Identifying Output Interactions Among Is Projects - A Text Mining Approach

The information systems (IS) literature provides anecdotal as well as empirical evidence for the presence of output interactions amongst IS projects, and their business impact. A number of sophisticated optimization models have been suggested for the consideration of output interactions when selecting IS project portfolios, but usually, the necessary data required for their application in business practice is not available at the planning stage. The literature currently does not offer techniques on how to identify output interactions at the planning stage a gap which we attribute to the semantical nature of output interactions. We contribute to filling this gap by applying semantic clustering – a technique originating in the text mining literature – to the field of information systems project portfolio selection. A prototypical decision support system is developed that uses latent semantic analysis and hierarchical clustering to identify potential output interactions among information systems project proposals based on semantic similarities within their goal descriptions. This research-in-progress paper focuses on the design of the prototype developed and argues that latent semantic analysis presents a very promising technique for the identification of output interactions among information systems projects.