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The paper seeks to develop an analytical theory of project investment.

The authors derive a partial differential equation that the variable cost of a project should satisfy, determine a proper initial condition through a thought experiment, and solve the equation.

A formula of variable cost as an analytical function of fixed cost, uncertainty of the environment and the duration of a project is obtained.

The analytical formula enables systematic comparison of returns of different investment under different market conditions to be made. This refines the insights from real option theory in many ways. Since all production systems need fixed investment to lower variable costs, by providing an analytical theory about the relation among fixed costs, variable costs and uncertainty, this theory contributes a new foundation to investment theory and other different fields.

An analytical theory of project investment about the relation among fixed costs, variable costs, uncertainty of the environment and the duration of a project, which is the core concern in most business decisions, does not exist in the current literature.

Purpose: This study examines whether changes in environmental and funding uncertainty during the first three years after the outbreak of the global financial crisis (which we presume to have increased significantly) are associated with changes in cost system design and intensity of use.

Design/methodology/approach: A dataset of survey responses from 56 Dutch municipalities is used for the empirical analyses. In the questionnaire, a senior-level financial manager reflected on the changes that he or she had perceived during the three years prior to the study (which was conducted at the end of 2010).

Findings: The results show that during these years, on average, ­environmental and funding uncertainty have indeed significantly increased, whereas cost system design and intensity of use have shown little change. The results further indicate that change in environmental uncertainty is positively related to changes in cost system complexity and cost system inclusiveness for activities and/or programs, whereas change in funding uncertainty is positively related to change in cost system intensity of use for product costing purposes. Also, change in cost system complexity is positively related to changes in cost system intensity of use for both operational control and product costing purposes.

Originality/value: Whereas previous large-scale research tends to focus on how the level of cost system design and/or intensity of use characteristics is related to the level of contextual factors, this study focuses on how changes in cost system design and intensity of use characteristics are related to changes in contextual factors. Also distinctive is that this study focuses on local government organizations experiencing a fiscal crisis.

This paper aims to design an optimal supply chain network and to develop a suitable distribution planning under uncertainty for perishable product's supply chain. The ultimate goal is to help in making decisions under uncertain environments.

In this paper, stochastic programming is used under conditions of demand, supply and process uncertainties, and a non-linear mathematical model is developed for perishable product’s supply chain. Authors’ study considers disruptions in transportation routes and also within the facilities and investigates optimal facility location and shipment decisions while minimising the total supply chain cost. A scenario-based approach is used to model these disruptions. The retailer level uncertainty due to demand-supply mismatch is handled by incorporating the newsvendor model into the last echelon of supply chain network. In this paper, two policies are proposed for making decisions under uncertain environments. In the first one, the expected cost of the supply chain is minimised. To also consider the risk behaviour of the decision maker, authors propose the second policy through a conditional value-at-risk approach.

Authors discuss the model output through various examples that are provided via a case study from the milk industry. The supply chain design and planning of the disruption-free model are different from those of the resilient model.

Authors’ research benefits the perishable products industries which encounter the disruption problems in their transportation routes as well as in the facilities. Authors have demonstrated the research through a real-life case in a milk industry.

The major contribution of authors’ work is the design of the supply chain network under disruption risks by incorporating aspects of product perishability. This work provides insight into areas such as the simultaneous consideration of demand, supply and process uncertainties. The amalgamation of newsvendor model and the approximation of the non-linearity of retailer level cost function especially in the context of supply chain under uncertainty is the first of its kind. We provide a comprehensive statistical study of uncertainties that are present in the supply chain in a unique manner.

In the past decades, artificial intelligence (AI)-based hybrid methods have been increasingly applied in construction risk management practices. The purpose of this paper is to review and compile the current AI methods used for cost-risk assessment in the construction management domain in order to capture complexity and risk interdependencies under high uncertainty.

This paper makes a content analysis, based on a comprehensive literature review of articles published in high-quality journals from the years 2008 to 2018. Fuzzy hybrid methods, such as fuzzy-analytical network processing, fuzzy-artificial neural network and fuzzy-simulation, have been widely used and dominated in the literature due to their ability to measure the complexity and uncertainty of the system.

