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  Computer modelling the development of organisations in the high-tech entrepreneurship ecosystem

   Faculty of Engineering, Computing and the Environment

   Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Technological firms are regarded as key for national and regional economic development. Today, more than ever before, the business environment is burgeoning with innovation; Even simply Googling for apps and services to help boost elementary office efficiency, returns over 2000 items.

In an organisation, implementing a beneficial innovation, results in benefit “I” minus costs “C”, but implementing a ‘bad’ innovation is very costly, (i.e. minus [I+C]). Without spillover and without hierarchy in an organisation that inhabits an environment that contain many ‘bad’ innovations; fads and fashions can circulate unchecked, and these detrimental ideas can swarm into the organisation and resemble epidemics, harming the organisation. Adding the simplest form of hierarchy, one manager who makes decisions about implementing innovations by flipping a coin, means the loss is halved compared to the flat organisation. However, successful high-tech firms are paradoxically well-known for being flat organisations.

This project investigates this important enigma because SMEs and other start-ups are major sources of innovation and employment. With intensified competition and globalisation, the imminent prospect of recession, national and regional Governments are increasingly looking towards being able to create high-tech ‘knowledge ecosystems’ to promote innovation and increase financial growth.

This work will model factors leading to success or failure of tech firms and their clusters, Science and Technology Parks, using a combination of classical techniques like GIS, mining standard open source (e.g., ONS) data and commercial business databases. It will rely largely on econometric techniques including SEM, Markov Chain and kinetic Monte Carlo methods, using e.g., Python and R.

Business & Management (5) Computer Science (8)

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