Category Archives: OI Practice

Articles dealing with the Open Innovation practices

Knowledge Transfer between Academics And Companies In Open Innovation

NCUB made a research commissioned by the Technology Strategy Board and Research Councils UK on the benefits of academic and business knowledge transfer partnerships through a model of best practice (you can get the full Successful engagement in Open Innovation  report here).  The academic expertise engaged with British businesses so as to improve their competitiveness and performance. The knowledge transfer (KT) partnerships has been shown to be particularly useful for universities wanting to engage with SMEs that do not have enough expertise and resources to manage an open innovation partnership.

There were challenges in the open innovation process. According to a study done on innovation , 65 per cent of UK businesses don’t like the long-term nature of academic research. Fifty-five per cent cited regulations regarding confidentiality or intellectual property. More significantly, the study suggests that UK trends in academic-industry engagement in innovation may be going in the wrong direction.

Factors that businesses cited to being a hindrance in creating a smooth relationship between the two worlds, in this study, were researchers focusing on achieving strong and leading hedge results while businesses were okay with 80% solutions, the speed of working  between the two or even un-shared expectations leading to barriers and broken trust and many others. Academics on the other hand found that they were at times insufficiently rewarded, there was too much academic bureaucracy or a lack of experience in dealing with external partners…

Generic model of ideal outcomes and attributes of Knowledge Transfer (5 Cs Model) and the importance of the individual who bridge the two communities

The Creative process in the study has been divided into five parts which can be seen below:

Company Opportunity: A business recognizes that there is an opportunity or a problem that it could address if it had access to knowledge and expertise in specific areas and this may come from a university or academic institution.

Co-Recognition: With the match already in place, a formalized agreement on issues such as IP and delivery conditions is sorted out. This agreement process will also involve the Technology Transfer Office of the academic institution (TTO) and legal representatives on both sides. Academic benefits of the collaboration need to be clear at this stage or the academic partner may not have the incentive to invest the resources required.

Co- Formulation – Knowledge from the academic and business domains is synthesized. This requires collaborative working and the building of trust amongst the partners.

Co-Creation: As the project develops, the partners create the opportunity for innovation in process, product or markets.

Commercialization – This is a mark of success for both parties.

During this process there are two people important to the success of the project:

1. Associate role – The Knowledge Transfer Partnership model allows the business partner to supplement their in-house resources through an Associate. He / She is an outsider recruited by the partnership, employed by the academic partner but embedded in the business to work on the project. Training and development for the Associate are a key part of the Knowledge Transfer Partnership.

2. Adviser role – He/ She is provided by the Technology Strategy Board to support the partnership in the development of its proposal and advise on the managerial aspects of the Knowledge Transfer process. Advisers act as mentors in the preparation for and the implementation of Knowledge Transfer Partnership projects and specifically focus on managerial requirements.

Importance of mechanisms that build trust and allow organisational learning

Knowledge Transfer Partnership is particularly successful at helping partners learn by doing and overcome the barriers to absorbing new knowledge and putting it into practice. It is crucial that staff from the business partner invest sufficient effort and time to absorb or embed the knowledge that they have gained. Over half the partnerships studied commented that the Project Plan was valuable.

It was observed that it:

• Offered a structure that stimulated informal contacts but also provided a controlled means of discussing changes to project plans.

• Provided a framework for regular review and reflection, enabling lessons learned to be fed into future planning.

• Encouraged learning by doing or ‘action learning’.

• Facilitated accountability and the clarity of roles and also helped in partnership building.

• Focus attention on the project through the regularity of contact. Regular attendance by the Academic supervisor is particularly important in this respect.

• Allowed wider contacts to be established through the Local Management Committee. This strengthens partnership bonds and enables knowledge to be embedded.

Added value of Knowledge Transfer Partnership

The Knowledge Transfer Partnership provides weekly meetings between the Associate and the academic supervisor on the business premises and monthly meetings between the Associate, academic supervisor and business supervisor. This helped in the communication between the specialist knowledge in a form that is comprehensible to the business environment. It contributes to building a sustainable relationship between the business and academic institution, which is critical for the innovation process.

The table below  summarizes the processes and mechanisms of the KTPartnership and shows how they help innovation partners meet the challenges of knowledge transfer and develop operational strategies for success, at each stage of the innovation process.

process and mechanism table 1

 from:  Successful engagement in Open Innovation, page 18

process and mechanism table2

from:  Successful engagement in Open Innovation, page 21

CONCLUSION

The study has shown that, in the new world of open innovation, engagement with business can bring tangible benefits for the Higher Educational Institutions that go far beyond patents, licenses or academic deliverables. It brings for instance case studies for teaching, new field research methodologies and management techniques. It also helps academics develop the skills and experience necessary for working with external partners in a sustainable fashion all equal to the importance of  business’ goal of commercialization.

