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Prior research has emphasised the importance of the early phases of construction projects, as well as the difficulties of procuring engineering services – especially due to the uncertainties. Despite that, studies on the public procurement of engineering services are scarce. Although scholars have shown that uncertainty may affect the choice of control modes, the level of uncertainty that characterises services is not addressed by the two task characteristics: knowledge of the transformation process and output measurability. The purpose is to investigate organisational control in public procurement of engineering services.

The existing control model was adjusted in this study by conceptually adding uncertainty as a third aspect to the two task characteristics. A single case study of the Swedish Transport Administration was used. The empirical data, comprising 14 interviews with managers from the client and engineering consulting companies, were analysed using flexible pattern matching and visual mapping approaches and then illustrated using the model.

The public client did not base its choice of control modes on uncertainty, but rather on the other two task characteristics. Consequently, the service providers argued that the chosen control modes reduced their creativity, increased their financial risks and caused unclear responsibilities. This study therefore shows that uncertainty is an important factor to consider in the choice of control modes, both from a theoretical perspective and from the service providers' point of view. The developed model may therefore be useful for researchers as well as practitioners.

This study is the first attempt to add uncertainty as a task characteristic when choosing control modes. The results contribute to the scarce control literature regarding the procurement of engineering services for construction projects and the procurement of other services with high uncertainty.

Uncertainty in data, whether in real-valued or integer-valued data, may result in infeasible optimal solutions or unreliable efficiency scores and ranking of decision-making units. To handle the uncertainty in integer-valued factors in data envelopment analysis (DEA) models, this study aims to propose a robust DEA model which is applicable in the presence of such factors.

This research focuses on the application of fuzzy interpretation of efficiency to a mixed-integer DEA (MIDEA) model. The robust optimization approach is used to address the uncertain integer-valued parameters in the proposed MIDEA model.

In this study, the authors proposed an MIDEA model without any equality constraint to avoid the arise problem by such constraints in the construction of the robust counterpart of the conventional MIDEA models. We have studied the characteristics and conditions for constructing the uncertainty set with uncertain integer-valued parameters and a robust MIDEA model is proposed under a combined box-polyhedral uncertainty set. The applicability of the developed models is shown in a case study of Malaysian public universities.

This study develops an MIDEA model equivalent to the conventional MIDEA model excluding any equality constraint which is crucial in robust approach to avoid restricted feasible region or infeasible solutions. This study proposes a robust DEA approach which is applicable in cases with uncertain integer-valued parameters, unlike previous studies in robust DEA field where uncertain parameters are generally assumed to be only real-valued.

Fused deposition modeling (FDM) is an extensively used additive manufacturing method with the capacity to build complex functional components. Due to the machinery and environmental factors during manufacturing, the FDM parts inevitably demonstrated uncertainty in properties and performance. This study aims to identify the stochastic constitutive behaviors of FDM-fabricated polylactic acid (PLA) tensile specimens induced by the manufacturing process.

By conducting the tensile test, the effects of the printing machine selection and three major manufacturing parameters (i.e., printing speed S, nozzle temperature T and layer thickness t) on the stochastic constitutive behaviors were investigated. The influence of the loading rate was also explained. In addition, the data-driven models were established to quantify and optimize the uncertain mechanical behaviors of FDM-based tensile specimens under various printing parameters.

As indicated by the results, the uncertain behaviors of the stiffness and strength of the PLA tensile specimens were dominated by the printing speed and nozzle temperature, respectively. The manufacturing-induced stochastic constitutive behaviors could be accurately captured by the developed data-driven model with the R² over 0.98 on the testing dataset. The optimal parameters obtained from the data-driven framework were T = 231.3595 °C, S = 40.3179 mm/min and t = 0.2343 mm, which were in good agreement with the experiments.

The developed data-driven models can also be integrated into the design and characterization of parts fabricated by extrusion and other additive manufacturing technologies.

