination, Cobo, C. & Naval, C.
      Abstract: Widespread access to digital technologies has enabled digital scholars to access, create, share, and disseminate academic contents in innovative and diversified ways. Today academic teams in different places can collaborate in virtual environments by conducting scholarly work on the Internet. Two relevant dimensions that have been deeply affected by the emergence of digital scholarship are new facets of knowledge generation (wikis, e-science, online education, distributed R&D, open innovation, open science, peer-based production, online encyclopedias, user generated content) and new models of knowledge circulation and distribution (e-journals, open repositories, open licenses, academic podcasting initiatives, etc.). Despite the potential transformation of these novel practices and mechanisms of knowledge production and distribution, some authors suggest that digital scholarship can only be of significance if it marks a radical break in scholarship practices brought about through the possibilities enabled in new technologies. This paper address some of the key challenges and raise a set of recommendations to foster the development of key skills, new models of collaboration and cross-disciplinary cooperation between digital scholars.
    10. Dissenting State Patent Regimes, Hrdy, C.A.
      Abstract: Inventors who believe in open innovation should start applying for state patents instead of U.S. patents. Patenting at the state level prevents rivals from obtaining U.S. patents and generates valuable innovation spillovers in other states where the patent has no legal effect. It also creates a unique opportunity to force patent law reform from the bottom up. In exchange for filing fees, inventors can demand patents based on rules that support open innovation, like shorter terms in fast-moving industries, stricter disclosure requirements, or new restrictions on patenting by non-practicing entities. The lobbyists who stymie reform at the national level will have a much harder time blocking reform in all fifty states. Meanwhile, patent law’s dissenters need only one state to start granting patents in order to get courts, the media, and eventually Congress to pay attention.

    Shell wants to invest in Open Innovation

    Shell indicates to be willing to invest hundreds of millions of dollars in technology-oriented companies for the next 6 to 8 years. according to belegger.nl. The website has published to following text (translated):

    This step helps Shell to enable the use of innovations on new projects, according to the company.

    Shell refers (amongst others) to the technology that enables the company to apply their resources more thoughtfully and smarter in their quest for oil and gas and in the improvement of the process of obtaining gas and oil.The investments are categorized under Shell Technology Ventures. ,,Ideas from outside our organisation are of great importance in the exploitation of R&D.. We want to be enable the brightest to develop plans and let them take advantage of our expertise and global impact of our company in order to use these technologies as quickly as possible on our projects”, according to the chief technology officer Gerald Schotman.

    Besides investing in promising technological companies, Shell wants to focus on so-called spin-outs, organisational assets that become independent and on funds of venture capitalists.

    As such a great example of Open Innovation in practise.

    The Innovation Spiral: a closer look on Ernst & Young’s innovation model

    “Mention the word “innovation” and most people will think of extraordinary inventions created by solitary geniuses,” as mentioned in the first line of Ernst & Young‘s introduction to (one of) their innovation model(s). The article is titled: Innovation for Growth: a spiral approach to business model innovation. A promising introduction: it seems to include (organizational) growth theories, innovation management theory and business model theory. Again, after last year’s successful article on Deloitte’s Fast Growth Track, we’ll take a closer look on this model. Is this model theoretically justified? And if yes – assuming it’s an absolute yes – why does it work and how could it help you?

    Business Model Innovation versus Innovation for Growth

    First of all, let’s take a closer look at one of their general promises; on the one hand the article promises to innovate your business model. Or, as Henry Chesbrough has written it:

    “There was a time, not so long ago, when ‘‘innovation’’ meant that companies needed to invest in extensive internal research laboratories, hire the most brilliant people they could find, and then wait patiently for novel products to emerge. Not anymore. The costs of creating, developing, and then shipping these novel products have risen tremendously (think of the cost of developing a new drug, or building a new semiconductor fabrication facility, or launching a new product into a crowded distribution channel). Worse, shortening product lives mean that even great technologies no longer can be relied upon to earn a satisfactory profit before they become commoditized. Today, innovation must include business models, rather than just technology and R&D.”

