The high percentage of IT/IS projects failure has led to a wide investigation efforts. Several studies have revealed that new or indeed major changes in IT/IS projects are affecting people, processes, organisational structures and current IT infrastructure. The hardest thing when dealing with such issues lies in changing people and business processes which are better known as 'soft issue'. The resistance to change might occurred due to lack of creating a sense of need and urgency for change among the employee and consequently might lead to the IT/IS project failure. The inability to assess organisational readiness to successfully embrace new systems into their work environments may result in wasting time and resources by dealing with the resistance to change or even worse may lead to IT/IS failure. The majority of current IT/IS evaluation approaches are mainly a product oriented and post-investment measures which don't address organisational readiness prior to IT/IS investment. This PhD study has developed a holistic model to measure the capability organisations to 'successfully' implement IT by measuring their readiness status to implement new IT/IS systems, prior to IT/IS investment. The model is capable of assessing; a) the organisational current readiness status in terms of four main elements; people, process, work environment and IT infrastructure, and b) the target readiness status which is required for a successful implementation of a particular IS/IT project. The idea is to provide managers with measurement tools to enable them to identify where improvements are required within their organisations prior to IT/IS investment. It is anticipate that this model will significantly contribute to the IT/IS field by adding a new dimension to IT/IS evaluation which will shift the focus towards the "soft issues" (organisational factors) rather than the systems themselves. Three organisations were participated in the validation process of this model and subsequently a set of questionnaire has been sent to the 250 organisations within the construction sector to measure the statistical influence of different components of the model.