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The purpose of this paper is to provide a systematic classification for frameworks, methods, and models of in-project quantitative risk analysis (IQRA) for the last 30 years.

An extensive literature review is conducted to identify pertinent IQRA works. Identified IQRA frameworks/methods/models are then classified on the basis of commonalities in key attributes and assumptions. Linkages between each category of IQRAs and dimensions of complexity are also observed.

Around 70 key publications on IQRAs are identified. Major attributes for each work are described. Five distinct categories of IQRAs emerge with unique linkages to complexity dimensions. An analytical framework in the form of a matrix is presented to illuminate evolution on modeling characteristics and to indicate a relationship between respective category and dimensions of project complexity.

The research coverage is intended to be comprehensive but it is by no means exhaustive. This study highlights research opportunities in IQRAs and the possible extension toward in-project quantitative complexity analysis (IQCA).

The proposed matrix provides guidance to practitioners to select the appropriate category of IQRAs for a specific project complexity type in a contingency fashion. The study highlights lessons from development and utilization of IQRAs. Outstanding issues from IQRAs are discussed to avoid similar drawbacks for IQCAs.

This study provides an original framework/matrix to classify extant works in IQRAs. It also establishes an association between IQRAs and the emerging conceptual works of complexity.

Aims to distinguish among different levels of default risk for residential mortgage loans and to examine the significant factors for the different levels of default risk. Classifies the sample into default and non‐default groups and analyses the original mortgage loan data by factor and cluster analyses based on borrower characteristics, property characteristics and microeconomic variables in order to derive risk classifications from various likelihoods of default. Furthermore, applies logit, probit and discriminant analyses to examine the significant factors for all three clusters. The empirical results show that the three clusters may be ranked as follows, in order of risk, from the least to greatest likelihood of default: the owner‐occupied housing buyer, invester group and young buyer clusters. In addition, the factor “borrower’s education level” has negative impact for all three clusters.

Measures of personality and organizational climate were subjected to three different analytical methods that extract patterns from data: Discriminant, Classification and Regression Trees, and neural network classification analysis. Risk, openness, rewards, and neuroticism (rather than conscientiousness) emerged as key variables in differentiating among three similar work groups. Results of the analyses support the central hypothesis of ASA theory of greater variance in personality across compared to within organizations and an interactionist paradigm between person and environment. Implications for ASA theory and for personnel selection are discussed.

Despite the fact that extensive studies on public-private partnerships have focused on risk identification and classification, research still lacks concentration on studying the latent structure of risks in build operate transfer (BOT) transportation projects, especially in developing countries. The research was carried out in Vietnam and this paper aims to explore the underlying relationships among risks in the context of BOT transportation projects.

A questionnaire survey was conducted to investigate the perception of stakeholders regarding the probability of occurrence and the severity of the impact of risks related to BOT transportation projects. Factor analysis was performed based on a total of 40 risks.

Seven risk groups were formed as a result of factor analysis, namely, “projects’ viability and political-regulatory risks”, “macroeconomic risks”, “projects’ feasibility study and market risks”, “financial risks”, “organization/coordination and force majeure risks”, “tolling, contractual, approvals risks” and “media and land expropriation risks”.

The research contributes to the current body of knowledge by providing deep insight into the structure of risks in BOT transportation projects in Vietnam through exploring the underlying relationships among risks, to form a latent risk structure from practical viewpoints. The findings are beneficial for involved stakeholders and policymakers to set up and propose suitable management strategies and related policies.

This paper examines supply chain risk management (SCRM) from a holistic systems thinking perspective by considering the different typologies that have evolved as a result of earlier research. The purpose of this paper is to identify important strategic changes in the field and to outline future requirements and research opportunities in SCRM.

The systematic literature review (SLR) methodology employed by this research was used to evaluate and categorise a literature survey of quality articles published over a period of ten years (2000‐2010). Additionally, the findings from the SLR have been strengthened through cross validation against results obtained from an associated text mining activity.

The SLR methodology has provided a rich, unbiased and holistic picture of the advances in the field of SCRM. Consequently, important new research areas have been identified based on a multi‐perspective descriptive and thematic data analysis. In addition, the analysis, based on evolved typologies, indicates a growth of SCRM from a nascent to a fairly established activity over the past decade.

The systematic approach undertaken for the literature review will provide future researchers and managers with an insightful understanding of the scope of the SCRM field. Also, the literature review provides important clues on new research directions for SCRM through identification of gaps in current knowledge.

The holistic approach to SCRM was found to be an important missing link in earlier literature surveys. The outcome of the SLR reported in this paper has provided critical insights into the present and future scope of the SCRM field. The identified research insights, gaps and future directions will encourage new research techniques, with a view to managing the risks in the globalized supply chain environment.

