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Civil utility projects, both open-trench and trenchless, are subject to risk. These risks have both direct and indirect effect on project cost, schedule, quality and safety. It is therefore critical for the project management team to include risk management as an integral part of their project planning and execution. The purpose of this study is to identify the pipe-bursting construction risks and determine their probability of occurrence and cost impact and provide the appropriate responses to mitigate the identified risks.

This is an exploratory design using an industry-wide questionnaire survey to collect data on the probability of occurrence and impact of risks on cost of pipe-bursting projects. A probability-impact model was used to categorize the risks to determine their criticality and the appropriate risk responses.

The model revealed that majority of the analyzed risks have low impact-low probability of occurrence and high impact-low probability of occurrence. Undocumented repairs to host pipe was the only risk identified as having high probability of occurrence and high impact on cost. The risk responses suggest a combination of risk transfer, reduction and acceptance to be appropriately applied to mitigate the risks. A discussion on the good practices indicates that most pipe-bursting operations can be done safely and successfully if site and project conditions are known before bursting and the appropriate measures are taken to address those conditions.

Although the identified risks may apply to other utility construction methods, the focus of this research is limited to risks that occur during the construction phase of a pipe-bursting construction project.

Risk management is very critical to the success of any construction project. Identification and assessment of risks alone will not serve the purpose of risk management unless meaningful ways to mitigate those risks in a structured way are planned. The probability-impact model for the pipe-bursting construction risks with the mitigation strategies will help owners, engineers and contractors plan for and adequately respond to these risks. Additionally, a logical assessment of the risks will aid in effective decision-making regarding the management of the project.

Extensive literature review indicates that there is no existing literature on the probability of occurrence and impact on cost of risks in pipe-bursting projects. This paper presents the results of a wide-ranging analysis on construction risks in pipe-bursting projects. This is the first analysis incorporating the use of the probability-impact model to determine the criticality of various pipe-bursting construction risks.

This study proposes the use of cumulative prospect theory (CPT) to predict over- and under-estimation of risks and the counteractive adjustment in a cold chain context. In particular, the purpose of this paper is to address the importance of the socio-demographic characteristics of an individual in influencing risk attitude and the analysis of measurable risk probability.

This study uses CPT as the basis to develop a decision analysis model in which the two functions of value editing and probability weighting are nonlinear to adequately determine the flexible risk attitudes of individuals, as well as their prospects with numerous outcomes and different probabilities. An experiment was conducted to obtain empirical predictions, and an efficient Markov Chain Monte Carlo algorithm was applied to overcome the nonlinearity and dimensionality in the process of parameter estimation.

The respondents overweigh the minor cold chain risks with small probabilities and behave in a risk-averse manner, while underweighting major events with larger ones, thereby leading to risk-seeking behavior. Judgment distortion regarding probability was observed under risk decision with a low probability and a high impact. Moreover, the findings indicate that factors, such as gender, job familiarity and confidentiality significantly influence the risk attitudes and subjective probability weighting of the respondents.

The findings fit the framework of CPT and extend this theory to deal with human risk attitudes and subjective bias in cold chains. In particular, this study enhances the literature by providing an analysis of cold chain risk from both the human decision-making and managerial perspectives. Moreover, this research determined the importance of the socio-demographic characteristics of an individual to explain the variability in risk attitudes and responses.

Managers must consider the issues of flexible risk attitude and subjective judgment when making choices for risk mitigation strategies. Given the focus on counteractive adjustment for over- and under-estimated risk, firms could evaluate cold chain risk more accurately, and thereby enhance their resilience to risky events while reducing the variability of their performance.

The current study is the first to materialize the phenomena of over- and under-estimation of cold chain risks, as well as to emphasize the different characteristics for loss aversion and judgment distortion at the individual level.

This paper aims to develop a process for prioritizing project risks that integrates the decision-maker's risk attitude, uncertainty about risks both in terms of the associated probability and impact ratings, and correlations across risk assessments.

This paper adopts a Monte Carlo Simulation-based approach to capture the uncertainty associated with project risks. Risks are prioritized based on their relative expected utility values. The proposed process is operationalized through a real application in the construction industry.