Stochastic behaviors of additively manufactured products were revealed by considering extensive manufacturing factors. The data-driven models were proposed to facilitate the description and optimization of the FDM products and control their quality.

This paper aims to study a neural network-based fault-tolerant controller to improve the tracking control performance of an unmanned autonomous helicopter with system uncertainty, external disturbances and sensor faults, using the prescribed performance method.

To ensure that the tracking error satisfies the prescribed performance, the authors adopt an error transformation function method. A control scheme based on the neural network and high-order disturbance observer is designed to guarantee the boundedness of the closed-loop system. A simulation is performed to prove the validity of the control scheme.

The developed adaptive fault-tolerant control method makes the system with sensor fault realize tracking control. The error transformation function method can effectively handle the prescribed performance requirements. Sensor fault can be regarded as a type of system uncertainty. The uncertainty can be approximated accurately using neural networks. A high-order disturbance observer can effectively suppress compound disturbances.

The tracking performance requirements of unmanned autonomous helicopter system are considered in the design of sensor fault-tolerant control. The inequality constraint that the output tracking error must satisfy is transformed into an unconstrained problem by introducing an error transformation function. The fault state of the velocity sensor is considered as the system uncertainty, and a neural network is used to approach the total uncertainty. Neural network estimation errors and external disturbances are treated as compound disturbances, and a high-order disturbance observer is constructed to compensate for them.

This paper aims to deepen the understanding of the relationship between global economic uncertainty and price volatility, specifically focusing on commodity, industrial materials and energy price indices as proxies for global inflation, analyzing data from 1997 to 2020.

The dynamic conditional correlation generalized autoregressive conditional heteroscedasticity model is used to study the dynamic relationship between variables over a while.

The results demonstrated a positive relationship between commodity prices and the global economic policy uncertainty (GEPU). Except for 1999–2000 and 2006–2008, the results of the energy price index model were very similar to those of the commodity price index. A predominant positive relationship is observed focusing on the connection between GEPU and the industrial material price index. The results of the pairwise Granger causality reveal a unidirectional relationship between the GEPU – the Global Commodity Price Index – and the GEPU – the Global Industrial Material Price Index. However, there is bidirectional causality between the GEPU – the Global Energy Price Index. In sum, changes in price indices can be driven by GEPU as a political factor indicating unfavorable economic conditions.

This paper provides a deeper understanding of the role of global uncertainty in the global inflation process. It fills the gap in the literature by empirically investigating the dynamic movements of global uncertainty and the three most important groups of prices.

This paper aims to explore the spillover impacts that domestic or global aspects of geopolitical risk generate on uncertainty. The latter is derived from a spectrum of different sources in the USA (economic policy, monetary policy, fiscal policy, national security, government spending, taxation) from 1985 up to November 2022.

Vector autoregressive schemes are used to detect causality and reverse causality between each aspect of geopolitical risk and each source of US uncertainty.

Notably, national security generates higher geopolitical risk by almost 8% in the first month but decreases GPR by 2% in the third month after the shock. USA is found to constitute a cornerstone as regards global peace and that the overall economic or monetary conditions or war status in the USA are remarkably more influential toward domestic and global geopolitical uncertainty than separate strands of fiscal policymaking. Reverse causality displays sizably weaker effects overall.

This study sheds light on the determinants of geopolitical risk and domestic instability by an international perspective and provides a compass for better decision-making for fiscal and monetary policymakers and market participants.

The primary study purpose is to examine whether managerial support triggers job crafting and sustainable employability, and to what extent work uncertainty moderates the managerial support and sustainable employability relationships mediated by job crafting using a moderated mediation approach. Thus, this study aims to uncover new antecedent and outcomes of job crafting using job demands and resources (JD–R) theory as no such research has yet examined these relationships.

The structural equation modeling (SEM) technique was employed to test the proposed relationships based on survey data that include a final 483 knowledge workers of the services sector. The authors tested the structural model using self-developed estimates for AMOS 24.0 to examine the moderated mediation process models (process models 7, 14 and 58) rather than using a conventional process macro through SPSS.

