Crisis-triggered Innovation Systems

While the Corona crisis is currently affecting millions worldwide, I wanted to already share with you a fragment of a book I’m currently writing about innovation in the new economy. The fragment is about how a crisis or disruption can create a (spontaneous) need for innovation and could open up opportunities for innovative companies to address new and changed market needs.

Schumpeterian economics

Although it wasn’t highly recognized at the time, the Schumpeterian approach to disequilibrative economics have been widely adopted nowadays. Schumpeter (1942) describes the entrepreneur as disequilibrative – destroying the pre-existing stage of the equilibrium (Kirzner, 1999). This concept is also called ‘creative destruction‘. Through this behaviour, markets will be in continuous cycles of reaching new equilibria and disruptions of that new equilibria. This approach relates to the diffusion of innovation, as was introduced by Rogers (2010). Towards the stage of a market equilibrium, new technology, services and products spread across its intended consumer markets: innovation gets adopted firstly by innovators and early adaptors – who trigger the market disruptions – then (if successful) followed by the early and late majority, reassuring a new equilibrium in the market. From an entrepreneurial perspective, we can see that entrepreneurs who are trying to be ‘creative destructors’, developing radical innovation that disrupts stable markets and mainly target the ‘innovators’ and ‘early adaptors’ have a sense of entrepreneurial alertness that we call seeking new opportunities, whereas entrepreneurs who are waiting longer to adapt their offering and only jump on board when markets are re-establishing again have an entrepreneurial sense that we call seizing (existing) opportunities. In order to function properly, markets need both types of entrepreneurs.

Crisis-triggered Innovation

However, it’s not only entrepreneurs seeking for creative destruction that will initiate market disruptions, it can also be a range of external factors. In innovation theory we distinguish between two types of innovation:

  • Endogenuous innovation: this perspective on innovation economics basically describes that innovation is inevitable. The longer a market is in a stage of an equilibrium, the higher chance will be that an entrepreneur will break that equilibrium. As a result, disruptive innovation is only a matter of time (Antonelli, 2017).
  • Exogenous innovation: this perspective on innovation economics describes the fact that disruptions in markets will occur because of specific external events that trigger the disruption. These events can be called crises or discontinuities. These crises can be initiated by for instance natural phenomena, but also by human behaviour (societal changes).

Both endogenuous and exogenous innovation will trigger a change in the difussion of innovation, starting off with the ‘valley of disruption’: the point in time where markets hibernate because of an on-going crisis.

Establishing the new normal

After the initial valley of disruption, markets will follow a typical recovering process that will bring them to a new baseline. When in crisis, academia are usually the initiator of new and innovative technologies needed to tame the crisis. New technologies gain attention (a little too much at first, the hype, followed by the valley of death) before venture capital or crowdfunding comes available and entrepreneurs start to take over and bring new solutions to market. In his work, Bessant et al (2015) describe 5 stages of crisis-driven innovation:

  • Crisis
  • Observatory
  • Laboratory
  • Prototyping
  • Scaling & Difussion

Organizational Latitude

So, the big question here is: what type of organizations are able to get the most out of a crisis? In the visual you can see the difference between market volatility – the variance in markets and sectors that are in disruptive stage – and organizational latitude – the maximum reach of innovation readiness of your organization. If your organizational latitude exceeds the volatility of your market, your organization will be able to deal with any disruption. Although I’m not saying that the following suggestions are inclusive, they are 2 excellent starting points:

  • Innovation Ecosystem: organizations who participate in knowledge ecosystems and business ecosystems, are much better able to pursue the valley of death and jump on board of the ‘upper new equilibrium’. Organizations who don’t, are more likely to be worse off after the crisis.
  • Teal Organizations: Laloux (2014) describes that so-called teal organizations and 42 ways in which teal organizations differ from other organizations. The more teal you are, the higher your organizational latitude, the better you’ll able to survive the crisis.

In this visual you’ll find all of the above visualized, including Laloux’s 42 characteristics of Teal Organizations.

Download

    Bibliography

    Antonelli, C. (2017). Endogenous innovation: The economics of an emergent system property. Edward Elgar Publishing.

    Bessant, J., Rush, H., & Trifilova, A. (2015). Crisis-driven innovation: The case of humanitarian innovation. International Journal of Innovation Management, 19(06), 1540014.

    Kirzner, I. M. (1999). Creativity and/or Alertness: A Reconsideration of the Schumpeterian Entrepreneur. Review of Austrian Economics, 11, 5–17.

    Laloux, F. (2014). Reinventing organizations: A guide to creating organizations inspired by the next stage in human consciousness. Nelson Parker.

    Rogers, E. M. (2010). Diffusion of innovations. Simon and Schuster.

    Schumpeter, J. (1942). Creative destruction. Capitalism, socialism and democracy, 825, 82-85.

    How to blend 10 Types of Innovation with the Business Model Canvas?

    Simple but effective: I’ve tried to combine the excellent framework of 10 Types of Innovation (Keeley et al, 2013) with the highly successful framework of the Business Model Canvas (Osterwalder, Pigneur et al, 2008). I wasn’t the first one to come up with this idea, some others have plotted the 10 types on the BMC before, such as Huw Griffiths on Medium or Heather McQuaid on Slideshare.

    Please download the file below and check out the example of Apple (based on the gameplan in Keeley’s book).

      When Ten Faces flirt with Ten Types: Ten Types of Innovation Teams

      When the book “Ten Types of Innovation” (Keeley et al.), in its most recent format, hit the shelves in 2013 – not only me, but many of my colleagues in higher education, embraced the work because of its clarity and integrality. It offered a much richer approach than the usual – perhaps more scientifically evidenced – approach of 4 types of innovation (product innovation, process innovation, business model innovation, service innovation). The work was, and still is, one of the most influential works used in our Business Innovation program and highly rewarded by both students and partners in the field. The infographic I made in 2014 based on this book has been one of the most downloaded infographics on this blog ever since.

      Parallel to that, but completely different in its approach, the work “Ten Faces of Innovation” (Kelley), acquired the same fame over the years. It was highly rewarded as a book that offered an approach to dealing with different roles in innovation teams – roles that were not necessarily bound to 1 person and are highly practical from nature – much more practical than for instance the typology mentioned in the Innovator’s DNA – which looks at roles from a more abstract, ambidextrous, point-of-view. The Ten Faces shouldn’t be used to tag people with specific skills (something that for instance DISC does), but can be used to start a deliberate conversation with your (innovation) team members about their strengths and ambitions.


        Innovation Teams

        But something that neither of them discuss is the existence and consistence of innovation teams. In innovation science the construct of an ‘innovation team’ is often overlooked – as if there is only one sort of innovation teams: the team that innovates and is run by innovators. In reality however, I came across many distinguishing approaches to what we could call innovation teams. They all have different set-ups and different aims. Drawing on my own experience (examples below) and backed by the models of Ten Types of Innovation and Ten Faces of Innovation, I deducted 10 Types of Innovation Teams. Let me introduce them to you:

        1. Spun-off Teams

        • Ten Faces: the hurdler, the director and the anthropologist
        • Ten Types: profit model innovation, network innovation, process innovation, product innovation;

        Spun-off Innovation Teams are teams consisting of seasoned entrepreneurs who have gained experience in SME or large corporations, created or designed a highly marketable technology or business model. They usually patent their idea from within the mothership and from then on collaborate with them to spin-out their business to increase chances of bringing it to market successfully.

        2. Tiger Teams

        • Ten Faces: the hurdler, the collaborator, the director
        • Ten Types: profit model innovation, network innovation, structure innovation, process innovation, channel innovation

        Tiger Teams are one-of-a-kind. They consist of influential innovators or project managers from within your organization. Their task usually is to dive into an immediate problem and come up with an innovative solution to overcome the problems. A problem could be that one of your suppliers is financially unstable, which rings all bells in your organization’s risk analysis. A Tiger Team can then be sent to the supplier to “overtake” their current business operations, create innovative managerial solutions and get things back on track. A Tiger Team could also intervene internally in organizational redesign or when opening up your business model. Tiger Teams will be able to deal with resistance and have a strong focus on the human impact of change.