The proposed process helped in identifying low-probability, high-impact risks that were overlooked in the conventional risk matrix-based prioritization scheme. While considering the expected risk exposure of individual risks, none of the risks were located in the high-risk exposure zone; however, the proposed Monte Carlo Simulation-based approach revealed risks with a high probability of occurrence in the high-risk exposure zone. Using the expected utility-based approach alone in prioritizing risks may lead to ignoring few critical risks, which can only be captured through a rigorous simulation-based approach.

Monte Carlo Simulation has been used to aggregate the risk matrix-based data and disaggregate and map the resulting risk profiles with underlying distributions. The proposed process supported risk prioritization based on the decision-maker's risk attitude and identified low-probability, high-impact risks and high-probability, high-impact risks.

The purpose of this article is to investigate the overconfidence effect on investor attitude towards new ventures investment valuation as a function of the investor involvement in estimating the venture's success probability.

The method used was experimental; econometric analysis within a prospect theory framework.

Participants in the role of investors are more risk seeking when they are called upon to judge success probability relative to them being offered a (reliable) success probability even when their own judgement is based on a restricted data set. Further, that investor overconfidence is a consequence of changes in risk attitude, not probability weighting.

Opens up a number of issues relating to investor‐entrepreneur opportunity perception disparity and its impact on the “investment‐gap”. Considers factors that might be explored experimentally, including task confidence, investor‐entrepreneur information asymmetry and source credibility. Detailed comment made on future research directions.

Impacts on the management of communication between entrepreneurs and investors. Six management variables that might influence disparities in investor‐entrepreneur risk assessment considered in relation to findings.

We take Cumulative Prospect Theory (CPT) seriously by rigorously estimating structural models using the full set of CPT parameters. Much of the literature only estimates a subset of CPT parameters, or more simply assumes CPT parameter values from prior studies. Our data are from laboratory experiments with undergraduate students and MBA students facing substantial real incentives and losses. We also estimate structural models from Expected Utility Theory (EUT), Dual Theory (DT), Rank-Dependent Utility (RDU), and Disappointment Aversion (DA) for comparison. Our major finding is that a majority of individuals in our sample locally asset integrate. That is, they see a loss frame for what it is, a frame, and behave as if they evaluate the net payment rather than the gross loss when one is presented to them. This finding is devastating to the direct application of CPT to these data for those subjects. Support for CPT is greater when losses are covered out of an earned endowment rather than house money, but RDU is still the best single characterization of individual and pooled choices. Defenders of the CPT model claim, correctly, that the CPT model exists “because the data says it should.” In other words, the CPT model was borne from a wide range of stylized facts culled from parts of the cognitive psychology literature. If one is to take the CPT model seriously and rigorously then it needs to do a much better job of explaining the data than we see here.

In fraudulent conveyance cases, plaintiffs allege that by entering into a complex leverage transaction, such as an LBO, a firm’s former owners ensured its subsequent collapse. Proving that the transaction rendered the firm insolvent may allow debtors (or their proxies) to claw back transfers made to former shareholders and others as part of the transaction.

Courts have recently questioned the robustness of the solvency evidence traditionally provided in such cases, claiming that traditional expert analyses (e.g., a discounted flow analysis) may suffer from hindsight (and other forms of) bias, and thus not reflect an accurate view of the firm’s insolvency prospects at the time of the challenged transfers. To address the issue, courts have recently suggested that experts should consider market evidence, such as the firm’s stock, bond, or credit default swap prices at the time of the challenged transaction. We review market-evidence-based approaches for determination of solvency in fraudulent conveyance cases.

We compare different methods of solvency determination that rely on market data. We discuss the pros and cons of these methods and illustrate the use of credit default swap spreads with a numerical example. Finally, we highlight the limitations of these methods.

If securities trade in efficient markets in which security prices quickly impound all available information, then such security prices provide an objective assessment of investors’ views of the firm’s future insolvency prospects at the time of challenged transfer, given contemporaneously available information. As we explain, using market data to analyze fraudulent conveyance claims or assess a firm’s solvency prospects is not as straightforward as some courts argue. To do so, an expert must first pick a particular credit risk model from a host of choices which links the market evidence (or security price) to the likelihood of future default. Then, to implement his chosen model, the expert must estimate various parameter input values at the time of the alleged fraudulent transfer. In this connection, it is important to note that each credit risk model rests on particular assumptions, and there are typically several ways in which a model’s key parameters may be empirically estimated. Such choices critically affect any conclusion about a firm’s future default prospects as of the date of an alleged fraudulent conveyance.

