Lessons from IGL2019: improving research productivity, collaboration, commercialisation and impact

By Henry Sauermann on Monday, 10 June 2019.

During the Policy and Practice Learning Lab at IGL2019 in Berlin, we hosted a range of workshops and interactive sessions to give policymakers the opportunity to put their learning into action. Henry Sauermann, Associate Professor of Strategy at EMST Berlin, joined a panel to discuss how we understand scientists’ and researchers’ motives to improve research productivity, collboaration, commercialisation and impact.

Here, he gives the four key lessons learnt from the panel:

1. The importance of motivational factors

Efforts to increase productivity, collaboration, and commercialisation can be achieved through a range of policies. Such policies should pay particular attention to motivational factors that shape individuals’ decisions to enter scientific careers, direct their attention to particular problems, or translate scientific insights into new products and services in entrepreneurial ventures. At the individual level, such factors include psychological motives such as the desire for intellectual challenge or money. At the level of organisations and institutions, such factors include incentives and constraints that encourage or discourage scientists’ activities.

2. Individual level

Stereotypical portrayals of individual scientists are often overly simplistic in two respects. First, they focus on a limited set of motives, emphasising self-interested motives such as peer recognition (kudos), challenge, and money. However, recent work shows that scientists are not only interested in their own benefits – some care deeply about having an impact on broader society with their work.

Second, there is important heterogeneity in the weights individual scientists attach to different motives. While some care most strongly about gaining the recognition of their academic peers, others forego some of that recognition in order to work on “applied” problems that they feel can make a greater impact on the world.

This diversity in motives may also relate to other kinds of diversity – such as differences in age, race or gender. Understanding how scientists differ with respect to their needs and preferences is important so that we can design mechanisms that encourage and enable a diverse range of people to join the scientific enterprise. And diversity is needed: countless studies demonstrate that diversity in knowledge and perspectives increases creativity and scientific productivity. Diversity among scientists and engineers is also needed to produce solutions that address the needs of our broad and diverse societies: women doctors may have an edge in understanding diseases that men are lucky enough not to get.

3. Institutional level

When designing policies and other institutional mechanisms to encourage productivity and commercialisation, it is important to avoid simplistic solutions. First, organisations are complex, and individuals working within them are subject to multiple layers of incentives, constraints, and conflicting demands. As such, policies that target particular aspects in isolation (for example, rewards for patenting) will show limited effectiveness if they clash with other incentives or if scientists face cultural or resource barriers that are difficult to overcome.

Second, we need to pay greater attention to differences across fields such as the basic physical sciences, medicine, or engineering. Fields differ with respect the nature of research, the importance of collaborations, or the distance from research to commercialisation. Policies to support productivity or encourage commercialisation need to take such differences into account: once size does not fit all.

4. Individuals and institutions

Finally, institutional levers need to be designed in light of individual heterogeneity. Financial incentives for patenting may “work” for individuals with strong pecuniary motives, but fail to elicit a response from scientists who care strongly about peer recognition. However, the latter may engage in commercialisation if the academic culture changes so that commercialisation and social impact are valued by their peers. Similarly, policies that seek to alleviate the constraints that scientists face in their work, need to recognise that constraints faced by different groups – such as men and women – will often differ. Policies that help one group may fail to activate the potential of another.

Much progress has been made towards understanding the drivers of scientific productivity, collaboration, and commercialisation. Recognising and addressing diversity and heterogeneity across scientists and fields will enable organisational leaders and policy makers to further improve the effectiveness of research for the benefit of science and the broader society.