Decision-making for Emerging Technologies

Decision-making for Emerging Technologies group (DM-ET)

 

Emerging technologies (ET) are often prospected as futuristic opportunities for people, companies and society. In the press, they are frequently anticipated as ways of solving difficult problems and of making people’s life easier. As such, companies consider emerging technologies possible ways to innovate and to create value for themselves and their customers.  However, when it comes to investing in acquiring and/or developing an emerging technology, managers face many problems. For one, even if they could revolutionise societies and industries, emerging technologies are still maturing and are far behind on many performance levels to guarantee their immediate real-life deployment. Managers need to take risks and plan on how to invest in these technologies, balancing risks and rewards prospects whilst dealing with high levels of uncertainties. This is a high-stake decision-making (DM) exercise which often involves different levels in an organisation over long periods of time.

 

Our group studies these decision-making processes in organisations to develop theoretical and practical models about how these processes happen and when they yield the best outcomes. At a high level, our work could be grouped in two main areas:

 

1. Deciding about emergent technologies

 

At DM-ET we study the act of taking decisions about emerging technologies at the individual or group level. The DM-ET process can be supported by decision support tools such as scenario planning, roadmapping, or portfolio management matrices and companies could adopt Technology Intelligence processes and systems to keep up with the latest information about emerging technological trends. Nevertheless, the individuals involved are implicitly affected by cognitive issues due to dealing with complex and uncertain outcomes. The adoption of ET may create new problems for those taking decisions as the changes required can be drastic. Technologies such as Augmented Reality (AR) and Artificial Intelligence (AI) may help reduce the problems in taking-decisions under uncertain conditions. In this space, current projects include:

  • How do intelligence managers interact with AI-tools? (Constanze Leeb)- This project is part of the EINST4INE consortium and receives funding from the EU Marie Sklodowska-Curie Innovative Training Networks (ITN) scheme.
  • How does AR affect cognitive processes in Strategic Technology planning? (Nicola Felicini, Bethan Moncur) This project is part of the EINST4INE consortium and receives funding from the EU Marie Sklodowska-Curie Innovative Training Networks (ITN) scheme, the Agriforwards CDT and industry.
  • How do group decisions on emergent technology unfold? (Pete Jha)
  • Modeling the value of IoT for mass customization – This project received support from Pitch-In 


2. Decisions in emerging technologies

 

This area covers the understanding of the impact of the emergence of certain technologies on firms and industries. A group of projects at DM-ET looks at past decisions company took when trying to implement an emerging technology to understand which types of conditions, approaches and configurations companies adopted to obtain the best results.  For example, the team has done much work on how companies adopt open innovation to acquire emerging technologies. Current projects include:

  • How do gene therapy firms balance innovation modes during technology emergence? (Johannes Wolff)
  • How does the availability of digital manufacturing technology in fabrication spaces affect the Entrepreneurial innovation process? (Dr Valeria Dammicco). This project has been supported by CDT EPSRC and RADMA
  • How do companies operate in the deployment of AI? ( Pradeep Debata)
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