        3. Formal Innovation Teams

        • Ten Faces: the hurdler, the collaborator, the caregiver
        • Ten Types: profit model innovation, network innovation, process innovation, product innovation and product system innovation

        Formal Innovation Teams are the teams we usually refer then framing innovation teams. They consist of powerful innovators with vision and a strong internal and external network to get things done. Innovation is not something they ‘do’ fulltime: they are freed of other tasks for 1-2 days a week and use this time to create shared visions, initiate new projects or follow top-down leads that may be interesting. They usually have some kind of stage-gate-model in place to be able to monitor and report about their progress.

        4. Strategy Teams

        • Ten Faces: the collaborator, the director, the anthropologist
        • Ten Types: profit model innovation, network innovation, structure innovation, product innovation

        Strategy Teams are responsible for developing new strategic directions. They thoroughly research market trends and user experiences in order to design new visions and possible scenarios for the organizations. Their task is to report continuously about these findings and suggest the creation of new business models for the organizations. They usually operate at a high level in the organization, are usually multidisciplinary and strongly intertwined with experts from universities to help them grasp the most important findings.

        5. Product Innovation Teams

        • Ten Faces: the collaborator, the experimenter, the set designer
        • Ten Types: network innovation, product innovation, product system innovation, service innovation

        Product Innovation Teams are an export product of the Agile Movement. Many organizations have recently transformed into ‘agile communities’ which includes (temporary, but fulltime) product innovation teams who are responsible for the full development of an idea completely to a market-ready stage. In digital companies they may take the form of scrum teams. The teams are usually flexible, but dedicated and result-oriented. They are makers and make sure to test and validate the product with users before its final implementation.

        6. Accelerator Teams

        • Ten Faces: the anthropologist, the cross-polinator, the set designer
        • Ten Types: structure innovation, process innovation, product innovation, product system innovation

        Accelerator Teams operate with innovation on another level: they don’t innovate themselves, but the facilitate innovation in their organization – using an innovative approach in doing so. They create spirit, they create learning spaces, they create inspiration, they build bridges, they make seemingly unrelated relations. With their approach they create a climate for innovation within the company that, in the long run, creates a process of continuous reinvention of the organization.

        7. Startup Teams

        • Ten Faces: the hurdler, the experimenter, the caregiver, the experience architect
        • Ten Types: product innovation, channel innovation, brand innovation

        Startup Teams consist of young, usually relatively unexperienced, innovators that have a specific mission in mind and are willing to iterate as long as possible until the mission becomes reality. They focus on creating minimum viable products and building strong brands on top of that. They create a flavor of freshness around the product and do not settle for known paths to market. To start online and stay online.

        8. Informal Innovation Teams

        • Ten Faces: the experimenter, the cross-polinator, the storytellers
        • Ten Types: product innovation, product system innovation, service innovation, customer engagement innovation.

        The informal nnovation teams breath idea generation. These are hidden ‘teams’, walking between the lines of the other teams. The create ideas at a non-stop basis and their influence in the organizations to bring the ideas to the more formal teams in order to realize them. Behind the scenes, they’ll stay involve to see how their ideas end up or if they should pull some more strings. These kind of teams are highly valuable to organizations: they exist everywhere, but are only appreciated in organizations with a strong innovation climate. They don’t want to be formalized, so let them be.

        9. Community Teams

        • Ten Faces: the cross-polinator, the storyteller, the experience architect.
        • Ten Types: service innovation, customer engagement innovation

        Community Teams drive innovation through the (user) community of the organizations. They try to continuously interact with the customers and involve external users in the creation of new opportunities for offering increased service and enhanced customer satisfaction. They use visual techniques and are tech-savvy in their ways to reach out and they growth hack their way into your innovation funnel. Their ideas may just be as good as the anthropologist’s ideas.

        10. Window Teams

        • Ten Faces: the storyteller, the experience architect
        • Ten Types: brand innovation, customer engagement innovation

        Window Teams are highly specialized in reinventing your brand and reputation. They will ideate, design and implement innovations that the outside world sees when googling for your organization. They usually consist of marketing-communication experts, graphical designers and fashionista’s and usually operate at a high level in the organization, because it’s not what’s good that gets the attention of the board but what gets wrong. Not having Window Teams may be bad for your reputation: nobody still believes that your product sells itself, do you?

        A 4-Step Approach to Creating Exceptional Business Models

        a futuring technique for public organizations

        Still a holy grail in market research: the DESTEP – STEEPLE – method. It is taught in almost every business-related course in the world and a very powerful tool to map trends for strategic purposes. 

        However, there are a few fatal flaws that may cause users of this method to miss out on important opportunities:

        1. It’s too broad to grasp the real pains and gains of customers and clients as well – and as such may result in insights for the whole market rather than insights for your users specifically.
        2. It’s supposedly a desk research method, missing out on many ‘odd’ opinions and visions that may actually change your market sooner than you think.
        3. It’s based on ‘old-world-thinking’ by looking into economies, demographics and technologies, rather than shifting paradigms, sociographics and new business models that may or may not be digitally enabled.

          Especially for the aim of doing strategic research within public institutes, such as (semi-)governmental organizations, non-govermental organization, education institutions and – yes, with their bureaucracy they function as public institutions as well – institutionalized corporations, the DESTEP-method doesn’t work. It is therefore that I came up and validated the TREINDI-approach: a futuring technique for public organizations.

          It consists of 7 categories that need to be addressed, in the right order, when doing strategic research for radically new or high-impact strategic changes. Examples may be:

          • New education programmes
          • New schools or universities
          • New public-private partnerships
          • New government-funded organizations or (temporary) collaborative ecosystems
          • New policy
          • New (innovation, economic or SDG) roadmaps

          When following these 7 categories, it creates a waterfall of ideas that may then be plotted in framework for further analysis. 

          7 Categories of TREINDI

          Ask yourself these questions when looking this magnificent 7.

          • Transformations: What technology-driven discontinuities are taking place? What challenges and transformations is the world facing?
          • Revolutions: In what way do they create revolutions in society? How do people, cultures and demographies react?
          • Ecosystems: How does the ecosystem in which we operate change? How does it effect the (economic) environment we're in?
          • Institutions: How is the foundation of our institiution impacted? How does it change the role of our department in society?
          • Nature of Work: In what way will that change the nature of work? How will we redesign or reinvent our structures?
          • Disciplines: What are the effects for our own discipline? How will the scope of our line of work change?
          • Innovation: What are the most recent (digital) innovations that impact us? What new tools, products, services and business models are around?

          When you understand the different phenomena, follow the following 4 steps to conclude your research

          1. Plan your Research

          First of all, make sure to take 2-3 months to complete your research: assuming you have about 8 hours/week available I would advise to do the following:

          • Gather the obvious: ask all of your colleagues to send you as much relevant papers, publications, links, videos, talks, and so on. Drop them in a folder. Moreover, dig up your own files and drop them in a folder as well. Don’t read everything now, but scan each article on validity and save some highlights for your expert interviews.
          • Gather insights: this consists of ‘user research’ and ‘conference crashing’:
            • User Research: set out on a path the find out what the users wants using Design Thinking methods. Speak to at least a 100 users.
            • Conference crashing: visit 2 or 3 high-end conferences on your topic. Pick the conferences carefully: choose the ones which have lots of paralel tracks, so you can sneak in and out of tens of sessions in a day to get as much deep knowledge as you need. Write down everything you remember and take dozens of pictures.
          • Gather visions: reach out to experts in the field and prepare excellent interviews for them. Use LinkedIn or your own network to find them and send your request carefully, knowing that you’ll take up valuable time from them, but recognising that your public mission might also be of importance to them.
          • Gather the unknown: organisatie at least 3 different workshops with clients and colleagues to further elaborate on your fist findings. Use other futuring techniques for that.

