Can a university partnership help solve your business challenge?

Dr Markus Perkmann, Associate Professor of Technology and Innovation at Imperial College Business School, sets out four models for businesses to successfully collaborate with universities

Many businesses have realised that instead of relying on their in-house laboratories, they need to go outside in order to drive their innovation process. Among external partners, universities offer high promise, since they allow access to an enormous global pool of talent, knowledge, and skills. Within the OECD alone, universities spend about $160b annually on research which enables them to be at the forefront of many areas in science and technology development.

Therefore, many companies seek to collaborate with universities in order to access new areas of expertise, develop new skills and support product development.

These collaborations also sometimes allow companies to achieve more than just instrumental outcomes and help them work towards broader purposes: they can help provide answers to wider societal problems, including energy conservation, carbon reduction and cost-effective disease treatments.

However, collaborations between businesses and universities aren’t always as smooth as they could be. Most of the challenges derive from the fact that universities and business have different cultures: universities strive to create new knowledge and publish it while businesses are focused on the application of knowledge to products and services.

There are a number of ways in which this difference in culture can not only be overcome, but even actively leveraged in order to generate productive outcomes for both businesses and universities. In research I conducted with Ammon Salter in 2012, How to Create Productive Partnerships With Universities, we found that there are four models of collaboration that can generate productive outcomes for both parties involved.

In a new infographic, we illustrate these four models: Idea Lab, Grand Challenge, Extended Workbench and Deep Exploration, including an explanation of which models are best for achieving desired outcomes.