The findings of this review article suggest that due to the limitation of subjective risk data and complex computation, the applications of these AI methods are limited in order to address cost overrun issues under high uncertainty. It is suggested that a hybrid approach of fuzzy logic and extended form of Bayesian belief network (BBN) can be applied in cost-risk assessment to better capture complexity-risk interdependencies under uncertainty.

This study only focuses on the subjective risk assessment methods applied in construction management to overcome cost overrun problem. Therefore, future research can be extended to interpret the input data required to deal with uncertainties, rather than relying solely on subjective judgments in risk assessment analysis.

These results may assist in the management of cost overrun while addressing complexity and uncertainty to avoid chaos in a project. In addition, project managers, experts and practitioners should address the interrelationship between key complexity and risk factors in order to plan risk impact on project cost. The proposed hybrid method of fuzzy logic and BBN can better support the management implications in recent construction risk management practice.

This study addresses the applications of AI-based methods in complex construction projects. A proposed hybrid approach could better address the complexity-risk interdependencies which increase cost uncertainty in project.

The purpose of this paper is to find how those uncertainty factors influence transaction costs generated and to identify ways to minimize the transaction costs borne by the construction owner.

The literature indicates that there is no consensus on a standard definition of transaction costs in the construction industry. A detailed literature review of research work on transaction costs in construction is conducted in order to identify the determinants of transaction costs in construction projects. A structural equation model is tested on data collected by means of a survey administered to construction owners.

The findings indicate that the transaction costs borne by the owner can be minimized if the owner minimizes the uncertainties inherent in the construction project by making sure the engineering design is as complete as possible before bids are sought from contractors; harmonious relationships between project participants; fair risk allocation; have experience in similar type projects; and contractor selection practices that routinely detect irregular behavior.

The data used in this research are primarily based on the experiences of public owners and the markets in which they operate; a larger representation of private owners could make the conclusions more general. Another limitation of the study is that it relies on a survey of opinions rather than actual records of costs and other hard data.

No empirical study has ever been conducted of transaction-related issues in the construction industry because of the lack of a common understanding of transaction cost. This paper provides the groundwork for such a study.

This paper attempts to reconcile the many determinants of transaction costs in construction projects under uncertainty considered by different researchers in a multitude of research studies.

This paper aims to study the impact of geopolitical uncertainty on corporate cost of debt and the moderating role of information asymmetry between creditors and borrowing firms.

This study uses 5,223 firm-quarter observations on German-listed firms spanning 2010:Q1–2021:Q4. This study regresses the cost of debt financing on the geopolitical risk, accounting quality and other control variables. Information asymmetry is measured using the performance-matched Jones-model discretionary accrual and the stock bid-ask spread. It uses interaction terms to check if information asymmetry moderates the impact of geopolitical uncertainty on the cost of debts and control for the moderating role of business risk. For the sake of robustness check, it uses long-term cost of debt and bond spread as alternative dependent variables. In addition, this study executes instrumental variables regression and propension score matching to control for potential endogeneity problems.

Estimation results show that geopolitical uncertainty exerts a positive impact on the cost of debt. This impact is found to be more important on the cost of long-term debts. Information asymmetry is found to exacerbate the positive impact of geopolitical risk on the cost of debt. These results are robust to the change of the dependent variable and to the mitigation of potential endogeneity. At high levels of information asymmetry, this impact is more important for firms belonging to “Transportation”, “Automobiles and auto parts”, “Chemicals”, “Industrial and commercial services”, “Software and IT services” and “Industrial goods” business sectors.

Geopolitical uncertainty should be seriously considered when setting strategies for corporate financial management in Germany and similar economies that are directly exposed to geopolitical risks. Corporate managers should design a comprehensive set of corporate policies to improve their transparency and accountability during increasing uncertainty. Policymakers are required to implement innovative monetary and fiscal policies that take into consideration the heterogeneous impact of geopolitical uncertainty and information transparency in order to contain their incidence on German business sectors.

Despite its relevance to corporate financing conditions, little is known about the impact of geopolitical uncertainty on the cost of debt financing. To the best of the authors’ knowledge, there is still no empirical evidence on how information asymmetry between creditors and borrowing firms shapes the impact of geopolitical uncertainty on the cost of debt. This paper tries to fill this gap by interacting two measures of information asymmetry with geopolitical uncertainty. In contrast with previous studies, this study shows that the impact of geopolitical uncertainty on the cost of debt is non-linear and heterogeneous. The results show that the impact of geopolitical uncertainty does not exert the same impact on the cost of debt instruments with different maturities. This impact is found to be heterogeneous across business sectors and to depend on the level of information asymmetry.