          2. Plot your Results

          Now, this is the most work. Go through all of your material and step by step plot them in the circle. You’ll be able to use thicker lines for trends that may have more impact on your organizations, and you may plot them closer to the ‘Zone of Impact’ if you think they will be of relevance sooner than others. 

          3. Categorize the Results

          Now, start categorizing the results by theme. Discuss this with a steering group and colleagues and try to find 4 or 5 different categories that summarize opportunities for your organization. Color them by category (don’t rearrange them) and try to draw starting points as well, depicting the current state of your business and making the gap clear. 

          4. Design New Business Models

          This is a whole tool in itself, but I’m supposing for now you’re familiar with that. Draw a new business model for each of the different categories. Discuss it with colleagues and redesign them until you’re happy about. Start some pilots. Draw a final report for the archives. 

          Congrats, you’ve made it, that’s kind of treindi!

          The Ambidextrous Organization

          4 Paths to a Sophisticated Innovation Strategy

          In december I reached out to both Alexander Osterwalder and John Bessant and asked them what is the most important organizational skill for engaging continuously with innovation. Their answers were almost the same:

          • Osterwalder mentioned that every board should consist of both a Chief Executive Office and a Chief Entrepreneurship Officer.
          • Bessant noted that organizations should always find a balance between innovators and innovation managers.

          Shorty after, I read an article by Ayse Birsel, on Inc.com1. She also talked to asked Alexander Osterwalder and asked him the question why ‘designers who are fluent at business strategy’ and ‘business people who are fluent at design’ are so different to each other. He could easily name 8 differences between the two of them, but the article concluded with the statement that organizations are in need of both explorers and exploiters – or evolutionaries and revolutionaires2.

            In management theory, this has long been referred to as organizational ambidexterity:> the ability of an organization to both explore and exploit—to compete in mature technologies and markets where efficiency, control, and incremental improvement are prized and to also compete in new technologies and markets where flexibility, autonomy, and experimentation are needed.3

            I discussed this construct briefly in an article last September.4. The construct usually deals with exploration on the one hand, and exploitation on the other hand. But reading the comments of Osterwalder and Birsel, and some other material that appeared online in December, I wasn’t exactly sure if exploration could be compared with ‘designer’ and exploitation could be compared with ‘business people’. I started to draw upon that idea, talked to a few people and figured that the construct of ambidexterity could actually be described in more detail when taking into account all 4 of these:

            Business Model Generation meets Strategy Creation

            This may need some clarification and finds it origin in Trkman & DaSilva’s 2014 work on What is a business model and what is not?5. Trkman & DaSilva argue that while strategy creation is an activity that focuses on the long-term, business model generation is something that focuses on the short-term:

            We concur that 'every organization has some business model' and 'not every organization has a strategy' (Casadesus-Masanell & Ricart, 2010, p. 206), we further emphasize that strategy reflects what a company aims to become, while business models describe what a company really is at a given time.6

            Trkman & DaSilva7

            This leads to the following two extremes on the horizontal axis. Following scientific evidence on ambidextrous organizations it could be stated that magic happens when business model generation meets strategy creation.

            • Exploration: creating a long-term strategy;
            • Exploitation: creating business models;

            Design meets Business

            This part is more in line with the discussion I had with Alexander Osterwalder and John Bessant. Among researchers, the intersection of design and management (science) has long been a topic of discussion. There is a growing group of scientists, mostly in business (engineering) that believe that management science is in fact a design-oriented science and that the two are as such inextricably intertwined with each other. Especially in the field of Entrepreneurship, there are findings that this is true:

            We conclude that the interaction between the two (creative rdesign and scientific validation) can drive the continual renewal of the entrepreneurship field and unlock the potential of an inclusive body of knowledge that is both rigorous and relevant. (Romme & Reymen, 2018)8

            In order to elaborate on the magic that happens when design meets business we should therefore look at theory on entrepreneurship that deals with this magic. One of the most relevant theories on that is the ever-lasting battle between Schumpeter and Kirzner. The Schumpetarian approach argues that organizations try to create something new9, while Kirzner argues that it's about seizing existing opportunities10. Research has shown that organizations deal with different strategies over time and that organizational design takes a more flexible approach in order to simultaneously deal with both effectuation and causation11 12., which can be seen in the following example:

            Ferreira et al.14

            This leads to the following two extremes on the vertical axis.

            • Creating opportunities: a design-oriented approach to innovation;
            • Seizing opportunities: a business-oriented approach to innovation;

            Capabilities for a sophisticated innovation strategy

            John Bessant and Joe Tidd created a model for developing and testing innovation capabilities in their 2009 work Managing Innovation.13 In 2015, Ferreira et al tested the assumptions and different capabilities Tidd & Bessant proposed and draw conclusions on the most interesting measurable capabilities for a sophisticated innovation strategy.14. A few of the most important capabilities were:

            • Organizations that create and share an explicit innovation strategy – and communicate clear goals – can achieve high innovation performance;
            • An innovation-friendly environment constituted in the organizational culture are fundamental to achieving high innovation performance;
            • Companies that are actively involved in outside-in activities can boost their innovation performance.

            Haanaes, Reeves & World argue that only 2% of the companies are part of the elite group of organization who understand that you have to excel at both efficiency and innovation. They find that the above-mentioned may be true: “Maintaining an outside-in perspective starts by continuously scanning the market, both demand and supply.”15

            In the visual I’ve included 28 capabilities that drive innovation on each of the extremes of the innovation landscape. Balancing the focus between them brings you closer to the 2%.

            4 Paths to Ambidexterity

            Each organization may have a different starting point, so each path to ambidexterity should be personalized. But roughly, we could distinguish four types of organizations with their corresponding paths to ambidexterity:

            Example of the Scale-up Path
            1. The Start-up Path: startups are usually driven by business creation: with a new technology, patent of idea they are seeking to create new markets and blue oceans to implement their idea with short cycles of experimentation. This means they are usually strong at creating opportunities and exploitation. In order to become more sophisticated they should focus more on the capabilities of seizing opportunities and exploration, starting with developing a stronger business sense and developing a long-term strategy.
            2. The Scale-up Path: scale-ups usually have a strong sense for their long-term objective: they set far-away goals and nothing gets in their way of achieving that: they are excellent at managerial causation and usually strong at creating opportunities and exploration. In order to become more sophisticated they need to find the right balance and focus more seizing opportunities and building strong business models.
            3. The SME Path: both technological and non-technological small-sized and medium-sized enterprises, which also includes family business, are usually very capable at entrepreneurial effectuation. They are ad hoc businesses who deal with everything that comes on their way. In order to become more sophisticated they need to focus more on creating opportunities using a design-oriented methodology and exploration creating a long-term strategy.
            4. The Corporate Path: large, established organizations are usually strong at business innovation by exploring scenarios and strategies for the long-term and by managerial, business-wise approach. They could become more agile, sophisticated organizations by focusing on a design-oriented approach to innovation and customers on the one hand and business modeling on the other hand for more short term results.

            Below, you’ll find an example of the Scale-up Path and the capabilities they could strive for.

