Large investments are made annually to develop and maintain IT systems. Successful outcome of IT projects is therefore crucial for the economy. Yet, many IT projects fail completely or are delayed or over budget, or they end up with less functionality than planned. This article describes a Bayesian decision-support model. The model is based on expert elicited data from 51 experts. Using this model, the effect management decisions have upon projects can be estimated beforehand, thus providing decision support for the improvement of IT project performance.

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Images such as Gantt, WBS, PERT, and CPM have always played an important role in project management. In recent years, new types of images have emerged in complex development projects. The purpose of this paper is to make an inquiry into how project management activities are supported by these alternative images, and suggest reasons why the more traditional images appear to be inadequate during turbulent and complex circumstances. In conclusion, we find that the alternative images are a means to managing integration activities and critical dependencies in a project. Typically, they emphasize common understanding and comprehensibility over formalism and rigour. These alternative images seem to be resonant with how our mental cognitive apparatus conceives coordination, thus making it easier to manage complex development tasks.

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