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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.

New business practices and the globalization of markets force firms to take innovation as the fundamental pillar of their competitive strategy. Research and Development (R&D) plays a vital role in innovation. As technology advances and product life cycles become shorter, firms rely on R&D as a strategy to invigorate innovation. R&D project portfolio selection is a complex and challenging task. Despite the management's efforts to implement the best project portfolio selection practices, many projects continue to fail or miss their target. The problem is that selecting R&D projects requires a deep understanding of strategic vision and technical capabilities. However, many decision-makers lack technological insight or strategic vision. This article aims to provide a method to capitalize on the expertise of R&D professionals to assist managers in making informed and effective decisions. It also provides a framework for aligning the portfolio of R&D projects with the organizational vision and mission.

This article proposes a new strategic approach for R&D project portfolio selection using efficiency-uncertainty maps.

The proposed strategy plane helps decision-makers align R&D project portfolios with their strategies to combine a strategic view and numerical analysis in this research. The proposed strategy plane consists of four areas: Exploitation Zone, Challenge Zone, Desperation Zone and Discretion Zone. Mapping the project into this strategic plane would help decision-makers align their project portfolio according to the corporate perspectives.

The new approach combines the efficiency and uncertainty dimensions in portfolio selection into an integrated framework that: (i) provides a complete representation of the stochastic decision-making processes, (ii) models the endogenous uncertainty inherent in the project selection process and (iii) proposes a computationally practical and visually unique solution procedure for classifying desirable and undesirable R&D projects.

Uncertainty management (UM) in projects has been a point of attention for researchers for many years. Research on UM has mainly been aimed at uncertainty analyses in the front-end and managing uncertainty in the construction phase. In contrast, UM components in the design phase have received less attention. This research aims to improve knowledge about the key components of UM in the design phase of large road projects.

This study adopted a literature review and case study. The literature review was used to identify relevant criteria for UM. These criteria helped to design the interview guide. Multiple case study research was conducted, and data were collected through document study and interviews with project stakeholders in two road projects. Each case's owners, contractors and consultants were interviewed individually.

The data analysis obtained helpful information on the involved parties, process and exploit tools and techniques during the design phase. Johansen's (2015) framework [(a) human and organisation, (b) process and (c) tools and techniques)] was completed and developed by identifying relevant criteria (such as risk averse or risk-taker, culture and documentation level) for each component. These criteria help to measure UM performance. The authors found that owners and contractors are major formal UM actors, not consultants. Empirical data showed the effectiveness of Web-based tools in UM.

The studied cases were Norwegian, and this study focussed on uncertainties in the project's design phase. Relevant criteria did not cover all the criteria for evaluating the performance of UM. Qualitative evaluation of criteria allows further quantitative analysis in the future.

This paper gave project owners and managers a better understanding of relevant criteria for measuring UM in the owners and managers' projects. The paper provides policy-makers with a deeper understanding of creating rigorous project criteria for UM during the design phase. This paper also provides a guideline for UM in road projects.

This research gives a holistic evaluation of UM by noticing relevant criteria and criteria's interconnection in the design phase.

This study aims to examine factors influencing potential commercial innovators' intention to innovate with open government data (OGD) via a risk perspective.

The authors develop a theoretical model that explains how different forms of uncertainty (i.e. financial, technology, competitive, demand, and data) and their inter-relationships influence potential commercial innovators' intention to innovate with OGD. The model is tested using survey data collected from 144 potential commercial innovators from a developed Asian country.

The results suggest that all other forms of uncertainty, except competitive uncertainty, negatively influence potential commercial innovators' intention to innovate, mediated by their perceived risk of innovating with OGD. The results also show positive relationships between different forms of uncertainty, i.e. competitive and financial, demand and competitive, data and financial uncertainty.

This paper identifies major forms of innovation uncertainty, perceived risk, their inter-relationships, and impacts on the intention to innovate with OGD. It also finds support for a unique form of uncertainty for OGD innovation (i.e. data uncertainty).

This paper provides a practical framework for managers to develop a sustainable supply chain. Given that rapid globalization has increased supply disruption risk, managers have been forced to establish efficient and responsive supply chain strategies. Nevertheless, diverse uncertainty factors, such as risk perception of strategies, have made practical management difficult. Quantifying managers' risk perceptions and applying them to supply chain strategies allows the authors to propose a structural and practical model for managing supply disruption.

The existing structural model is refined by taking subjective factors into account using the analytic hierarchy process. The applicability of the refined model is demonstrated through a comparative case study.

Managers' risk perceptions vary not only among companies but also between managing divisions within a company, which necessitates possible changes in strategy due to environmental turbulence. The principal component analysis (PCA) characterizes managers' risk perceptions that illustrate companies' emphases on disruption risk.