            Further reading:


            1. https://www.inc.com/ayse-birsel/how-opposites-attract-in-love-business.html ↩︎

            2. https://www.inc.com/ayse-birsel/there-are-4-innovation-personalities-which-one-are-you.html ↩︎

            3. O'Reilly, C. A., & Tushman, M. L. (2013). Organizational ambidexterity: Past, present, and future. Academy of Management Perspectives, 27(4), 324–338. ↩︎

            4. http://innographics.nl/2018/09/05/typology-for-innovative-organizations/ ↩︎

            5. DaSilva, C. M., & Trkman, P. (2014). Business model: What it is and what it is not. Long Range Planning, 47(6), 379–389. ↩︎

            6. DaSilva, C. M., & Trkman, P. (2014). Business model: What it is and what it is not. Long Range Planning, 47(6), 379–389. ↩︎

            7. DaSilva, C. M., & Trkman, P. (2014). Business model: What it is and what it is not. Long Range Planning, 47(6), 379–389. ↩︎

            8. Georges Romme, A. L., & MMJ Reymen, I. (2018). Entrepreneurship at the interface of design and science: Toward an inclusive framework * Entrepreneurship at the interface of design and science: Toward an inclusive framework, 10. ↩︎

            9. De Jong, J. P. J., & Marsili, O. (2010). Schumpeter versus Kirzner: An empirical investigation of opportunity types. ↩︎

            10. Kirzner, I. M. (2009). The alert and creative entrepreneur: a clarification. Small Business Economics, 32(2), 145–152. https://doi.org/10.1007/s11187-008-9153-7 ↩︎

            11. Reymen, I. M. M. J., Andries, P., Berends, H., Mauer, R., Stephan, U., & Burg, E. (2015). Understanding dynamics of strategic decision making in venture creation: a process study of effectuation and causation. Strategic Entrepreneurship Journal, 9(4), 351–379. ↩︎

            12. Gibson, C., & Birkinshaw, J. (2004). Building Ambidexterity into an Organization Topic: Leadership and Organizational Studies. Reprint 45408, (4), 47–55. ↩︎

            13. Tidd, J., & Bessant, J. (2009). Managing innovation: Integrating technological, market, and organizational change. Chichester, England: John Wiley & Sons. ↩︎

            14. Ferreira, J. J. M., Fernandes, C. I., Alves, H., & Raposo, M. L. (2015). Drivers of innovation strategies: Testing the Tidd and Bessant (2009) model ☆. Journal of Business Research, 68, 1395–1403. https://doi.org/10.1016/j.jbusres.2015.01.021 ↩︎

            15. http://www.rotman.utoronto.ca/Connect/Rotman-MAG/Current-Issue/Winter2019-FreeFeatureArticle-The2PercentCompany?hss_channel=tw-162094534 ↩︎

            Typology for Innovative Organizations

            Typology for Organizations: an update

            It has been a while since Henry Mintzberg developed his influential work that made us aware of the importance of structures in organization design. To my opinion, Mintzberg’s work was a refreshing change to the world of organization design that until then has been largely influenced by Taylor’s Scientific Management Approach and Henry Ford’s efficiency-based adaptation of that.

            As an entrepreneur and lecturer in organization science I find myself still using Mintzberg-related terminology on a regular base: ‘professional organizations’, ‘top management’, ‘middle management’, ‘hierarchy’ or ‘organization charts’. While these terms may be common language in business and as such might be useful in having a common understanding of what we’re talking about, much of it is outdated: organization design has shifted it’s focus over time. Structures are no longer of primary focus in design organizations. In fact, building blocks as ‘middle management’ might only still exist on paper today. Let me show you how the focus of organization design has changed over the years:
             

            ScholarOrganization Design in their eyes
            Frederick Winslow Taylor (1911)Organization Design encompasses the development of task packages for employees that align with their strengths and competencies. It enhances productivity.
            Henry Ford (1913)Ford embraced the idea that not tasks should be optimized, but processes should be optimized and automatized: organization design is the effective and efficient design of processes.
            Henry Mintzberg (1979)Mintzberg looked at organization design from a perspective of structures.
            Robert Quinn & Kim Cameron (1983)Quinn & Cameron argued that organization can be defined by their cultures and introduced their Competencies Values Framework.
            Larry Greiner (1989)Greiner discussed in his work Evolution and Revolution as Organizations Grow that all of the before are true, but change over time for a growing company.
            Steve Blank (1995)Steve Blank argued, while coining the term Customer Development, that organization design needs to support the value proposition of organizations.

            But times are changing and organizations are emerging, scaling and managed completely differently. New generations, societal change, sustainable goals and disruptive technology require organizations to be much more flexible, self-reinventing organisms that don’t fit above-mentioned design principles. They require openness, transparency, adaptability, co-creation, self-management and responsiveness. While searching for a modern-day typology for innovative organizations – to show our students and what kind of context they most likely would want to work – I found that none was there, so I created a new one.

            A Typology for Innovative Organizations

            Below you’ll find an overview of the new typologies that I’d like to propose. The model describes organizational typologies based on cultures of innovation. This model is drawn upon a combination of Quinn & Cameron's values framework (2011) and Nagji and Tuff's innovation ambition framework (2012). The typology proposes 4 types of organizations. Each type of organization exists in three different levels of innovation. At the centre are the innovation brokers: consultancy firms, education professionals and knowledge brokers who do not directly work with innovation, but accelerate it (Chesbrough, 2007).

            On the right-hand side you’ll see a more structured-approach to the new typologies. All of Mintzberg’s types would now be grouped under ‘traditional structures’.

            Figure 1: Typology for Innovative Organizations. The figure in the middle was initially published in 2018 in the internal document Professional Profile Business Innovation at Avans University of Applied Sciences which I co-authored with the aim of explaining students in what environment they are most likely to find jobs after graduating.

              Why this typology: innovation management in organizations

              Academic Relevance

              Innovation Management focuses on creating and managing sustainable business (Crossan & Apaydin, 2010; Keeley, Walters, Pikkel, & Quinn, 2013).
               
              Romme (2016) argued that we are now far beyong early thinkers as Taylor and Ford and that organizational learning is a key aspect for innovative organizations (drawn from i.e. Garud & Van De Ven, 1992; Romme, 2016; Romme & Endenburg, 2006, Simon, 1991) and for business model innovation (Berends, Smits, Reymen, & Podoynitsyna, 2016; DaSilva & Trkman, 2014). Organizational learning helps innovative organizations to deal with the ever-changing, unsure and unpredictable context of business (Van De Vrande, 2017).
               
              As a result, ‘typologies’ are not as black-and-white as they used to be. Organizations are now ambidextrous by nature: 'the ability of an organization to both explore and exploit—to compete in mature technologies and markets where efficiency, control, and incremental improvement are prized and to also compete in new technologies and markets where flexibility, autonomy, and experimentation are needed' (O'Reilly & Tushman, 2013, p. 2) and has been widely studied (i.e. structured ambidexterity; O'Reilly & Tushman, 2008; i.e. contextual ambidexterity; Birkinshaw & Gibson, 2004). As such, a modern-day typology for innovative organizations should deal with ambiguity in organizations.
               
              Ambiguity isn’t new: the 'Schumpetarian approach' and the 'Kirznerian approach' have widely discussed over the last decades. The Schumpetarian approach argues that organizations try to create something new (De Jong & Marsili, 2010; Schumpeter, 1934), while Kirzner argues that it’s about seizing existing opportunities (Kirzner, 1999). Research has shown that organizations deal with different strategies over time and that organizational design takes a more flexible approach in order to simultaneously deal with both effectuation and causation (Samuelsson & Davidsson, 2009; Johnson, Craig, & Hildebrand, 2006; Shane, 2003; Busenitz, 1996; Walrave, van Oorschot, and Romme, 2011; De Jong & Marsili, 2010; Reymen et al., 2015, Christensen, 2011, Birkinshaw & Gibson, 2004, Kelley, 2005).

