The goal of IT governance is not only to achieve internal efficiency in an IT organization, but also to support IT’s role as a business enabler. The latter is here denoted IT governance performance. IT management cannot control the IT governance performance directly. Instead, their realm of control includes several IT governance maturity indicators such as the existence of different IT activities, documents, metrics and roles. Current IT governance frameworks are suitable for describing IT governance, IT-systems, and business processes, but lack the ability to predict how changes to the IT governance maturity indicators affect IT governance performance. Bayesian networks are widely used for goal modeling and prediction in several research fields. This paper presents an application of Bayesian networks for IT governance performance prediction. Data from 35 case studies conducted in a variety of organizations has been used to determine the behavior of the network. An assumption on linearity is introduced in order to compensate for the limited amount of data, and the network learns using the proposed Linear Conditional Probability Matrix Generator. The resulting Bayesian network for IT governance performance prediction can be used to support IT governance decision-making.
Stored only when both papers are in the corpus — 32 citations exist upstream.
Imported from OpenAlex (CC0) — nothing here is generated.