Improving the idea selection process within organizations using idea markets

Companies constantly strive to improve their capacity to generate and select new ideas, as these new ideas are critical to survival and growth. Idea markets have recently been proposed as mechanisms to improve idea selection processes.;An idea market can be described as an electronic suggestion box, internal to the organization, in which each idea is regarded as a stock. The members of the organization receive a certain amount of play money, which they can invest in the different ideas. The expectation is that the market mechanism will ensure that the best ideas will get the highest share price. These ideas can then be selected by the organizations' management and moved forward as projects.;The first trials however with such idea markets reported low prediction accuracy---with accuracy being defined as the correlation between the idea share prices and expert rankings of the ideas. What is needed therefore is a better understanding of how idea markets work and how their accuracy can be improved. This dissertation aims to improve our current understanding of idea markets through three separate but related contributions.;Based on a literature review, we developed a theory which explains and predicts idea market accuracy. Predictions based on our theory were subsequently tested in three cases studies and within one simulated idea market environment.;The second contribution is the design of an idea market software platform and its associated market-mechanism. This dissertation describes the software design in sufficient detail so that someone skilled in the art of software development will be able to replicate it. The design was instantiated in a commercial web-based software package, MyIdeaShare, which was subsequently used for the case studies, in three different organizations.;The last contribution is the design of an idea market agent-based simulation model. The model was implemented in an agent-based simulation engine. The simulation engine was calibrated with data from one of the case studies and then used to test predictions based on the earlier derived theory. The algorithms of the simulation engine are made available to the community as open source software.