              Socio-economic Relevance

              An updated version of typologies is useful because it adopts new discussions, for instance about overexploitation (Raworth, 2017), innovation (Coley, 2009) and sustainability (Griggs et al, 2013; Sachs, 2012, United Nations, 2017) and puts them at the heart of organizational typology. As such, education programs and public instances would be more accurate in their teaching – which has a strong influence on future economic developments (Georghiou & Sachwald, 2017, p. 29). It follows up on trends in education to break the shift towards a more entrepreneurial environment into a model of multisided value creation (Manshanden et al, 2014; Zwaan, 2016)

              Usage

              The model can be used in three different ways:

              • For identification: it helps you in identifying the (most applicable) form of organizational typology for your organization. It helps in explaining differences between organizations and it helps in understanding why some companies mature in innovation and other don’t. It helps students in preparing for business environment and finding types of organizations that suit their wishes. It creates a common language.
              • For analysis: it helps in analyzing the strenghts and weaknesses of every aspect of your organizations. You can create a weighted variant that reveals the nuance in your strategy and company branding.
              • For discussion: it helps in understanding and discussing the strenghts and weaknesses of regional ecosystems, as it may be used to show the importance of certain types of organizations that are under- or over-represented in your area. It helps in organization your partner-network and starting open innovation projects

              References

              – Berends, H., Jelinek, M., Reymen, I., & Stultiëns, R. (2014). Product innovation processes in small firms: Combining entrepreneurial effectuation and managerial causation. Journal of Product Innovation Management, 31(3), 616–635. doi:10.1111/jpim.12117
              – Berends, H., Smits, A., Reymen, I., & Podoynitsyna, K. (2016). Learning while (re)configuring: Business model innovation processes in established firms. Strategic Organization, 14(3), 181–219. doi:10.1177/1476127016632758
              – Birkinshaw, J., & Gibson, C. (2004). Building ambidexterity into an organization. MIT Sloan Management Review, (4), 47–55.
              – Busenitz, L. W. (1996). Research on entrepreneurial alertness: Sampling, measurement, and theoretical issues. Journal of Small Business Management, 34(4), 35.
              – Cameron, K. S., & Quinn, R. E. (2011). Diagnosing and changing organizational culture: Based on the
              competing values framework. John Wiley & Sons.
              – Chesbrough, H. W. (2007). Why companies should have open business models. MIT Sloan Management Review, 48(2), 22–28.
              – Coley, S. (2009). Enduring ideas: The three horizons of growth. McKinsey Quarterly.
              – Crossan, M. M., & Apaydin, M. (2010). A multi‐dimensional framework of organizational innovation: A systematic review of the literature. Journal of Management Studies, 47(6), 1154–1191. doi:10.1111/j.1467-6486.2009.00880.x
              – DaSilva, C. M., & Trkman, P. (2014). Business model: What it is and what it is not. Long Range Planning, 47(6), 379–389. doi:10.1016/j.lrp.2013.08.004
              De Jong, J. P. J., & Marsili, O. (2010). Schumpeter versus Kirzner: An empirical investigation of opportunity types. EIM Business and Policy Research, Scales Research Reports.
              – Garud, R., & Van De Ven, A. H. (1992). An empirical evaluation of the internal corporate venturing process. Strategic Management Journal, 13(S1), 93–109. doi:10.1002/smj.4250131008
              Georghiou, L., & Sachwald, F. (2017). Europe's future: Open innovation, open science, open to the world: Reflections of the Research, Innovation and Science Policy Experts (RISE) High Level Group. Retrieved from the EU Publications website: https://publications.europa.eu/en/publication-detail/-/publication/527ea7ce-36fc-11e7-a08e-01aa75ed71a1
              – Griggs, D., Stafford-Smith, M., Gaffney, O., Rockström, J., Öhman, M. C., Shyamsundar, P., … Noble, I. (2013). Policy: Sustainable development goals for people and planet. Nature, 495(7441), 305–307. doi:10.1038/495305a
              – Keeley, L., Walters, H., Pikkel, R., & Quinn, B. (2013). Ten types of innovation: The discipline of building breakthroughs. Hoboken, NJ: John Wiley & Sons.
              – Kirzner, I. M. (1999). Creativity and/or alertness: A reconsideration of the Schumpeterian entrepreneur. Review of Austrian Economics, 11, 5–17. doi:10.1023/A:1007719905868
              – Lawrence, K. (2013). Developing leaders in a VUCA environment. UNC Executive Development, 1–15. Retrieved from https://www.emergingrnleader.com/wp-content/uploads/2013/02/developing-leaders-in-a-vuca-environment.pdf
              – Manshanden, W., de Heide, M., Koops, O., van der Horst, T., Poliakov, E., Bulasvkaya, T., … Bekkers, F. (2014). De Staat van Nederland Innovatieland: R&D: impuls voor economische groei. Special issue [The State of the Netherlands as an Innovation Country: R&D: Impetus for economic growth]. The Hague Centre for Strategic Studies.
              – Meadows, D. H. (2008). Thinking in systems: A primer. London: Chelsea Green Publishing.
              – Nagji, B., & Tuff, G. (2012). Managing Your innovation portfolio. Harvard Business Review, 66. Retrieved from https://hbr.org/2012/05/managing-your-innovation-portfolio
              – O'Reilly, C. A., III, & Tushman, M. L. (2008). Ambidexterity as a dynamic capability: Resolving the innovator's dilemma. Research in Organizational Behavior, 28, 185–206. doi:10.1016/j.riob.2008.06.002
              – O'Reilly, C. A., III, & Tushman, M. L. (2013). Organizational ambidexterity: Past, present, and future. Academy of Management Perspectives, 27(4), 324–338. doi:10.2139/ssrn.2285704
              – Osterwalder, A., & Pigneur, Y. (2010). Business model generation: A handbook for visionaries, game changers, and challengers. Hoboken, NJ: John Wiley & Sons.
              – Osterwalder, A., Pigneur, Y., Bernarda, G., & Smith, A. (2014). Value proposition design: How to create products and services customers want. Hoboken, NJ: John Wiley & Sons.
              – Raworth, K. (2017). Doughnut economics: Seven ways to think like a 21st-century economist. London: Chelsea Green Publishing.
              – Reymen, I. M. M. J., Andries, P., Berends, H., Mauer, R., Stephan, U., & Burg, E. (2015). Understanding dynamics of strategic decision making in venture creation: A process study of effectuation and causation. Strategic Entrepreneurship Journal, 9(4), 351–379. doi:10.1002/sej.1201
              – Romme, G. (2016). The quest for professionalism: The case of management and entrepreneurship. Oxford, UK: Oxford University Press.
              – Sachs, J. D. (2012). From millennium development goals to sustainable development goals. The Lancet, 379(9832), 2206–2211. doi:10.1016/S0140-6736(12)60685-0
              – Samuelsson, M., & Davidsson, P. (2009). Does venture opportunity variation matter? Investigating systematic process differences between innovative and imitative new ventures. Small Business Economics, 33(2), 229–255. doi:10.1007/s11187-007-9093-7
              – Schumpeter, J. A. (1934). The theory of economic development: An inquiry into profits, capital, credit, interest, and the business cycle (Vol. 55). Piscataway, NJ: Transaction Publishers.
              – Shane, S. A. (2003). A general theory of entrepreneurship: The individual-opportunity nexus. Cheltenham, UK: Edward Elgar Publishing.
              – Simon, H. A. (1991). Bounded rationality and organizational learning. Organization Science, 2(1), 125–134. doi:10.1287/orsc.2.1.125
              – Tushman, M., Lakhani, K., & Lifshitz-Assaf, H. (2012). Open innovation and organization design. Journal of Organization Design, 1(1), 24–27. doi:10.7146/jod.6336
              – United Nations. (2017, May 17). Innovators, UN discuss using tech to tackle world's development challenges. Retrieved from https://news.un.org/en/story/2017/05/557562-innovators-un-discuss-using-tech-tackle-worlds-development-challenges
              – Van De Vrande, V. (2017). Collaborative innovation: Creating opportunities in a changing world. ERIM Inaugural Address Series Research in Management. Retrieved from http://hdl.handle.net/1765/100028
              – Walrave, B., van Oorschot, K. E., & Romme, A. G. L. (2011). Getting trapped in the suppression of exploration: A simulation model. Journal of Management Studies, 48(8), 1727–1751. doi:10.1111/j.1467-6486.2011.01019.x
              – Zwaan, B. van der. (2016). Haalt de universiteit 2040? Een Europees perspectief op wereldwijde kansen en bedreigingen [Will the university reach 2040? A European perspective on worldwide opportunities and threats]. Amsterdam, the Netherlands: Amsterdam University Press.

