It is still an open issue of designing and adapting (data-driven) decision support systems and data
warehouses to determine relevant content and in particular (performance) measures. In fact, some
classic approaches to information requirements determination such as Rockart’s critical success
factors method help with structuring decision makers’ information requirements and identifying
thematically appropriate measures. In many cases, however, it remains unclear which and how many
measures should eventually be used. Therefore, an optimization model is presented that integrates
informational and economic objectives. The model incorporates (statistic) interdependencies among
measures – i. e. the information they provide about one another –, decision makers’ and reporting
tools’ ability of coping with information complexity as well as negative economic effects due to
measure selection and usage. We show that in general the selection policies of all-or-none or themore-the-better are not reasonable although they are often conducted in business practice. Finally,
the model’s application is illustrated by the German business-to-business sales organization of a
global electronics and electrical engineering company as example.
Röglinger, Maximilian, "Research 2.0: Improving participation in online research communities" (2009). ECIS 2009 Proceedings. 217.