Open Innovation and Public Policies in Europe

Henry CHESBROUGH & Wim VANHAVERBEKE, fathers of Open Innovation have just released a very insightful report on Open Innovation and public policies in Europe, in collaboration with Tuba BAKICI and Henry LOPEZ-VEGA.   Abstract of the report Industrial innovation processes are becoming more open. The large, vertically integrated R&D laboratory systems of the 20th century are giving way to more vertically disintegrated networks of innovation that connect numerous companies into ecosystems. Since innovation policy ultimately rests on the activities and initiatives of the private sector, it is vital that policy follows this evolution. Previous innovation policies relied on large companies to act as the engines of innovation in the EU. While large companies remain quite relevant to innovation within the

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A Novel Approach for Open Innovation Platforms: Multistep Dynamic Expert Sourcing : Conclusion

In the first section, the main drawbacks of traditional problem solving platform were presented. These drawbacks are the difficulty to attract and engage sufficient number of qualified experts, the difficulty to efficiently match seeker problems with potential experts, the difficulty to give the seeker convincing incentives to reward all promising solutions and the difficulty to manage confidentiality and to ensure a reliable transfer of intellectual property. In the second section, we presented the disruptive approach that we have developed and implemented. The Multistep Dynamic Expert Sourcing approach relies on both an innovative Expert Search Engine technology and a three-step process for the actual problem solving phase. The Expert Search Engine ensures a relevant matching between experts and problems and avoids

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Main advantages of Multistep Dynamic Expert Sourcing

Implementing the MDES approach with the right technological and methodological assets (i.e. a powerful Expert Search Engine and a relevant methodology to enhance problem formulation) presents strong advantages for experts, seekers and Open Innovation intermediaries (problem solving platforms). For the experts Get relevant problems without having to register Ensure the protection of their intellectual property Avoid irrelevant work and frustration Get the guaranty to be paid for their work For the seekers (the client companies) Have a better control of confidentiality Get potential access to tens of millions of worldwide experts Engage more and better experts Get better solutions For the intermediaries (the platforms) Decrease cost structures Increase scalability Build a reputation of quality & trust Improve the solving rate

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Multistep Problem Solving Process

Multistep rather than black box The multistep approach allows to engage experts and to satisfy seekers, in a safe, secured and trustable environment. It ensures better performance for problem solving, less frustration among solvers and less disappointment among seekers. Online problem solving is often thought of as a “black-box process”: the seeker gives the input, waits a few months and finally opens the box to discover the set of submitted solutions. This process implemented by nearly all Open Innovation platforms has various drawbacks, the main ones being: motivation decrease and risk-averse behavior of solvers, inefficiency to reach quality and relevant solutions, limited incentives for seekers to be fair and pay for all valuable solutions. We propose instead a three-step “gray-box”

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Expert Sourcing and Dynamic aspect

Expert sourcing rather than crowdsourcing To solve highly critical and technological problems, a company needs experts, not random solvers. This is why MDES relies on highly skilled experts rather than solvers. Dynamic (or On-Demand) rather than subscription-based As previously explained, most Open Innovation intermediaries or network of experts rely on registration-based platforms: potential experts have to know about the platform and to register. This is not the right paradigm to address global expertise. Instead, we propose to build a worldwide automatic network of Experts that can be solicited on-demand and that allows automatic profiling of experts. In addition, this paradigm enhances confidentiality, since the visibility of the problems can be restricted to preselected experts (and not to any registered solver).

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A disruptive approach: Multistep Dynamic Expert Sourcing (MDES)

Expert Sourcing The French startup PRESANS developed and implemented the Multistep Dynamic Expert Sourcing (MDES) approach. It relies on a combination between a state-of-the-art web-mining technology and a secured multistep problem solving process. In this approach, experts do not register, instead, the various digital tracks they leave on the web allow to detect and to invite them on-demand to tackle most challenging technological problems. MDES has strong advantages for the platform, for the experts and for the client companies. The pillars of the Dynamic Expert Sourcing Approach As illustrated in Figure 1, the MDES approach seeks to bring value to seekers by connecting their needs to worldwide scientific knowledge & intelligence. The MDES philosophy holds on 3 points: Expert Sourcing Dynamic

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Confidentiality and intellectual property issues are poorly addressed

Confidentiality. This is the big issue on the client side Companies using Open Innovation Platforms are mostly concerned by the confidentiality of the information they provide to the intermediary and to the rest of the world. Even if the name of the company remains anonymous, it may be easy for competitors to guess who is behind the problem, which is a serious issue. Putting a problem online and broadcasting it to a priori unknown solvers is too sensitive for many companies, and is a significant break for the use of Open Innovation platforms. Online Non-Disclosure Agreements proposed to solvers is not a satisfactory solution as anybody can sign them without real engagement or verification. Intellectual property (IP) management is yet

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Matching algorithm and process fail to engage solvers into problem solving

How can we ensure that the Need is broadcast to the right potential experts, without narrowing or broadening too much the broadcast and and do we ensure that we manage to engage these potential experts? Tradeoff between targeting and large broadcast To maximize the probability to solve a problem, a tradeoff has to be found between strong targeting of the solvers and large broadcast: even assuming a large base of solvers, it is merely impossible to guaranty their engagement into problem solving. To engage solvers, one needs to contact them (for example by email) to “advertize” the problem. Given a certain problem, who should be contacted? All the solvers in all the fields? Solvers whose online profile shows a certain

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Registration Yields a Weak Number of Solvers

As an intermediary, how can you constantly find new and interested solvers around the world (i.e. people ready to propose solutions to technological problems)? You need efficient incentives to attract them on your platform, to make them subscribe, to engage them in solving problems and to release intellectual property (IP). Registration has a direct negative impact on the number of solvers, although it may be, in principle, a nice way to select supposedly motivated solvers. Few experts register on crowdsourcing platforms How many experts go and register on crowdsourcing platforms? 10,000? 100,000? Most famous platforms hardly reach 300,000 solvers, which is far less than the tens of millions of experts in the world. In addition, figures claimed by many companies

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Where Open Innovation Crowdsourcing Platforms Fail

The way Open Innovation crowdsourcing platforms work is rather simple, at least in principle: they help companies to match a problem or a need to an existing solution or to somebody able to solve the problem. Underlying requirements are (i) a large pool of problems, (ii) a large pool of solvers[1] and (iii) a good matching algorithm or process. In addition, the lubricant for all that to work is trust, which here means careful management of confidentiality and intellectual property. However, behind this simple idea, a lot of theoretical and practical difficulties have emerged from a decade of experimentations. The 3 upcoming articles on Open-Your-Innovation.com detail these points: REGISTRATION YIELDS A WEAK NUMBER OF SOLVERS MATCHING ALGORITHM AND PROCESS FAIL

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