Construction projects are risky. However, the characteristics of the risk highly depend on the type of procurement being adopted for managing the project. A build‐operate‐transfer (BOT) project is recognized as one of the most risky project schemes. There are instances of project failure where a BOT scheme was employed. Ineffective rts are increasingly being managed using various risk management tools and techniques. However, application of those tools depends on the nature of the project, organization's policy, project management strategy, risk attitude of the project team members, and availability of the resources. Understanding of the contents and contexts of BOT projects, together with a thorough understanding of risk management tools and techniques, helps select processes of risk management for effective project implementation in a BOT scheme. This paper studies application of risk management tools and techniques in BOT projects through reviews of relevant literatures and develops a model for selecting risk management process for BOT projects. The application to BOT projects is considered from the viewpoints of the major project participants. Discussion is also made with regard to political risks. This study would contribute to the establishment of a framework for systematic risk management in BOT projects.

The purpose of this paper is to develop a method for information classification. The proposed method draws on established standards, such as the ISO/IEC 27002 and information classification practices. The long-term goal of the method is to decrease the subjective judgement in the implementation of information classification in organisations, which can lead to information security breaches because the information is under- or over-classified.

The results are based on a design science research approach, implemented as five iterations spanning the years 2013 to 2019.

The paper presents a method for information classification and the design principles underpinning the method. The empirical demonstration shows that senior and novice information security managers perceive the method as a useful tool for classifying information assets in an organisation.

Existing research has, to a limited extent, provided extensive advice on how to approach information classification in organisations systematically. The method presented in this paper can act as a starting point for further research in this area, aiming at decreasing subjectivity in the information classification process. Additional research is needed to fully validate the proposed method for information classification and its potential to reduce the subjective judgement.

The research contributes to practice by offering a method for information classification. It provides a hands-on-tool for how to implement an information classification process. Besides, this research proves that it is possible to devise a method to support information classification. This is important, because, even if an organisation chooses not to adopt the proposed method, the very fact that this method has proved useful should encourage any similar endeavour.

The proposed method offers a detailed and well-elaborated tool for information classification. The method is generic and adaptable, depending on organisational needs.

This study aims to focus on identifying the business risks that cause a delay in the oil and gas projects and suggest the way forward toward the better development of the city gas distribution (CGD) sector in India by suggesting the appropriate mitigation strategies.

The study is a systematic review of literature on risks causing a delay in oil and gas projects. Comprehensive literature was carried out following a seven-step model to develop an exhaustive list of risk classifications and factors, risk identification methods and strategies to mitigate the risks. Weighted average ranking method is used to identify the top ten risks affecting oil and gas projects.

This research identifies the top ten risks frequently impacting the oil and gas projects, which are project cost, improper project management, change in economic parameters, currency exchange rate, government regulations and laws, contractor and subcontractors issues, lack of skilled labor, delay in approvals, health and safety issues and force majeure. These risks are primarily responsible for cost overrun and project delay. Additionally, this study recommends the implementation of joint risk management to avoid CGD project delay.

The CGD industry is in the growing stage with many projects under construction. However, there is a lack of research to manage risks in the CGD project. This study contributes to the limited literature available on risk management in oil and gas projects. Additionally, it highlights the need for further research to explore the different risks factors affecting the CGD business and its operations and subsequently develop appropriate mitigation strategies.

Risk management (RM) is now widely accepted as an important tool in the management of projects. Through a series of semi‐structured interviews with RM facilitators, current practice is explored. The findings provide a number of soft benchmarks. Interest in RM comes largely from educated clients and is regularly adopted as an integrated front‐end service. Ongoing RM studies throughout the project life cycle are limited largely to the public sector and utilities. The use of RM workshops and the production of risk registers are commonplace. The use of Monte Carlo simulation through specialist software is widespread as a means of obtaining a greater degree of confidence in project budgets. There is scepticism regarding the usefulness of complex risk analysis techniques and a predisposition to rely on judgement based on experience. The use of historical data is limited. Evaluation of the service is informal and there is a relative lack of training and skills development underpinning RM provision.

The aim of this study is to analyze the relationship between personality traits and students’ cheating behavior using the five-factor personality model and the fraud triangle factors. This chapter develops an evidential study that has the goal to determine the relationship between the students’ cheating behavior and personality traits by using fraud triangle factors. In this context, 251 surveys have been conducted on students of a foundation university located in Istanbul. As means of data collection, NEO – Five Factor Inventory and Academic Fraud Risk Factors have been used. Data have been analyzed by regression tree analysis. Risk and classification tables have been created before starting the study with a decision tree in which classification and regression trees algorithms were implemented. The results reveal that rationalization behind the cheating is the most important reason for students to copy and people who believed that they were extremely appropriate to copy were responsible ones when analyzed in terms of their personality traits. The results of this study contribute to the literature by discovering the characteristics of those who admit academic dishonesty and underlie the factors or predispositions for engaging in this behavior. For sure, three factors of the fraud triangle may have different levels of significance in this study; in addition, pressure is not associated with the cheating behavior.