One aspect remains unsolved in the article which is how SMEs identify the right academic partners that have the expertise for solving their problems. This is a difficult question that open innovation intermediaries as ideXlab have a mission to resolve.

To sum up the report is an overall diagram showing the value added of the KTP.

value added table

from:  Successful engagement in Open Innovation, page 21

 

cc photos to /blogs.nottingham.ac.uk/

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Open Innovation barriers

Interview with Anders Hjalmarsson of Viktoria Swedish ICT, Götegorg, Sweden. Author of “Beyond Innovation contest: a framework of barriers to open innovation of digital services

 

Q: Hello Anders, thank you for your time today. We would like to discuss the main findings of your work related to the “Open Innovation barriers”, work that you have presented at the European Conference on Information Systems on June 9 2014.

To start with, could you say a few words about yourself, your interest and research areas?

A: Sure, thanks for the opportunity of this interview. At Viktoria Sweedish ICT I do research on Open Innovation applied to the transportation and automotive industry. I work together with public transportation authorities in Sweeden and Europe with the aim of creating a basis for digital Open Innovation based on Open Data.

Similarly we work with the car industry, namely Volvo who has interest in the digital innovation applied to their own industry.

Q: How does this study fit in your own and your department work and research projects ?

A: We have done a number of different research projects since 2009, including the “Innovation for sustainable everyday travel”, a 4M€ project spanning from 2009 to 2011. We created a hub for public transportation’s open data exploitation. The different Governmental organization in Sweden delivered their data and we created the web platform for new application to be developed. In 2010 we started thinking about the way to connect with the developers’ community. One thing we did was to organize a contest  “Travelhack 2013”, under the premise of “making every day transportation more sustainable”.

Q: Can you give us a one or two examples of applications that were developed during this hackathon ?

A: Yes, the winner was a mobile travel planner for people with cognitive dysfunctions. It helps plan the travel in detail from door to door and make public transportation accessible to these people. The first public version is expected shortly now on iOS and Android platforms. Another winner in the “make travel funnier” category is an app that matches your travel position with music that was composed in the area.

Q: These seem to be very interesting apps ! So how is it that your starting point in the paper is that many contests ideas do not transform into actual products or services ? What is the success ratio ?

A: As a matter of fact we have observed in our contest that the majority of these brilliant solutions just died, they don’t turn into viable applications. So we started to investigate the reasons why there was so much “waste” in these digital services contests, and the paper you are mentioning is the first result of this analysis.

We also realized a lack of analysis of innovation contest success and this calls for additional research on this topic going forward. Initially we made a simple comparison of three contests and found an average 9% of viable services (see table 1). When we presented this at ECIS2014 earlier this month we had comments that this percentage is rather high compared to other contests.

Naturally we wanted to understand the reasons for these wasted efforts and try and create a methodology that enables the organizers of contests to generate more viable services and to manage the post contest process to optimize the global outcome.

 OPEN INNOVATION BARRIER 1

 

Q: What was the methodology of your study ?

A: We have proceeded in three main steps:

  1. A comprehensive literature review related to open innovation barriers, we studied 24 papers from the most influential journals and found 179 factors that we have grouped into 10 categories.

  2. We interviewed teams involved in the Travelhack 2013 contest prior to the final and we established a list of anticipated barriers.

  3. We have then contacted the teams again two months after the contest and updated the list and the ranking of perceived barriers.

Q: So what are the main perceived barriers based on your data ?

A: The top 4 open innovation barriers as they were perceived after the contest are:

1) Lack of time or money to pursue the development of the application

People proposed applications because it was fun to participate in the hackathon but had no real plan or resources to take their applications to the market.

2) Lack of marketing competence/information

Most teams involved were from R&D and did not involve marketers so they  lacked half of the competences to move forward.

3) Weak value offering

As a consequence of the previous point, many teams did not work on a business model and realized later that the app was cool but had a limited added value.

4) Lack of partner cooperation for development

Once apps are developed, partners and distributors need to be involved and the teams did not have the capabilities to do so.

A more complete list of barriers and their relative perceived importance is provided in table 8 of our paper.

 open innovation barriers 2

Q: This is very clear and expected to some extent given the concept and organization of Hackathons. But would you say that similar barriers would be found in other Open Innovation approaches like the outside-in solution sourcing ?

A: I would say yes but one must be careful when extrapolating research results to other situations. This would require a dedicated study based on such innovation cases, this is an interesting and unexplored field for us.

Q: Now I suppose that your next step could be to use your framework to propose a method for shaping OI projects so that barriers are avoided or lowered, “by design” I would say ?

A: Yes we are currently developing such a model. It’s important to understand the barriers and be able to lower some but some other need to be maintained or even strengthened. These barriers are needed for the selection process of the best services. Once the objective of the contest is clear then the barriers need to be adjusted to ensure the proper competition and selection. This is the model we are working on now in a two years project just starting.

