The literature on risk management acknowledges a growing number of Risk Maturity Models (RMM). However, for the construction sector, there is no validated RMM that is based on both theory and experts' opinions. In this article, a Generic RMM (GRMM), inspired by the EFQM model, is developed and validated to remedy this shortcoming. The GRMM uses a list of statements extracted from risk management literature by means of qualitative content analysis. The statements and the model are both validated by means of two focus group sessions, based on which the statements and the model are improved. According to the experts, the GRMM is easy to use and provides projects with a clear picture of potential improvements regarding risk management. Project managers can use the GRMM for planning and improving risk management, as well as for cross-project analysis for learning purposes. Further research on application of the GRMM in real projects is recommended.

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The current literature discusses the methods to estimate the costs and cost contingency. The literature also distinguishes "known unknowns" and "unknown unknowns" contingencies. Little is written, however, about the evolvement of total project cost estimates during the preconstruction phase of construction projects. Moreover, not many studies are investigating the "known unknowns" and "unknown unknowns" contingencies in real construction projects. Practice expressed the need for getting more insight into the development of the estimated costs of the projects in the preconstruction phase. This paper, therefore, discusses the estimate of the total project costs (and cost contingency) in the preconstruction phases of 29 Dutch flood defense projects using a case study approach. Altogether, the projects have experienced an 11.51% increase in the estimated costs compared to the initial estimates, which is low compared to previous studies. This increase in the cost estimates of the flood defense projects can be explained by "technical" reasons. The investigation of "known unknowns" and "unknown unknowns" contingencies shows that the percentage of the "unknown unknowns" contingency has increased in the preconstruction phase while a reduction was expected. This increase suggests that the projects were not confident about their estimates and the increase can be explained by a lack of experience, organizations' culture, or the phenomenon of "pessimistic bias." Practitioners can avoid "pessimistic bias" behavior by asking for opinions about their estimates and using historical project data. Further research is suggested into realized cost contingency after project execution.

Current literature shows that poor cost performance in projects has become routine. Research on cost performance has mostly focused on one of the involved parties, either the client or the contractor. Not many research efforts discuss the cost contingency performance of projects. This research discusses the cost performance and cost contingency of projects in the execution phase from the perspective of a client as well as a contractor. Using a case study approach, 95 projects are investigated: 44 client projects and 51 contractor projects. The results show that depending on the perspective, projects can have cost overruns or cost underruns. Comparing the total realized and estimated costs, projects experienced on average about 16% underrun from a client perspective. From a contractor perspective, projects experienced on average up to 2% overrun. The estimated cost contingency in the client's projects was on average 2.64% more than the required cost contingency. The estimated cost contingency in the contractor's projects was on average 5.41% less than the required cost contingency. These differences are explained by pessimism bias and technical reasons on the client's side. On the contractor side, optimism bias and technical and political reasons play a role, resulting in opportunistic behavior. The findings help practitioners to enhance their cost estimates by avoiding both pessimistic bias and optimism bias behavior-for example, by using historical data from earlier projects. Further investigation into the influence of market conditions on cost estimates is suggested.

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