Consistent with the formulated hypothesis, the results of this study indicate that managerial support directly stimulates job crafting and sustainable employability. Further, job crafting mediates the relationships between managerial support and sustainable employability. This validates the JD–R theory assumption that managerial support as job resources initiates a motivational process through job crafting, leading to sustainable employability as the outcome of the motivational process. Additionally, the moderated mediation results show that in the presence of high work uncertainty, employees are more engaged in job crafting to boost their sustainable employability.

Organizations may incorporate these findings while developing human resources (HR) management policies and practices to align top-down and bottom-up job re(design) approaches. For example, by designing line managers’ role in implementation of supportive HR practices, their supportive leadership behavior towards employees will successfully trigger job crafting and nurture their sustainable employability.

This research adds to the work design and employability literature. No such study has yet examined whether managerial support triggers job crafting and sustainable employability. Prior studies examine personality traits, some individual difference variable, job characteristics, or leadership influence as antecedents of job crafting. Utilizing the JD–R theory, we empirically validate that job crafting plays a vital role in the motivational process initiated by the job resources (managerial support), leading to sustainable employability as an outcome of the motivational process. The authors further show that in the presence of high job demands (work uncertainty), employees are more engaged in job crafting and more conscious to boost their sustainable employability.

The purpose of this paper is to construct an index for systemic risk in China.

This paper develops a systemic risk index for China (SRIC) using textual information from 26 leading newspapers in China. Our index measures the systematic risk from 21 topics relating to China’s economy and provides narratives of the sources of systemic risk.

SRIC effectively predicts changes in GDP, aggregate financing to the real economy and the purchasing managers’ index. Moreover, SRIC explains several other commonly used macroeconomic indicators. Our risk measure provides a helpful monitoring tool for policymakers to manage systemic risk.

The paper construct an index of systemic risk based on the information extracted from newspaper articles. This approach is new to the literature.

The purpose of this study is to systematically review the existing literature on the blood supply chain (BSC) from a network design perspective and highlight the research gaps in this area. Moreover, it also aims to pinpoint new research opportunities based on the recent innovative technologies for the BSC network design.

The study gives a comprehensive systematic review of the BSC network design studies until October 2021. This review was carried out in accordance with preferred reporting items for systematic reviews and meta-analyses (PRISMA). In the literature review, a total of 87 studies were analyzed under six main categories as model structure, application model, solution approach, problem type, the parties of the supply chain and innovative technologies.

The results of the study present the researchers’ tendencies and preferences when designing their BSC network models.

The study presents a guide for researchers and practitioners on BSC from the point of view of network design and encourages adopting innovative technologies in their BSC network designs.

The study provides a comprehensive systematic review of related studies from the BSC network design perspective and explores research gaps in the collection and distribution processes. Furthermore, it addresses innovative research opportunities by using innovative technologies in the area of BSC network design.

Although the literature is clear on what comprises effective project stakeholder management, communication between key stakeholders and project managers is often ineffective. Research is silent on stakeholders' insufficient knowledge of project management terminology, which is a barrier for such effective communication. This paper aims to identify the project management concepts that key stakeholders should understand to improve effective communication.

This paper employs a three-step research design. In the first step, based on threshold concept theory, the authors identify the key project management concepts through interviews with 20 project management practitioners, trainers and trainees. In the second step, the authors confirm the findings from the first step and identify effective approaches to enhance project stakeholders' communication through seven additional interviews with project stakeholders. In the third step, the authors construct a functional model of the research findings by employing a system-level modeling tool.

This research identifies five project management threshold concepts (TC) that are challenging for project stakeholders to understand (1) project benefits, (2) the iron triangle, (3) the critical path, (4) uncertainty and (5) project leadership. Following these knowledge barriers, the paper proposes unique approaches to develop effective project stakeholder communication.

This paper advances project stakeholder management research by identifying knowledge barriers and providing project managers with more effective approaches to better engage with their stakeholders.