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This chapter explains the concept of risk management in construction in relation to project success. The types of risks were examined based on the date of identification which are known risk, unknown risk, new or discovered risk, secondary risk and residual risk. Project risk is not an all-encompassing negative event as it could also cause a positive impact on construction projects. It was acknowledged that project risk in itself could have a positive impact if its risk management process is properly implemented by the construction project team.

The aim of this article is to develop an understanding of the issues related to risk management in digital library projects as well as techniques for mitigating risk in these projects.

Using evidence from other research in the area, this article outlines the major risk issues within a project and then defines a model for mitigating risk within a project.

The article finds that understanding the risk management entails understanding the underlying factors that contribute to project risks. These risks are often the same, regardless of the nature of the project. The first step in risk assessment is risk identification. Once risk identification is complete, risk analysis is used to identify the likelihood the risks that have been identified will happen. While there are several formal methods that can be used for risk analysis, many project managers use some type of matrix‐based decision process for analyzing and evaluating project risk. The most successful project managers maintain open lines of communication throughout their organizations to stay in touch with constituent's needs.

This article fills a gap in the digital project management literature by helping project managers understand the issues related to project risk and how to avoid them, thereby insuring greater probability their project will come to a successful and satisfying conclusion.

The purpose of this paper is to highlight the main findings of a successfully defended doctoral thesis that studied factors or interventions causing the discrepancy between how adequate project risks should be managed and how project risks are actually managed.

The approach involved interviews and a survey using questionnaires gathered data from project managers about their experiences with project risk management during two phases of fieldwork. The first phase included in‐depth interviews with information technology (IT) project managers in order to explore patterns involving risk mediators and their influence on project risk management. A web‐based survey was used in the second phase for the purpose of testing these patterns on a wider range of project managers.

Specific risk‐related interventions strongly influence the effective use of project risk management: project managers tended to deny, avoid, ignore risks and to delay the management of risk. Risks were perceived as discomforting, not agreed upon. IT project managers were unaware of risks and considered them to be outside their scope of influence and preferred to let risks resolve themselves rather than proactively engaging with them. As a consequence, factors such as the lack of awareness of risks by IT project managers appeared to constrain the application of project risk management with the result that risk had an adverse influence on the outcome of IT projects.

The underlying rational assumptions of project risk management and the usefulness of best practice project risk management standards as a whole need to be questioned because of the occurrence of interventions such as the lack of information. IT project managers should first prevent risk‐related interventions from influencing the use of project risk management. However, if this is not possible, they should be prepared to adapt to risks influencing the project outcome.

The paper contradicts the myth of a “self‐evidently” correct project risk management approach. It defines interventions that constrain project manager's ability to manage project risk.

The purpose of this paper is to discuss and highlight the results of a study that identified critical risks involved in the procurement of community‐based infrastructure projects in Zambia.

Brainstorming was chosen as the method for identifying risks in the study. It was applied in the form of group discussions with project management committees at community level to identify potential risks affecting their particular projects.

The identified critical risks were classified into six categories: project initiation; community contribution and participation; budget and finance; skilled labour; materials procurement and technical supervision; and quality control. Several significant risk factors were identified in all these six categories in community‐based construction projects. The paper concludes that indeed there are critical risks in community‐based construction projects that require forward planning, assessment and mitigation.

The consequences of not assessing and managing construction risks in both conventional and community‐based procurement systems are that projects may experience time and cost overruns and lead to poor quality structures. It is therefore important to identify risks in community projects.

Managerial finance has become a modern professional discipline with a coherent theory and a growing body of statistical research in support of the theory. Finance faculty in leading business schools around the world are now actively engaged in making the modern theory accessible to executive participants in post‐experience educational programmes. What makes the modern theory of finance exciting is the simplicity and the authority with which issues of concern to management today can be resolved. One of the areas of interest where answers to old questions are being found is in the estimation of discount rates or required rates of return for capital projects.

To examine the causes of knowledge management (KM) failure.

A multi‐case analysis approach was used to review five documented cases of KM failure in the literature. Categories of risk were identified through an iterative analysis of each case.

There are four main categories of risk associated with KM failure, namely technology risk, culture risk, content risk and project management risk. The nature of these risks differs dependent upon the stage of a KM project.

A limited number of cases were reviewed.

Practitioners need to proactively manage risk to avoid failure in KM projects.

Proposes a taxonomy of KM risk.

Urban rail projects are typically large-scale transport infrastructure projects (megaprojects) which have many potential risks that can influence the strategic goals of owners. However, there is a paucity of studies which explore the impact of risks on both “urban rail” project time and cost together considering quantitative assessments. Therefore, this paper focuses on investigating critical risks and quantifying such risk impacts on urban railway project schedule and cost in practice.

A combination of qualitative and quantitative research methods comprising semi-interviews with five experts and a questionnaire survey of 132 professional respondents is used. The data were modeled using Monte Carlo Simulation to predict the probability of project schedule and cost.

The results show that 30 risk variables are categorized into seven main groups which have significant impacts on both project time and cost. Outstanding five risk variables were highlighted as follows: (1) project site clearance and land compensation; (2) design changes; (3) physical project resources; (4) contractors’ competencies and (5) project finance. Such findings were supported by Monte Carlo simulation which predicted in the worst case that the project may suffer 11.03 months’ delays and have cost overrun with a contingency of US$287.68 million.

This study expands our knowledge about time and cost contingency of urban metro railway implementation across developing economies and particularly within the context of Vietnam. Policymakers will not only gain an understanding about risk structure but will also recognize the significant impacts of critical risk through risk impact modeling and simulation. Such an approach provides insights into risk treatment priorities for planners so that they can proactively establish suitable strategies for risk mitigation in practice.

Research and development (R&D) projects are often pursued through a project portfolio (PP). R&D PPs involve many stakeholders, and without proactive management, their interactions may lead to conflict risks. These conflict risks change dynamically with different stages of the PP life cycle, increasing the challenge of PP risk management. Existing conflict risk research mainly focuses on source identification but lacks risk assessment work. To better manage the stakeholder conflict risks (SCRs) of R&D PPs, this study employs the dynamic Bayesian network (DBN) to construct its dynamic assessment model.

This study constructs a DBN model to assess the SCRs in R&D PP. First, an indicator system of SCRs is constructed from the life cycle perspective. Then, the risk relationships within each R&D PPs life cycle stage are identified via interpretative structural modeling (ISM). The prior and conditional probabilities of risks are obtained by expert judgment and Monte Carlo simulation (MCS). Finally, crucial SCRs at each stage are identified utilizing propagation analysis, and the corresponding risk responses are proposed.

The results of the study identify the crucial risks at each stage. Also, for the crucial risks, this study suggests appropriate risk response strategies to help managers better perform risk response activities.

This study dynamically assesses the stakeholder conflict risks in R&D PPs from a life-cycle perspective, extending the stakeholder risk management research. Meanwhile, the crucial risks are identified at each stage accordingly, providing managerial insights for R&D PPs.

In this paper, we study the role of government financial guarantees as catalyst for project finance (PF). On the one hand, the government's incentive compatibility and participation constraint determine the optimal portion of the loan to be backed. On the other, the borrowing interest rate satisfies the debtholders’ participation constraint. The project's sponsor may choose to underinvest or overinvest depending on its own capital contribution, the risk technology, the risk measurement errors, and the proportion of guarantee provided by the government. We derive the project optimal investment level as well as the government partial loan guarantee coverage. We also discuss the impact of the risk measurement errors on the project's credit spreads.