Cloud computing is an emerging paradigm for provisioning computing and IT services. Migration from traditional systems setting up to cloud computing is a strategic organisational decision that can affect organisations’ performance, productivity, and growth as well as competitiveness. Organisations wishing to migrate their legacy systems to the cloud often need to go through a difficult and complicated decision-making process. This can be due to multiple factors including restructuring IT resources, the still evolving nature of the cloud environment, and the continuous expansion of the cloud services, configurations and providers. This research explores the factors that would influence decision making for migration to the cloud, its impact on IT management, and the main tasks that organisations should consider to ensure successful migration projects. The sequential exploratory strategy is followed for the exploration. This strategy is implemented through the utilisation of a two-stage survey for collecting the primary data. The analysis of the two-stage survey as well as the literature identified eleven determinants that increase the complexity in the decisions to migrate to the cloud. In the literature some of those determinants were realised, accordingly, there have been many proposed methods for supporting migration to the cloud. However, no systematic decision making process exists that clearly identifies the main steps and explicitly describes the tasks to be performed within each step. This research aims to fill this need by proposing a model to support the decision process for migrating to cloud. The model provides a structure which covers the whole process of migration decisions. It guides decision makers through a step-by-step approach aiding organisations with their decision making. The model was evaluated by exploring the views of a group of the cloud practitioners on it. The analysis of the views demonstrated a high level of acceptance by the practitioners with regard to the structure, tasks, and issues addressed by the model. The model offers an encouraging preliminary structure for developing a cloud Knowledge-Based Decision Support System