The proposed approach quantifies risk perception, which enables practitioners to deal with subjective information in quantitative form. Comparative studies clarify differences in perception given different business backgrounds. The results provide managers with in-depth insights for establishing supply chain strategies reflecting their risk perception.

Quantification of managers' subjective risk perception clarifies both the trend and the individual features for uncertainties. The results allow the authors to conduct the PCA, which characterizes companies. Comparative studies generalize the results of extant work, shedding light on cross-sectional differences given different business backgrounds. The effectiveness of the approach is confirmed through retrospective interviews with practitioners.

The purpose of this paper is to investigate the risk and economic policy uncertainty (EPU) shocks on China’s equity markets while controlling for changes in sentiments and liquidity.

The GED-TARCH(1,1)-M procedure is used in estimations to deal with the heteroscedasticity problem.

Evidence shows that stock returns are positively correlated with predictable volatility and lagged downside risk. This study indicates that the stock returns are negatively correlated with both local and global uncertainty innovations. The test results are robust across different measures of stock returns and model specifications. The global EPU innovations have more profound impact on stock returns than that of Chinese EPU.

The findings are based on the data in the China’s stock market, other global markets may be considered in the future research.

Evidence indicates that a rise in EPU produces a negative effect on stock returns at the time news hits a market; however, investors will be rewarded by a premium as prices rebound in the subsequent period for compensating the investment decision made at a high uncertainty period.

The excess stock returns are negatively related to the EPU innovations, regardless of whether EPU originates from a domestic source or external sources.

The purpose of this paper is twofold, first providing researchers with an overview about the uncertainties occurred in procurement including applicable approaches for analyzing different uncertain scenarios, and second proposing directions to inspire future research by identifying research gaps.

Papers related to supply chain risk management and procurement risk management (PRM) from 1995–2017 in several major databases are extracted by keywords and then further filtered based on the relevance to the topic, number of citations and publication year. A total of over 156 papers are selected. Definitions and current approaches related to procurement risks management are reviewed.

Five main risks in procurement process are identified. Apart from summarizing current strategies, suggestions are provided to facilitate strategy selection to handle procurement risks. Seven major future challenges and implications related PRM and different uncertainties are also indicated in this paper.

Procurement decisions making under uncertainty has attracted considerable attention from researchers and practitioners. Despite the increasing awareness for risk management for supply chain, no detail and holistic review paper studied on procurement uncertainty. Managing procurement risk not only need to mitigate the risk of price and lead time, but also need to have sophisticated analysis techniques in supply and demand uncertainty.

The contribution of this review paper is to discuss the implications of the research findings and provides insight about future research. A novel research framework is introduced as reference guide for researchers to apply innovative approach of operations research to resolve the procurements uncertainty problems.

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.

A growing need for global sourcing of business has subjected firms to higher levels of uncertainty and increased risk of supply disruption. Differences in industry and infrastructure make it more difficult for firms to manage supply disruption risks effectively. The purpose of this paper is to extend developing research in this area by addressing gaps within existing literature related to environmental turbulence and uncertainties.

The authors test the model using data collected from 253 senior managers and directors in the Thai beverage industry using advanced statistical techniques to explore the relationship between representations of supply disruption risk and uncertainty.

The results show that both magnitude and probability of risk impact on the disruption risk, but the probability of loss is a dominant determinant. The authors also find that demand uncertainty and quality uncertainty affect the risk perception of purchasing managers, and are related to the magnitude of disruption risk, rather than the frequency of occurrence. Interestingly, the results show that quality uncertainty negatively impacts on the severity of disruption risk.

The construct validity of demand uncertainty was under the required threshold, intimating the need for further construct development.

The framework provides managers with direction on how to formulate and target their disruption risk management strategies. The work also allows practitioners to critical reflect on implicit risk management strategies they may already employ and their effectiveness.

The paper identifies key antecedents of supply disruption risk and tests them within a novel industrial context of the beverage industry and a novel national context of Thailand.

The objective of this paper is to offer a validated framework for the analysis of (future) risks and uncertainties involved in the decision‐making process concerning the upgrade and roll‐out of large infrastructural projects, e.g. broadband networks. The framework classifies risks and uncertainties based on the nature of the risks, levels and sources.

The approach takes the form of conceptual as well as qualitative and quantitative empirical analyses.

Telecommunications operators are faced with various types of risks and uncertainties in their decision‐making process concerning the upgrade and roll‐out of their broadband networks. In one respect, these risks and uncertainties have to do with the characteristics of large infrastructural projects, while, on the other hand, being caused by (unknown) competitor behaviour, (unknown) end‐user demand, rapid technological development and different development paths available to operators. Framing risks and uncertainties into a typology provides greater insight into the categories, characteristics and sources of the risks and uncertainties, as well as being a first step in finding ways to deal with them.

The paper presents and validates a framework for the analysis of risks and uncertainty. It also offers empirical data on how operators manage risk and uncertainties.