    Source: Chesbrough (20o7): Business Model Innovation: it’s not just about technology anymore

    So, the strategic focus of organizations has made a transition from product or service innovation towards business model innovation. That said, it surely doesn’t mean that service or product innovation is of less relevance: it has just shifted from a strategic level to a more tactical level. I got the opportunity ask (well, actually I’m filming, a colleague is asking the questions) Alexander Osterwalder about the place of innovation in the Business Model theory. This is what he said:

    So the business model is not directly linked to innovation per se. Osterwalder:

    “What it does is, it gives you a language. It’s very tangible, very visual, that will help you to create better conversations and it will make it easier for you to convince people of innovative possibilities.”

    Concluding this part: it’s hard to focus on both Business Model Innovation and “Innovation for Growth”, because they are both executed at completely different levels.

    Spiral Approach to Innovation: Innovation Processes

    Well, so far the analysis of the title page. Let’s take a closer look at their PDF. I will include it here for your convenience:

    [gview file=”http://www.ey.com/Publication/vwLUAssets/Growing_beyond_-_Innovation_report_2012/$FILE/Innovation-Report-2012_DIGI.pdf” height=”500px” width=”100%”]

    I’ll directly skip to the folowing passage in the text:

    “For the most innovative companies today, innovation isn’t a linear process. Rather, it’s a continuous cycle with ups and downs, inputs from different places, repetitions, failures, and many steps back and forth.”

    Our guts feeling says that this statement is right. Indeed, it is. Innovation management is a process and many processes are theoretically seen as cycles.The origin of innovation studies lies within the product life cycle, firstly decribed by Lewitt in 1965 and later elaborated on by Perreault, for instance in 2000. It basically consists of four phases: market introduction, market growth, stability and decline. More focused on innovation, Rogers (1995) created a more specified model, ‘the diffusion of innovation and adopter categories.’

    These models are singular, while innovation is repeatable. That can be shown by the following figure:

     

    The art of innovation, the process of innovation, is often referred to as innovation management. Innovation Management, or New Business Development, aims to enhance the possibility of technical and commercial success of new products and services (Schilling and Hill, 1998, Brown and Eisenhardt, 1997, Robert, 1994 and Clark and Fujimoto, 1991). The article Fast Track Growth for Innovation shows more indepth information into the different steps of the innovation process.
    Typically, each process is cyclic, in order to enhance the room for reflection and dynamical growth. Francis Bacon in 1620 wrote about this explaining that every scientific process should consist of hypothesis – experiment – evaluation. In 1982 Deming developed the Plan-Do-Check-Act cycle, which we all have heard of. Cole, in 2002, was the first who explicitly refered to innovation as a cycle: Probe – Test – Evaluate – Learn. Bacon gave his cycle the name ‘inductive approach’ – basically the same as a spiral approach.

    The Model Magnified: Is it good or could it be better?

    So, the circle as round: yes, innovation should be a spiral approach. Below a look on Ernst & Young’s inductive spiral approach:

    Wow, that’s something, isn’t it? At least it’s all-inclusive. Let’s take start with the second cycle: “Innovation Process”