Q: Our readers and ourselves are very interested to take this one step further and develop best practices to anticipate barriers in our innovation sourcing model. I’m afraid we won’t be able to wait until the end of your project (…), so let me try an exercise and figure what would be the main points to be anticipated

 

Open Innovation barrier

How it translates for solution sourcing

Example of mitigation guideline

1) Lack of time or money

Bad anticipation of maturation/integration time and cost.

An OI project shall be planned and budgeted upfront similarly to internal projects, including a specific “maturing and transfer” stage. Integration time need to be anticipated, reviewed with the provider, and budget adjusted.

2) Lack of marketing competencies/ information

Lack of interaction between R&D and product marketing.

An OI project shall involve the whole product core team (Marketing, R&D, purchasing, Quality). OI projects conducted exclusively by e.g. R&D  have low chances of success.

3) Weak value offering

Over estimation of the targeted innovation market value. Limited teams capability to build a business model.

The project ROI need to be planned and measured, starting with a core team agreement on the target innovation market value.

4) lack of partner cooperation for development

Wrong fit between the solution provider and the seeking company. Bad project execution.

The R&D readiness to engage in Open Innovation collaborations shall be audited. Information and training conducted. Involve professional project management  and quality monitoring, include terms in supplier contracts.

Table 3: ideXlab own elaboration on barriers anticipation

What would be your expert opinion on this, are we tackling the main dimensions with this check list ?

A: Yes, but again the priorities can only be translated carefully until we have studied specific data on similar cases. We will have results based on our study at Volvo in the near future and will keep you posted !

Q: Anders, thank you very much this was very informative. I am looking forward to reading your next research results and let’s keep in touch may be some of our users will be open to share their experience with you if you are interested.

Read the full article from Anders Hjalmarsson at  http://ecis2014.eu/E-poster/files/0211-file1.pdf

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A SUCCESS STORY OF AN OPEN INNOVATION PLATFORM

Open innovation platforms  are a great success when it comes to bringing experts and companies together so that they can innovate. We got the opportunity to interview Justo Puerto Albandoz , a professor of operations research at the university of Seville Spain, who has a research group, Nuevos desafios de la matematica combinatoria,  that worked together with a company X ( information of the company can’t be disclosed ). The two meet through the ideXlab open innovation platform. Here is [tweetherder]the success story of open innovation [/tweetherder]told to us.

1.What is your domain of expertise?

Our domain of expertise is the analysis and design of complex networks. This includes location, optimization of supply chain networks and other relative subjects. These problems involve a series of different fields that lie within applied mathematics. We have some expertise of analyzing different networks like technological networks, some biological networks, information networks and social networks. Our major expertise is in the analysis of technological network essentially distribution networks, routing problems and the likes.

– Why is it important?

The study of networks, in the form of mathematical graph theory, is one of the fundamental pillars of discrete mathematics.  Typical network studies in sociology involve the circulation of questionnaires, asking respondents to detail their interactions with others or in logistics the organization of routes, schedules and timetables of personnel vehicle fleets of jobs in production plants. One can then use the responses to reconstruct a network in which vertices may represent individuals and edges the interactions between them. In the same form, the optimized plans of actions are used in logistics  networks  to increase benefits or reduce costs. Typical social network studies address issues of centrality, connectivity and optimization. Our focus is twofold to consider the design of networks seeking for amenable properties that allow effective optimization of several aspects on those structures and to shift to the consideration of large-scale statistical properties of large scale networks.

 – Where does it apply?

Our expertise and methods can be applied to solve problems of many kinds in different complex structures. Specifically, they are directly applicable to Social networks, Information networks, Technological networks, Biological networks and Logistics networks.

2. Why is it sometimes difficult to get your expertise known and recognized by industry players?

Our role is basically teaching and doing basic research so our communication channels remain around the academia people who are working in the same environment as us. The only way to contact industrial partners is either by chance or by collaboration with partners we previously contacted. Usually we are in contact with companies where former students are working. So the possibility to work with industrial partners is very limited.

 – How did ideXlab help?

IdeXlab put us in contact with an industrial partner that was looking  for a solution for a particular problem that was exactly in our field of expertise. Without this contact it would have been impossible to work with them. We are in the south of Spain in Seville and our partner is a company working in France.

 3. Does the university or the government encourage you to work with the industry or is it your own initiative?

There are no such initiatives. In principle, there should be some incentives for us to work with industries because this produces a return that goes into the universities. But in terms of promotion in the universities, most of the time, it isn’t by industrial contract but by technical publication.  So if you are a scientist that is looking for better positions in the universities then there aren’t real incentives for doing contract with industries. But on the other hand, when you are coordinating a big group of researchers or a group of people who have achieved some degree of seniority, there is some incentive for you to do contracts with industries because you get in touch with actual problems that, in fact, help you in developing new techniques and new research that will go into the technical knowledge in your field.

