CASEIA study investigates serendipity in innovations from research institutions and businesses and makes recommendations for the future
How do EU innovation programs affect the socio-economic impacts of European research institutions? This complex question was addressed by the CASEIA (Comparative Analysis of Socio-Economic Impact) project, which developed an analytical framework and methodology to better understand these impacts. The methodology has the potential to improve the planning and evaluation of similar innovation programs in the future. The project was led by Dr. Sonia Utermann, an expert at the Steinbeis Transfer Hub in Berlin. It analyzed three case studies in order to evaluate the impacts of the Horizon 2020 ATTRACT project.
One challenge that always confronts innovations from research institutions is bridging the gap that exists between the “deep tech” they develop and its commercial implementation and application. Cultural factors are among the many causes of this gap [1]. The ATTRACT project aims to bring these two worlds closer together. This Horizon 2020 project to boost innovation recognizes that research institution culture is not accustomed to design thinking, and aims “to instill an entrepreneurship and co-creation mind-set” [2] in them. However, the project lacks an overarching framework for measuring its impact. This was addressed by the CASEIA study, which developed a pilot framework.
Methodology and analytical framework
The project team undertook three case studies involving a project driven by a research institution [3], a project carried out by a business [4] and a control case that was not part of the ATTRACT project [5]. The aim was to identify and understand the impacts of the ATTRACT measures.
Interviews shed light on the network between the stakeholders in each case study. The relationships were described using factors such as funding, knowledge, data, intellectual property, governance structures, knowledge transfer, cooperation and value chains. This information and the analytical principles of ATTRACT were used by CASEIA as the basis for mapping the case studies across six key socio-economic impact dimensions.
Attract’s theoretical principle: serendipity
ATTRACT can be viewed as an experiment in systematizing technological serendipity for big science infrastructures [6]. Serendipity – the theory of happy coincidence – is what sets ATTRACT apart from other innovation projects. The British academic Ohid Yaqub identifies four types of serendipity: Mertonian, Walponian, Bushian and Stephanian [7].
Research institutions usually undertake research with a defined problem in mind. To the extent that serendipity occurred in them at all, the two cases investigated by CASEIA fell into the Mertonian category, where innovation is only likely to be incremental in nature.
The more radical outcome expected of research institutions falls into the Walponian category. This is the conventional technology transfer model for universities, where both incremental and radical innovations can occur.
Due to the high degree of risk involved, non-targeted lines of enquiry are mostly pursued in the private sector or in other cultures that are comfortable with risk-taking. There is no space for either Bushian or Stephanian serendipity to flourish in the current cultural climate of research institutions. Stephanian serendipity occurs when a technology is so disruptive that its use transforms entire industries. The business project in the CASEIA study can be seen as an example of deliberate Stephanian serendipity. The participating enterprises took a conscious decision to market a technology (i.e. a solution) rather than an application, leaving it up to the user to find the right problem for it to solve. This resulted in the project’s most important commercial impact. A disruptive technology – 3D printing of glass – was made commercially available for a wide range of users and applications. It is still too early to predict the radical impacts of this innovation [8].
Research institutions versus businesses
The business-driven case study was the only one that the CASEIA project team deemed to have achieved significant positive impacts in all six of the key dimensions analyzed. The main positive impacts of the case studies involving the research institution and the control case that was not part of the ATTRACT project were in the knowledge production, knowledge spillover and skills development dimensions.
The two ATTRACT case studies illustrate the different cultures, processes and missions of research institutions and businesses. The fact that the business-driven case was embedded in the cultural and incentive structures of industry meant that it ultimately had impacts in the industrial sector. The research institution-driven case was much more closely tied to the world of academia and universities. In this instance, support from the ATTRACT project was not enough to bridge the innovation gap. The impacts of this case are more comparable to the impacts of research institutions in general.
One provisional finding from this comparative study is that business-led consortia that are embedded in the cultural and incentive structures of industry are likelier to deliver the kind of innovation outcomes that are favored by innovation programs and seed capital and have impacts in the market. The impacts of the core missions research infrastructures are distinct from the impacts that might emerge from their commercialisation activities – the two impact pathways follow very different courses and logics.
Knowledge spillover and socio-economic impact
In the research institution-led projects, the most significant impact of the support provided through ATTRACT was knowledge spillover. However, the business-driven project clearly demonstrated that the biggest overall impact resulted from commercialization. This suggests that research institutions should also focus on potential commercialization. However, the nature of this commercialization must be considered in the light of the EU’s wider policy goals.
The project team summarized the key conclusions in the following three statements:
- The culture of research institutions favors the types of serendipity that are likely to result in incremental innovations. Few research institutions have a well-developed strategy for creating impact pathways. ATTRACT achieves impact through the market.
- Businesses are by definition active in the market and thus likelier to have an impact there. If a research institution’s chosen route to impact is through the market, the initiative should be led by businesses.
- The most important impact of innovation programs in research institutions is knowledge spillover.
ATTRACT understands “open innovation” to mean technologies that do not have a particular area of application in mind [9]. This approach can also act as a driver of innovation if coupled with the tools for enabling Stephanian serendipity proposed in the
CASEIA project’s final report. This type of “openness” can be a good indicator of impact through radical innovation.
The project team also found examples of negative innovation outcomes with a positive socio-economic impact. This should be taken into account by future programs. If a program like ATTRACT is only evaluated on the basis of its innovation outcomes, valuable positive socio-economic impacts could be overlooked.
Contact
Dr. Sonia Utermann (author)
Associate
Steinbeis Transfer-Hub Berlin (Berlin)
Arne Jungstand (author)
Freelance project manager
Steinbeis Research Center Technology Management Northeast (Rostock)
www.steinbeis-nordost.de
Prof. Dr. Michael Gastrow (author)
Research Director HSRC’s Impact Centre
Human Sciences Research Council (Kapstadt/Südafrika)