In the light of challenges experienced in cost estimation at the bidding stage of complex engineering services in the defence industry (e.g. contracting for availability), the purpose of this paper is to present a framework to manage the influence of uncertainty on cost estimates.

The research applied the Soft Systems Methodology and benefitted from interaction with four major organisations in the defence industry through document sharing, semi-structured interviews, workshops, and case studies.

The framework is composed of seven stages to plan, identify, prioritise, classify, and manage cost uncertainties. Through the validation of three case studies some of the key benefits of the framework were realised in project planning, uncertainty visualisation, and capability management.

The research has been applied in the defence sector in the UK and focuses on the bidding stage. Further research needs to be applied to confirm that the findings are applicable across industries and across the life cycle.

The paper builds on the theory behind risk and uncertainty management and proposes an innovative framework that avoids the assumption of “perfect” knowledge by raising questions about the validity of the input data.

This study aims to design a proper supply chain network for the vaccine industry in Iran, which considers several features such as uncertainties in demands and cost, perishability of vaccines, wastages in storage, limited capacity and different priorities for demands.

This study presents a mixed-integer linear programming (MILP) model and using a robust counterpart approach for coping with uncertainties of model.

The presented robust model in comparison with the deterministic model has a better performance and is more reliable for network design of vaccine supply chain.

This study considers uncertainty in the network design of vaccine supply chain for the first time in the vaccine context It presents an MILP model where strategic decisions for each echelon and tactical decisions among different echelons of supply chain are determined. Further, it models the difference between high- and low-priority demands for vaccine.

This study aims to form a new concept of power-to-gas-based virtual power plant (GVPP) and propose a low-carbon economic scheduling optimization model for GVPP considering carbon emission trading.

In view of the strong uncertainty of wind power and photovoltaic power generation in GVPP, the information gap decision theory (IGDT) is used to measure the uncertainty tolerance threshold under different expected target deviations of the decision-makers. To verify the feasibility and effectiveness of the proposed model, nine-node energy hub was selected as the simulation system.

GVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. The IGDT method can be used to describe the impact of wind and solar uncertainty in GVPP. Carbon emission rights trading can increase the operating space of power to gas (P2G) and reduce the operating cost of GVPP.

This study considers the electrical conversion and spatio-temporal calming characteristics of P2G, integrates it with VPP into GVPP and uses the IGDT method to describe the impact of wind and solar uncertainty and then proposes a GVPP near-zero carbon random scheduling optimization model based on IGDT.

This study designed a novel structure of the GVPP integrating P2G, gas storage device into the VPP and proposed a basic near-zero carbon scheduling optimization model for GVPP under the optimization goal of minimizing operating costs. At last, this study constructed a stochastic scheduling optimization model for GVPP.

The paper’s purpose is to consider, using a transaction cost economics (TCE) framework, the mechanisms used by space agencies to encourage private investment in the commercial spaceflight sector.

The authors conducted a content analysis of 554 pages of news articles, relating to issues pertaining to partnerships between national government-based space agencies and private space travel providers, published over a 20-year period. Leximancer was used to initially screen the data and then the authors manually analysed the content to identify themes.

The data analysis revealed three themes, relating to: the uncertainty of space travel; National Aeronautics and Space Administration (NASA) stimulating innovation in the private sector; and risk, insurance and regulation. These themes informed by TCE reveal the “hierarchical” organisational forms used to achieve human spaceflight and then the “hybrids”, insurance and regulations used to stimulate private sector investment and innovation.

This paper contributes to the accounting literature by answering the calls of Alewine (2020) and Tucker and Alewine (2022a, b) for more research into accounting in the space context. Specifically, the paper contributes by identifying mechanisms used by NASA to stimulate private investment in the space travel sector, as well as issues that have affected the implementation of these mechanisms. The paper also contributes to the literature by, based on the analysis, identifying a series of reflections designed to stimulate further management accounting research in the space context.