              Innovation Management Canvas

              As part of a simulation game on innovation management we have been running at universities and in corporate training programs for over 4 years now, we have developed an integrative model for dealing with innovation management on a daily basis. Innovation Management is a strategic activity that isn’t necessarily needed to implement throughly for every company. Mostly large companies have included structured processes that include administrative stages to following the (large number of) project that are in progress and to be able to follow-up on them and calculate the effect of innovation management in general. For smaller companies however, that is not general practice: having such a formal process in place simply doesn’t weigh up to cost efficiencies will generate. But for them, innovation management is just as important – but they rather use a toolkit than a formal process. Based on our 8 Types of Innovation Processes model this is a useful canvas design that makes it easy to start working on formalizing your innovation activities and processes in your organization.

              Based on three categories – value creation, strategy and operations – you would be able to start improving the activities of your organization.

                Business Model Generator

                The use of Open Innovation is to a large extent related to the rise of technology. Not only does technology smoothen Open Innovation, also the adaption of new technologies to the core business (model) can be accelerated by participating in Open Innovation networks. In fact, when talking to businesses the questions that they have do almost never directly include the use of Open Innovation as a goal. It’s assumed a logical and necessary step to take when dealing with actual problems. Simply said, many innovation questions that companies have follow the simple pattern: ‘How can we [change something] in order to improve our [business model construct] in line with [trending topic]?’. These are as such practical, design-oriented questions, not about why [something] happens, or what is the effect of [something] but about how to change to adapt to that [something].

                Given that, without having done a lot of research, I created a small game that you could use in workshop, team meetings, or whatsoever, to lead the discussion in the right way. The [change something] is about Open Innovation, the [trending topic] is about smart technologies. Why? Because I think that covers most of the current questions available. Below, you’ll find the result.

                How can we…

                …with partners or customers to improve our…

                …with the use of…?

                Do you like it? I’m looking for great alternative ‘wheels’ to create. Drop a suggestion and I’ll add them to this post. Looking for a more sophisticated game on Innovation & Enterpreneurship, take a look at the simulations of Innovative Dutch.

                 

                 This article was written by Jan Spruijt. Jan Spruijt is a senior lecturer and entrepreneur in Innovation Sciences. Connect with Jan to stay in touch:

                 

                11 Artificial Intelligence Eureka!s for Organizations: my recap of World Summit AI 2017

                In 1990 Kurzweil instantly incubated the way we think about Artificial Intelligence (AI) with his work The Age of Intelligent Machines. While there is now, almost 30 years later, still a long road ahead of us, the technology readiness level of AI is getting significantly closer and many applications are trying to implement AIs state-of-the-art features and starting to accelerate the creation of the Smart Business: AI-enabled organizations that thrive digitally, are hyperconnected (both digitally and physically), use machine learning and cognitive techniques to work smarter and that are increasingly becoming autonomous organizations. In his 2016 book the Fourth Industrial Revolution Klaus Schwab mentions 6 basic technologies that are based on AI and currently impacting business: 1) the Internet of Things (IoT), 2) Autonomous Vehicles, 3) Advanced Robotics, 4) 3D-printing, 5) new materials and 6) the biological revolution.

                But while AI may speak to our imagination, it is in fact one of the slowest developing fundamental technologies and its current state is still highly debatable. Last week, on October 11 and 12, over 2000 professionals in AI gathered in Amsterdam at the World Summit AI 2017 and discussed the state of Artificial Intelligence and Machine Learning. Among the keynotes were Google, Intel, NASA, ING, Airbus, Facebook, Booking.com, Facebook, IBM Watson, Amazon, Alibaba and Uber. Or as Ebay CPO RJ Pittman said: “The brains in this room are worth trillions of dollars.”

                These are some of the most important insights I deducted from the different keynotes, workshops and many people I met.

                This article was written by Jan Spruijt. Jan Spruijt is an expert in Artificial Intelligence & Business Innovation. He designs business simulations, (game) workshops and academic programs in the field of Open Innovation and Smart Business. Connect with Jan at jan@openinnovatie.nl for opportunities.


                1. Anno 2017, Artificial Intelligence equals Machine Learning.

                While AI has been around quite long – its main incentive is to simulate human thinking and human behaviour – the current applications of AI mostly just focus on Machine Learning. And Machine Learning is, as one of the keynotes pointed out just a form of very advanced programming, not AI per se. Or to say it more clearly: “what we are currently experiencing is not ‘general AI’, it’s just a lot of machine learning on big data”. The screenshot below is from a presentation of Google and show where Google is currently working on when it comes to AI:

                2. From Machine Learning to Cognitive Learning

                The problem with Machine Learning is that it focuses on learning machines how to think or behave like humans in stable environments, with repetitive information. So it works well for for instance face recognition or speech analytics (faces and language don’t change). But this is not how humans think. We also know what to do and what to think in unique situations. This asks for a complete different approach to AI, one that is called Cognitive Learning: a dynamic approach to data analysis and ‘intelligence’. With Machine Learning companies would be able to automate (routine) processes, but with Cognitive Learning techniques companies would be able to autonomously create new ideas, new inventions and new innovations. Images from the presentation of Gayle Sheppard, Intel.

                3. We are wrong about the objective of AI

                Currently, almost all AI is preprogrammed to attain a certain goal. But that’s a huge problem. Because when AI works well, it will thus try ‘to keep alive’ in order to attain its goal. So if humans put on off-button on the machine, in order to be able to still control it, the machine’s first action will actually be to break the off-button, because it thinks that it will likely get in its way when it tries to achieve its preprogammed goal.

                What we actually should try to do is to learn AI that it has only one goal: to maximize the realization of human values. In order do that, it needs to be much smarter. It needs to ask us what‘s right and what’s wrong. It needs to ask us what we want. It needs to learn from our behaviour (see point 2) in order to behave adequately.

                But then again, the question raises to what extent it should maximize our own believes. Too less or too much will result in absurd situations, such as these examples:

                4. Next steps in AI

                According to Ronny Fehling, Airbus, there are a few epochs that describe the road ahead for AI. We’re currently at Epoch 1 (using historical and operational data) and trying to get ahold of Analytical data. But we’re far from the next steps: predictive algorithms for business, descriptive algorithms for business (Epoch 2) and explorative algorithms for business (Epoch 3, more on that later).

                5. We need Open Data. But we won’t get Open Data.

                In order to get historical data (see point 4) and actually be able to learn from that (see point 2), we are in hard need of open data. There are many written and digitalized datasets available, that we can use the analyze human behaviour to understand our values better (see point 3). But the problem is that personal data will be much harder to get, due to tough (and rightfully so!) legislation on data. Or as Miles of Google said: ‘other companies will never get access to our data. Our data is our business. We will decide what you can retrieve from our data (through machine learning) and how you can achieve the data (through APIs).

                6. AI is as good as the data it’s built upon

                Because the current state of AI is that it is mostly based upon Machine Learning, and ML only functions when it can analyze repetitive and structured information, we could argue that AI is only as good as the datasets that it uses. And every company that tries to get into AI should therefore first extensively organize and structure its datasets. Keynotes that got into this fact were Airbus and SwedBank who argued that without thorough data they would never have made steps into AI.

