(The following article is reproduced courtesy of Exnovate www.exnovate.org)
Innovation is increasingly the result of collaborative efforts between different organizations. We examined all research projects of a European based multinational: the company has been increasingly collaborating with a wide range of innovation partners to successfully conduct its research projects. Tapping into external sources of knowledge is not new, but this large company started to record the collaboration activities for its research projects since 2003. This resulted in a large database with more than thousand research projects.
We studied the impact of collaboration with external partners on the performance of these projects. Research project success was measured in three different ways: transfer volume, innovation speed, and financial impact of R&D projects. We were interested in the effect of collaboration with external partners on projects’ success. We distinguished between so-called science-based (e.g. universities and research institutions) and market-based (e.g. customers and suppliers) partners.
Effect on transfer volume
Open innovation research projects create more technology transfers than closed innovation research projects.
Effect on Innovation Speed
Second, we zeroed in on transfer speed and the time to reach business success. We do not find convincing evidence showing that collaboration with external partners is accelerating the transfer speed. In contrast, collaboration with market-based partners (eventually in combination with science-based partners) speeds up the transfer process, while collaboration exclusively with science-based partners does not influence projects’ innovation speed.
Effect on Financial Impact
There is also strong evidence that open innovation projects are creating more business value than closed innovation projects. This is the case for collaborations with technology-based partners as well as with market-based partners.
Open and closed innovation research projects are different in many ways. Open innovation projects are larger, more costly and have different objectives than closed innovation projects. Open innovation projects are “open” because of technological complexities and market related knowledge that is not available in the company. Closed innovation projects are on average more incremental in nature. Likewise, collaborations with science-based and market-based partners are quite different from each other. Market-based partners contribute more to larger transfer volume and the acceleration of the innovation speed than science-based partners, while the latter are more valuable in the creation of large business successes. We also find that a more formalized project management and spending more internal research time lead to greater business success when the company collaborates with market-based partners. The opposite is true for collaboration with science-based partners: universities and research labs can be loosely managed. This reflects the routinized cooperation and standardized interface between large companies and their research partners.
Choosing the right open innovation mode
Open innovation projects affect research project performance differently than closed innovation projects. Likewise, collaboration with technology-based partners leads to different benefits than collaboration with market-based partnerships. Therefore, managers should carefully choose between open and closed innovation and between different open innovation modes when they start a research project. The company usually chooses for instance to collaborate both with technology and market based partners for larger or more complex projects while closed innovation is usually used for smaller projects. The analysis of the effects of openness on innovation performance indicates that it is imperative for managers to optimize the choice between open and closed innovation projects and the type of collaboration which is required. A wrong choice between these innovation modes will lead to suboptimal-levels of innovation performance.
Choosing the right timing
In a follow-on study we examined the timing of collaboration in open innovation projects. Timing of R&D collaboration is crucial for the performance of research projects. We find that managers should pay attention to when and how long they collaborate with partners: a firm should not collaborate with all partners all the time. Optimal research project results are obtained when the company collaborates two-thirds of the project lifetime with market-based partners, while the duration of collaboration with science-based partners is not affecting the outcome. Research projects also benefit from continuous collaboration (without interruptions) with market-based partners, but the opposite holds for collaborating with science-based partners. In other words, collaborating with market-based partners should be done in a continuous way without interruptions, while it may be more beneficial to collaborate with science-based partners over different periods during the research project.
There is also evidence that a firm should not start to work with different types of partners at the same time; sequencing collaboration has its advantages. Research projects are performing better when collaboration with market-based partners takes place at the end of the project, while R&D collaboration with science-based partners is beneficial when it is organized at the beginning of a project. Initiating collaboration with science-based partners in a late stage can harm the innovation performance of research projects.
How open innovation data help you in making better managerial decisions?
Open innovation has been around for a decade. Yet there is almost no hard evidence based on large-scale databases about research projects or other innovation activities where open innovation may play a crucial role. The database we examined shows that longitudinal data about research projects can be very useful in measuring the performance impact of open innovation activities during these projects. The more detailed the data the deeper we can dig into specific mechanisms why open innovation is productive in particular circumstances and not in other ones. Collecting data over extended periods of time results in a wealth of data, which provides evidence about the transfer speed, and time to generate a business success. The data also provide ammunition to make decisions about the timing when to establish or end partnerships. Companies should not only know with whom they have to partner in different types of research projects, but also when, how long and in which sequence.
The company we examined has benefited considerably from analyzing the research project dataset. This hard evidence about the benefits and challenges of open innovation was a powerful instrument in reporting to top management and it helps to legitimize open innovation initiatives in a corporate context. We encourage other companies to set up similar databases and to share it with open innovation experts. The analysis by our research team proves that this type of data also advances our academic understanding of open innovation considerably.