Simply using market evidence does not necessarily eliminate the question of bias in any analysis. The reliability of a plaintiff’s claims regarding fraudulent conveyance will depend on the reasonableness of the analysis used to tie the observed market evidence at the time of the alleged fraudulent transfer to default prospects of the firm.

There is a large body of literature in financial economics that examines the relationship between market data and the prospects of a firm’s future default. However, there is surprisingly little research tying that literature to the analysis of fraudulent conveyance claims. Our paper, in part, attempts to do so. We show that while market-based methods use the information contained in market prices, this information must be supplemented with assumptions and the conclusions of these methods critically depend on the assumption made.

In methods and manuals, the product of an information security incident’s probability and severity is seen as a risk to manage. The purpose of the test described in this paper is to investigate if information security risk is perceived in this way, if decision-making style influences the perceived relationship between the three variables and if the level of information security expertise influences the relationship between the three variables.

Ten respondents assessed 105 potential information security incidents. Ratings of the associated risks were obtained independently from ratings of the probability and severity of the incidents. Decision-making style was measured using a scale inspired from the Cognitive Style Index; information security expertise was self-reported. Regression analysis was used to test the relationship between variables.

The ten respondents did not assess risk as the product of probability and severity, regardless of experience, expertise and decision-making style. The mean variance explained in risk ratings using an additive term is 54.0 or 38.4 per cent, depending on how risk is measured. When a multiplicative term was added, the mean variance only increased by 1.5 or 2.4 per cent. For most of the respondents, the contribution of the multiplicative term is statistically insignificant.

The inability or unwillingness to see risk as a product of probability and severity suggests that procedural support (e.g. risk matrices) has a role to play in the risk assessment processes.

This study is the first to test if information security risk is assessed as an interaction between probability and severity using suitable scales and a within-subject design.

The purpose of this paper is to propose a model that enables a company to select the supply chain strategy based on risk probability p (measure of how likely/often a detrimental event occurs) and risk impact i (expression of the significance of a loss when that event occurs).

This paper discusses four supply chain strategies: agility, robustness, resilience and rigidity. Mathematical models are used for the strategies' cost functions, which reveal optimal solutions and break‐even points in dependence of p and i.

This paper proposes that resilience is appropriate in the case of high supply chain risk probability and impact, and rigidity if both values are low. When only risk impact is low, robustness is optimal, whereas agility is optimal when only risk probability is low.

This research extends existing models for selecting the appropriate supply chain strategy.

Knowledge of the interplay between the strategies' cost functions and risk probability and risk impact is vital for companies. This may encourage managers to become more familiar with their strategy costs and supply chain risks.

To the author's knowledge, no corresponding model exists so far that links risk impact and risk probability to the four supply chain strategies.

The purpose of this paper is to propose a method integrating fault tree analysis and optimization model to allocate response budget from the preventive and protective perspectives.

The proposed method consists of two main steps. The first step is to analyze and calculate the probability and the loss of the risk. The second step is to build an optimization model for allocating response budget.

First, there exists an optimal response budget. Second, risk protection is preferred to risk prevention when the total budget is limited. Third, the protective budget should be first invested for the consequence event with greatest expected loss. Fourth, the preventive budget should be first allocated to the risk cause with highest occurrence probability that belongs to the OR set in the fault tree.

Managerially, our results indicate that project managers (PMs) should make a tradeoff between the budget invested for risk response and reduced expected loss of the risk. Then, in the case of inadequate response budget, PMs should pay more attention to risk protection and cope with the event that can cause severe loss. In addition, under this circumstance, PMs had to better allocate the risk preventive budget in proper order.

Project risk response is a critical issue in project risk management as PMs can take actions actively to cope with project risks in this phase. Effective risk response, in general, requires financial support in practice, and reasonable allocation of the total budget among risk response strategies can produce better response effects.

We review the experimental evidence on risk aversion in controlled laboratory settings. We review the strengths and weaknesses of alternative elicitation procedures, the strengths and weaknesses of alternative estimation procedures, and finally the effect of controlling for risk attitudes on inferences in experiments.