                7. The gateway to AI are APIs

                As argued in point 5, the current gateway to AI for most companies are APIs: Application Programming Interface; small pieces of software that understand the underlying machine learning algorithms. This way, complex machine learning algorithm will become available for software programmers around the world: they can build dedicated apps. These apps can be digital (chatbots, software, business intelligence, intellegent systems), but can also be phyical (advanced robotics, biotech, internet of things, autonomous vehicles, and so on).

                8. The misuse of AI is cybercrime on steroid

                As one of the keynote speakers, Parry Malm, mentioned during the conference: “The misuse of AI is a cybercrime problem on steroids. And at the moment things are not going too well with cybersecurity.” It is a known fact that, although with products like Intel Saffron’s Anti-Money Laundring algorithms, criminals are in many ways always a step ahead of regular businesses and misusing the possibilities of Artificial Intelligence to their own benefit. While AI is hot, cybersecurity is much less so, but should be on the top of each companies agenda. Check this [Cyber Trends Index] of Owlin, another company that presented itself at the conference.

                9. AI isn’t gender-neutral, and that’s a serious problem

                Thursday early morning, there was an interesting panel discussion about AI and gender neutrality. The discussion started off with a few good examples of the fact that most AI-driven applications developed for both professional and personal use are created and developed for males rather than females. But the discussion then took another approach and discussed a much more serious problem: as a result of the fact that in the 80’s and 90’s earliest (game) consoles and PC’s were mostly branded as ‘toys for boys’, an unproportionate amount of males started studying computer sciences. A shift towards a ‘male industry’ started to happen and is still going on. Take the example of this conference, an approximate estimate would say that 95% of visitors are male. And that causes a serious, and ethical, problem – because Artificial Intelligence is not gender-neutral. And with artificial general intelligence in foresight, that would mean that a one-sided approach to intelligence could have an enormous impact on society – while a diverse approach would be much more needed. Interesting discussion: will the future artitificial intelligence be male or neutral?

                10. AutoML and Autonomous AI

                Many companies, amongst which Google, are taking steps on what they call AutoML, the machine learning on machine learning. In essence, that means that it won’t be necessary to create more machine learning algorithms, but that more intelligent algorithm learns about the effect of its own predecessors and automaticallys starts to create better algorithms. This technique, though still far off, could be a path the artificial general intelligence.

                11. When artificial general intelligence starts supervising AI: singularity

                As always, there was a lot of reference to technological singularity: the moment when artificial general intelligence becomes more sophisticated than human intelligence. General consensus is that we’re still far away from singularity, but that we’re getting closer. Some advanced robots already have the function of controling or supervising other robots. Though only in its own specialization, this is a form of singularity within that specialization.
                But we’re much closer to singularity than you might think: in a keynote of NASA, one of their most recent projects is creating an interstellar space station, that will take dozens of years to reach its destination. Because communication will be extremely delayed, and humans won’t survive for such a long time, the ship will contain AI-driven robots that gather research data. But the fun fact is that these robots will have to sustain on itself for a long time, they will have to find new ways of doing research based on what they find, they will have to deal with unforeseen circumstances, and so – all without human intervention. Although interstellar, this is definitely a form of singularity. If they run into extraterrestrial life, what will they do? What decisions will they make? What will we think of those decisions when we finally hear about them years after?

                All in all a very inspiring and interesting conference!

                33 Routes to Open Innovation

                It has been a while since Henry Chesbrough coined the term Open Innovation and formulated it’s definition: “combining internal and external ideas as well as internal and external paths to market to advance the development of new technologies.” (Chesbrough, 2003). In the course of time, the terminology surrounding Open Innovation has evolved alongside developments in management literature and practises. Open Innovation as a paradigm on itself is on its quest to touch base. Rather than taking a (technical) process-oriented approach, Open Innovation is now also about Open Business Models (Chesbrough, 2006), Open Services (Chesbrough, 2010) – both from a more strategic perspective – and practical tools (Vanhaverbeeke, 2017) – more from a tactical or operational point-of-view.

                While it could be argued if Open Innovation is the best approach to be used as a general framework to put different strategic, tactical and operational activities into perspective, it is useful to try. So that’s what I did below: I used to initial Open Innovation framework, based on the innovation funnel, to describe and position a long, but non-exclusive, list of activities that are related to Open Innovation. Of course, also other frameworks could be used to do so, but this seemed like a solid approach.

                The infographic includes 33 routes to Open Innovation, ordened by:

                • the level of involvement of partners (upper half) and clients (lower half): the closer the activity is to the funnel, the more involvement is required to succeed.
                • The size of the circles are partly intuitive, partly evidence-based, and describe to current usage of the phenomenen or in some cases the current impact of the phenomomen.
                • Also note that some of the ‘activities’ are rather ‘systems’ that could be tapped into to use it as a source of innovation in stead of an activity that you’ll have to organize and accelerate yourself.

                  The goal of this framework: to give you an idea of all the possibilities that come with Open Innovation, where you could start and in what stage of your internal process it comes in (most) handy.

                  Partner Activities:

                  Route 1: In-licensing

                  The process of sourcing for external knowledge, patents or technology and to formalize the use of that information in your own innovation process. The ‘license’ often include information about the collaborators, how the risks are shared, how the pofits are shared and to what extend the technology or information may or may not be altered or adapted.

                  Route 2: Co-patenting

                  The process of collaboration between inventors and joined registration for a patent that may be used for further exploration and exploitation onwards. The effect has been studied by for instance Belderbos and it also an indication of the strength of (inter)regional collaboration, according to OECD.

                  Route 3: Spin-off

                  A spin-off is a form of Open Innovation in the sense that a company can ‘spin-off’ a newly developed technology to the public market for further exploitation by the involved engineers or startup team. It thus a technique to split off an early innovation in the hope that, when it leaves it mother’s wings, it will become more successfull on his own.

                  Route 4: Collaborative Innovation

                  Collaborative Innovation is a branche within Open Innovation that studies the effect of temporary Open Innovation-projects with a single goal in mind, such as the creation of a new product or the development of a new service. It is as such not a paradigm but a program management method. Vareska van de Vrande was recently appointed as professor of Collaborative Innovation at the Rotterdam School of Business.

                  Route 5: Co-engineering

                  Collaborative engineering: a term mainly used in conventional manufacturing and production industry, with a focus on collaboration between two or more partners in the full process of design, engineering and manufacturing with multidisciplinary teams and supply chain integration.

                  Route 6: Co-learning

                  A different approach to open innovation because it is more about HRM and than about the processes itself that become open. Co-learning is about the collaborative learning platforms or trajectories for personnel in order to gain new skills, both on operational level as on more tactical or strategic levels. The knowledge than flows back into the company making the influx of knowledge applicable to business processes. For instance: Faems (2006) and Rowley, Kupiec-Teahan and Leeman (1983)

                  Route 7: Spin-out

                  A spin-out differs from a spin-off in the sense that the technology or startup-team is moving to another ‘mothership’ in the form of an acquisition, merger or (most likely, because the former two usually don’t happen at this early stage) a joint venture.

                  Route 8: Open Innovation-based Business Models

                  Basically, this is about having a business model in place that exploits the opportunities that arise because of Open Innovation. Businesses with Open Innovation-based Business Models usually are trying to take the place of innovation intermediaries in Open Innovation networks. They can for instance be inventors with the sole purpose of registering and selling intellectual property. Or they can be network brokers. More information in Weiblen (2014) and Chesbrough (2010) when he describes these companies as merchants.

                  Route 9: Out-licensing

                  Out-licensing is one of the most important strategies within Outbound Open Innovation. Outbound Open Innovation is a core principle of the Open Innovation Paradigm and includes for instance also spin-offs and spin-outs. Out-licensing explores gainin external rewards for internally developped technologies. More information: Lichtenthaler (2009).

                  Route 10: Co-design

                  This approach could also have been placed underneath the funnel: co-design usually happens with both partners and customers and is meant to have a more human-centered design approach in your R&D-funnel. It has become a main topic of research within design thinking. More info: Steen, Manschot & De Koning (2011).