4. What did your project entail with the company we put you in touch with?

I can only say a few words about this because we have a non-disclosure contract with the company. But I can talk about the field of collaboration. This company asked us to develop a new management tool for the control and design of future metro networks. We developed a tool that was based on an optimization framework that was able to cope with the different types of metro networks and could be used in the future to improve management and control of complete networks.

5.How would you qualify the relationship created with the company now?

We are satisfied with the company and we finished our first collaboration by October 2013. We are now looking for new extension because after the final delivery, we internally thought of the problem in a more theoretical point of view and we have developed an even better solution. So this open innovation experience created some additional appetite on our side and this is the way it should be.

6.  Had you ever heard of open innovation before? If so, explain to us in a few words, your thoughts on it?

Before being in contact with ideXlab, we had no idea what open innovation was. This was the first question I asked Jean-Louis (CEO of ideXlab) because so far the way we were contacted by the industry was completely different. Either someone who knew us would either come to the office or phone us, send us a letter directly from the company. In this occasion the contact was different because we were contacted by an intermediate company that was looking for a solution for another company and this was something we understood after the first meeting with ideXlab but we didn’t have a previous experience with this type of collaboration.

I think open innovation is a great idea. I would recommend it to other people. If there is a company which is an expert in looking for solution for industrial companies then it’s something that could help both sides and this is very important.

 

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Implementing swifter Open Innovation thanks to the C-K design theory PART 1

The C-K design theory is a theory of reasoning in design, that aims to provide a single yet domain-independent approach to innovators seeking to streamline their innovation workflows.

It builds on classical design theories, then goes beyond problem-solving by stimulating truly innovative design. This is due to the fact that it includes the creative, surprising and serendipitous aspects of design.

According to the C-K design theory, the design situation can be represented by two different spaces : the Concept space (C) and the Knowledge space (K). The Concept space is the space of the unknown, where ideas only exist. A Concept, in the C-K paradigm, is an object characterized by a certain set of properties that the creative designer can’t tell if it exists or not. Actually, it could exist since nobody can prove it is impossible it doesn’t – yet it does. A concept can’t be “decided”, thus is a seed of creativity. For instance, an “anti depressive toothbrush” is a concept : I dare you to show me one, yet we might as well create it together.

On the opposite, the Knowledge space (K) is the space of what is known for sure. It is filled with existing objects, laws of physics, past observations… Every object or assertion in the knowledge space has a logical status, i.e. is true or false. You know that “a toothbrush is used to brush one’s teeth” and “depression is a mental disorder”.

By associating knowledge to the initial concept, we can build on it by specifying it further. For instance, “laughter can help beat depression” is knowledge that I associate to the “anti depressive toothbrush” concept. I can then partition my initial concept between “anti depressive toothbrush that use laughter”… and the others!

Therefore I can’t get fixed on my initial idea of using laughter for my toothbrush, since I had to write down that those not using it could as well be designed. C-K design theory prevents fixation effects therefore boosting creativity.

ck image

If you’re designing a chair, some knowledge could be “most chairs have 4 legs” and you would then partition your initial concept into any-number-of-leg chairs (even a no leg chair !). Those are called “expansive partition” since they break the dominant design of the chair object.

The design process concludes when a concept far down in the concepts tree calls for knowledge creation, and this newly acquired knowledge enables fulfillment of the concept.

What about Open Innovation then ? How does the C-K design theory relate to it ?

In a certain light one could argue that both Concepts and Knowledge can be exposed in most Open Innovation workflow. Seekers carry Concepts: they have ideas, but are unable to make them real. They lack the proper Knowledge to make their concept come true. Seekers reach out to Solvers with a description of this missing Knowledge, hoping to find an expert with the Knowledge that fits their Concept and turns the initial idea into reality.

However this classical implementation of Open Innovation doesn’t take advantage of the C-K design theory to its full : it doesn’t provide a way for the Seeker to reassess it’s initial concept, or for the Solver to build on it expansively. Maybe another road in the implementation of Open Innovation to experiment with ?

 Stay turned for the upcoming part 2!

 

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WEBINAR VIDEO: THE TOP 8 IMPLICATIONS OF OPEN INNOVATION IN YOUR ORGANISATION

The president and co- founder of ideXlab, an open innovation platform that automates intermediation between experts and enterprises, shares the experiences encountered by companies wanting to implement open innovation since ideXlab is in constant dialogue with them and explains the main dimensions that need to be understood.

“[tweetherder]INNOVATION DISTINGUISHES  BETWEEN LEADERS AND FOLLOWERS[/tweetherder]”

 

Download the whitepaper used in the webinar video:  http://www.idexlab.com/en/whitePaper

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