    • Innovation Process: Ernst & Young have defined 5 steps: Intuition, Socialization, Ideation, Development and Exploitation. Clearly, it shows similarities with other – more theoretically accepted – models. The first two are quite surprising to me: Intuition and Socialization. The article explains: “Our research reveals a major shift in how leading companies go about innovation today. Intuition is the process of obtaining ideas, from anywhere and everywhere. Socialization happens when the idea is discussed and debated with other people, formally and informally.” I think this is a interesting perspective to look at the first step in innovation. On the one hand, it’s a modern way of looking at things: it’s fast and creates immediate action. It includes social media and people as a source for information and ideas, something that most models don’t include. On the other hand, it kind of simplified. Like (market) research and problem finding isn’t a scientific issue anymore, but more something that we come up by intuition. Perhaps intuition could play a small role, but it defintely isn’t how organizations repeatedly will structure innovation processes for the continuation of their core business. So yes, it’s a contemporary approach, but it’s not comprehensive.
      Even more, the relations between the different steps are quite strange. They all go two ways, except from the last one (and: is it actually the last one?), between exploitation and intuition. A two way arrow is a rather unfortunate way of showing that the process is iterative, meaing things could happen simultaneously in time. It definitely isn’t a two way process: after (unsuccesfull) exploitation, it’s not very logical to go back to the development phase, because the source of the problem needs to be re-identified and a new idea has to be created before redeveloping the product or service.
    • The other circles: to my opinion, the other circles try to include all exogene factors that could play a role in the primary innovation process. They are not cyclic at all and therefore it seems a forced way of including them in the model. It seems like a ‘sales pitch’ telling the clients all factors that could be taken into account during the advisory project. Perfectly plausible, but it should’t all be included in the model, because it doesn’t always make sense. For instance, the inner circle explain the different areas of innovation that could be addressed (processes, products and services and business model). Like explained before, these are three completely different strategic areas. Of course, they have to be addressed simultaneously, the influence each other, which explains their presence in this model. Also the outer circles don’t contribute to the value of the model. They are more seperate wheels (or clouds) around the model containing – very useful! – insights in innovation enablers and possible collaborators (read: possible clients).
    • The boxes: they only seem to offer information that didn’t fit inside the wheels. Please be honest, would you have missed them if they weren’t there?

    Summing up, I’m not very enthousiastic by the spiral approach towards business model innovation of Ernst & Young. It’s mostly a marketing instrument. Though a good one: it includes all expertises that Ernst & Young could probably help you with and is therefore a useful instrument for explaining how they could of help (and not how innovative business models could be (re)developed).

    A New Spiral Approach towards Innovation

    Of course, I will not only analyse the current model, I will also propose a better one. One that takes into account the five steps of the innovative process, but also the recent developments in innovation systems. And I left out all unnecessary information. This is what I get:

    Obviously, when ‘walking’ through this innovation process, it’s not necessary to stay at one level and address each step for the same amount of time. It’s more often and iterative process than not, like the following figure shows:

    Please, let me know what you think of this analysis. Am I right, or completely wrong?

    I would like to end with a quote from Maria Pinelli, Ernst & Youngs Global Vice Chair, which I actually find one of the best quotes I have recently bumped into:

    “It is not enough just to be innovative. It is essential to be innovative all the time.”

    Europe dominates Global Competitiveness Report

    Switzerland keeps its prime position in the list and Singapore stays second. Switzerland is renowned for its high investment in Research and Development and highly integrated collaboration efforts between business and knowledge institutes. In Singapore the main factors mentioned are the professional attitude and efficiency of the government. The top 5 is completed with two Scandinavian countries – Sweden and Finland, because of their investments in innovation and their outstanding integration between higher education and companies and The Netherlands.

    One of the new-comers in the Top 5 are The Netherlands, according to the recently published report by the World Economic Forum. The last time they were part of the Top 5 was in 2000. The Netherlands score particularly high on “advanced technology” and “innovation” and is therefore one of the most innovative countries of the world this year.  The figure below shows the competitiveness of The Netherlands over the years:

    The report has taken into account a bunch of different factors, grouped among the following aspects:

    • Institutions
    • Infrastructure
    • Macro-economical environment
    • Health and prime school
    • Higher education and training
    • Efficiency of the goods market
    • Efficiency of the labour market
    • Development of the financial markets
    • Technological consciousness
    • Market size
    • Business environment
    • Innovation

    Spreaded across the different aspects, several different factors in the field of innovation have been studied and depicted in the report. For instance, The Netherlands score as followed on those factors:

    The following factors translate as: capacity for innovation, quality of scientific institutes, expenditures on R&D, R&D-related collaboration between universities and companies, governmental procurement of advanced technological products, availability of knowledge workers and intelectual property/patents.

    For more information (in Dutch only) you can download the report of the Rotterdam School of Management.

    Stanford Technology Ventures Program: already 50K subscribers

    Next January, new (free) courses on Technology Entrepreneurship will be offered at Stanford University. The programs consist of separate video colleges of about 8-12 minutes each, counting up to almost 2 hours a week on course material. And above all, this is – amazingly – completely for free. We would like to recommend the following two courses:

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