                  Route 11: Open Business Models

                  Open Business Models are all-inclusive approaches to Open Innovation: “Open Business Models take a broad perspective of ‘resources’ that are exchanged and shared with the ecosystem. […] It is seen as an ecoystem-aware way of value-creation and capturing. (Weiblen, 2014). As such, firms with an Open Business Model collaborate with its ecoystem by building up partner-networks, platforms. The process of ‘opening up the business model’ is often referred to as Business Model Innovation.

                  Route 12: Open Business

                  Although the term is almost the same as the before-mentioned approach, ‘Open Business‘ is something completely different. An Open Business embeds a business model that aims to publicly share all data and information. It is related to open source, freeware and open science.

                  Route 13: Co-branding

                  Collaborative branding refers to the fact that a network of organizations join to create a synergetic branding effect. In many case they will create a joint brand that replaces the current product or company brand in order to gain a larger scale effect of the brand. This process is very common in public networks (such as Brainport, the Netherlands, were many companies use the brand Brainport rather than there own branding), but also works out for business-only partnerships, such as the Douwe Egberts and Philips co-brand Senseo. A related term is co-promotion.

                  Route 14: Co-production

                  Co-production – or co-manufacturing – is largely the same as co-engineering except from the fact that it focuses only the production part of the process, thus enhancing economies of scale and cost reductions in (mass) production environments.

                  Route 15: Co-marketing

                  Co-marketing, like co-branding, is about creating a synergetic effect in the commercialization stage of the innovation process. Collaborative marketing focuses on sharing distribution channels and pricing information. It involves joint teams of marketeers bringing to market different products from differnt companies.

                  Partner systems:

                  Route 16: Sectoral Innovation Systems

                  A sectoral innovation systems describes the complete institutional environment, whose aim is to accelerate innovation and employability in a certain sector. In the EU, sectoral innovation systems have been a main focus point of both international and national programs over the last two decades. It’s effects still have to be proven.

                  Route 17: Shared Facilities

                  The availability of facilities that can be used by networks of companies. From an inbound approach, a company could make use of machine labs, printing labs or hubs with design and production lines; from an outbound approach, companies could share their facilities with others. It contributes to Open Innovation because of the fact that when using these shared facilities, often new combinations or ideas arise. An example of a shared facility is the Holst Centre in Eindhoven.

                  Route 18: Regional Innovation Systems

                  A regional innovation describes the institional environment, whose aim is to accelerate innovation and employability in a certain (geographically bounded) region. An example is Brainport. I’ve previously written about regional innovation systems.

                  Route 19: Business Ecosystems

                  These are ecosystems that are created and driven by businesses. Another term would be clusters. While business ecosystems are more likely to be created because of commercial opportunities (and as thus may be actually quite ‘closed’ and could prevend Open Innovation from happening), they could also be created with the purpose of Open Innovation in mind.

                  Route 20: National Innovation Systems

                  Same as regional, but than national 😉

                  Route 21: Fieldlabs

                  Field Labs are collaborative working places where businesses and knowledge institutes meet to create and develop new ideas. It’s primarily a place where students can work with professionals to create new products.

                  Customer activities:

                  Route 22: Crowdsourcing

                  The activity of ‘sourcing’ the crowd: gather opinions, ideas, drafts, suggestions and information from the general public, sometimes – but not always – targeted to specific crowds, such as your current customers or users, a group of elite users or targets platforms (such as designers). Crowdsourcing is effective in the early stages of an innovation process because of the fact that it per definition a diverging activity and it results in a wide variety of options to choose from. The technique is not focused enough to be of use later on in the process. Be aware of having enough resourses avaiable when starting a crowdsourcing campaign, as it may go viral and require lots of hours to manage and react. As it is a form of ‘brainstorming’, the general rules of ‘brainstorming’ also apply to crowdsourcing, which includes taking every idea or opinion seriously.

                  Route 23: Crowdfunding

                  Based on the popularity of crowdsourcing, crowdfunding was firstly introduced in the beginning of the 21st century in the US. Its principles are the same, but the main ‘source’ you’re looking for is not ideas or opinions, but finance for your project. Crowdfunding platforms, just like crowdsourcing platforms, deal with intellectual property rights, commons and other legal issues that come into play when dealing with using external work for your project. Crowdfunding is a hugely popular technique but has very low success rates, because of the lower entry barrier.

                  Route 24: Open Data

                  This is more a philosophy than a concrete activity, but at least it is fair to say that the process of opening up your data and tapping into open data is an activity. Increasingly popular in software industry, public institutes and educational institutes, opening up (big) data creates opportunities for organizations that otherwise wouldn’t be able to see and use that data. Searching for and using open data is an effective and efficient Open Innovation tool. Wikipedia states, although it misses a source, that “Some make the case that opening up official information can support technological innovation and economic growth by enabling third parties to develop new kinds of digital applications and services.”

                  Route 25: Co-creation Labs

                  Co-creation labs are almost identical to Fieldlabs, except from the fact that co-creation labs are mainly intended for the public to participate (customers, local civilians, et cetera). Co-creation labs are an effective way to gather feedback on newly developed prototypes and get ideas regarding branding and marketing.

                  Route 26: Co-creation

                  The term of co-creation is used for a whole lot of different purposes, but in the context of Open Innovation is points to the fact that organizations deliberately seek contact with end customers to test and validate new ideas and prototypes and to gather new ideas for bringing the product to market. Although not intended as such, co-creation, if done right, is also an accepted marketing technique: it engages customers with your product.

                  Route 27: Community

                  Communities are groups of highly engaged customers, usually voluntarily involved with your product because of personal interest. Searching for and collaborating with these communities may increase new ideas. Lee et al (2011) argue that communities in the example of Lego, have an automatic filtering, for instance through fora, of ideas and these ideas are as such much more worth looking at than for instance ideas generated by crowdsourcing.

                  Route 28: E-Participation

                  Primarily a public or governmental activity, e-participation tries to involve the public in (usually) gathering feedback on delivered services. It also works for companies because gathering feedback helps in validating and incrementally increasing the quality of products.

                  Route 29: Open Source

                  Much related to Open Data, Open Source is a philosophy adopted by software engineers to generate sources codes that are freely available. This doesn’t mean that there isn’t any commercial activity involved: while the source code may be open to the public for use, only developers will understand it – and thus commercial activities can be exploited when making the software available for the public. Examples of Open Source projects are Wikipedia and WordPress.

                  Customer systems:

                  Route 30: Co-working spaces

                  Increasingly popular, mainly because of the growing number of freelancers and self-employed personal, co-working spaces are actually an excellent place to start networking and source for new ideas. Because of the diversity of specialists working in those places, you are more likely to gather diverse ideas, which work best in the early stages of the inonvation process. In cities such as Amsterdam co-working spaces pop-up all the time, so it’s worth to search for a space that is as diverse as possible and offers also opportunities to chat and discuss.

                  Route 31: Collective Intelligence

                  This is the fundamental construct behind crowdsourcing: the idea is that the ‘collective intelligence’ always outperforms individual intelligence, even of the most awarded geniuses in your expertise. Tapping into the collective intelligence is therefore a useful activity.

                  Route 32: Smart Cities

                  The concept of Smart Cities is based around the ICT-perspective on ‘intelligence’: a highly digital, hyperconnected accessible information society in which broadband is present and the main industry focuses on services and online activities. Smart Cities are a cosmopolitan view on the world, but being located in one of them opens up a wide range of opportunities for innovation.

                  Route 33: User Engagement

                  The last route to Open Innovation focuses on the end users of your product or service. User engagement is widely researched as a highly effective approach to Open Innovation. This involves (creative) user research (Kumar) and Lead User Involvement (Bogers).

                  I’m quite sure there are many more techniques. Please feel free to add them and to indicate how to could be included in the graphic and I’ll update it.