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Current Army doctrine stresses a need for military leaders to have the capability to make flexible and adaptive decisions based on a future unknown environment, location and enemy. To assess a military decision maker’s ability in this context, this paper aims to modify the Wisconsin Card Sorting Test which assesses cognitive flexibility, into a military relevant map task. Thirty-four military officers from all service branches completed the map task.

The purpose of this study was to modify a current psychological task that measures cognitive flexibility into a military relevant task that includes the challenge of overcoming experiential bias, and understand underlying causes of individual variability in the decision-making and cognitive flexibility behavior of active duty military officers on this task.

Results indicated that non-perseverative errors were a strong predictor of cognitive flexibility performance on the map task. Decomposition of non-perseverative error into efficient errors and random errors revealed that participants who did not complete the map task changed their sorting strategy too soon within a series, resulting in a high quantity of random errors.

This study serves as the first step in customizing cognitive psychological tests for a military purpose and understanding why some military participants show poor cognitive flexibility.

Errors are inevitable, resulting from the human condition itself, system failures and the interaction of both. It is essential to know how to deal with their occurrence, managing them. However, the negative tone associated with them makes it difficult for most organizations to talk about mistakes clearly and transparently, for fear of being harmed, preventing their detection, treatment and recovery. Consequently, errors are not managed, remaining accumulated in the system, turning into successive failures. Organizations need to recognize the inevitability of errors, making the system robust, through leadership and an organizational culture of error management. This study aims to understand the role of these influencing variables in an error management approach.

In this paper, the authors applied the methodology of a quantitative nature based on a questionnaire survey that analyses error management, leadership and the organizational culture of error management of 380 workers in Portuguese companies.

The results demonstrate that leadership directly influences error management and indirectly through the organizational culture of error management, giving this last variable a mediating role.

The study covers companies from different sectors of activity on a topic that is little explored in Portugal, but part of the daily life of organizations, which should deserve greater attention from directors and managers, as they assume a privileged position to promote and develop error management mechanisms. Error management must be the daily work of leaders. This study contributes to theoretical knowledge and business practice on error management.

On shield tunnel construction (STC) site, human error is widely recognized as essential to accident. It is necessary to explain which factors lead to human error and how these factors can influence human performance. Human reliability analysis supports such necessity through modeling the performance shaping factors (PSFs). The purpose of this paper is to establish and validate a PSF taxonomy for the STC context.

The approach taken in this study mainly consists of three steps. First, a description of the STC context is proposed through the analysis of the STC context. Second, the literature which stretch across the PSF methodologies, cognitive psychology and human factors of STC and other construction industries are reviewed to develop an initial set of PSFs. Finally, a final PSF set is modified and validated based on STC task analysis and STC accidents cases.

The PSF taxonomy constituted by 4 main components, 4 hierarchies and 85 PSFs is established for human behavior modeling and simulation under the STC context. Furthermore, by comparing and evaluating the performance of STC PSF and existing PSF studies, the proposed PSF taxonomy meets the requirement for qualitative and quantitative analysis.

The PSF taxonomy can provide a basis and support for human behavior modeling and simulation under the STC context. Integrating PSFs into a behavior simulation model provides a more realistic and integrated assessment of human error by manifesting the influence of each PSFs on the cognitive processes. The simulation results can suggest concrete points for the improvement of STC safety management.

This paper develops a taxonomy of PSFs that addresses the various unique influences of the STC context on human behaviors. The harsh underground working conditions and diverse resources of system information are identified as key characteristics of the STC context. Furthermore, the PSF taxonomy can be integrated into a human cognitive behavior model to predict the worker’s behavior on STC site in future work.

Learning from errors is a complex process that requires careful support. Building on affective events theory, the purpose of this paper is to explore how a supportive learning from error climate can contribute to social learning from errors through affective and cognitive error responses by individual professionals.

A total of 139 early-career auditors completed an online questionnaire consisting of validated survey scales, allowing for serial mediation analysis to compare direct and indirect effects.

Learning from error climate was directly and positively related to engagement in social learning activities after committing an error. Furthermore, the authors found a double mediation by error strain (an affective error response) and reflecting on errors (a cognitive error response) on this relationship.

Organizations can actively encourage professionals to learn from their errors by creating a supportive learning from error climate and holding professionals accountable for their errors.

The present study enriches the authors’ understanding of the mechanisms through which learning from error climate influences engagement in social learning activities. It extends prior research on learning from errors by investigating the sequential effects of engagement in error-related learning activities performed individually and in social interaction.

This paper aims to introduce a new mixed integer programming formulation and associated heuristic algorithm to solve the Military Nodal Capacity Problem, which is a type of supply chain network design problem that involves determining the amount of capacity expansion required at theater nodes to ensure the on-time delivery of military cargo.

Supply chain network design, mixed integer programs, heuristics and regression are used in this paper.

This work helps analysts at the United States Transportation Command identify what levels of throughput capacities, such as daily processing rates of trucks and railcars, are needed at theater distribution nodes to meet warfighter cargo delivery requirements.

This research assumes all problem data are deterministic, and so it does not capture the variations in cargo requirements, transit times or asset payloads.

This work gives military analysts and decision makers prescriptive details about nodal capacities needed to meet demands. Prior to this work, insights for this type of problem were generated using multiple time-consuming simulations often involving trial-and-error to explore the trade space.

This work merges research of supply chain network design with military theater distribution problems to prescribe the optimal, or near-optimal, throughput capacities at theater nodes. The capacity levels must meet delivery requirements while adhering to constraints on the proportion of cargo transported by mode and the expected payloads for assets.

In this paper, we depart from extant conceptualisations of knowledge translation mechanisms to examine projects as a way to achieve effective knowledge transfer. Our empirical analysis focused on a university–industry research project in the automotive industry.

The empirical analysis was based on a qualitative investigation. We analysed material collected within a research project involving a partnership between two universities and Fiat-Chrysler Automotive (FCA), a multi-brand auto manufacturer with a product range covering several different market segments. We used three data collection techniques: internal document analysis, participant observation and semi-structured interviews.

Our findings show that, in a U-I research project, goals represent a key dimension to support knowledge translation. Defining the goal implies an ongoing negotiation process, where researchers and company employees work together, in order to converge towards a shared meaning of the goal. In this sense, goal orientation and goal-based interaction have significant implications for knowledge translation processes.

Studies to date have focussed on the concept of knowledge translation as a way to contextualise the transfer from the source of knowledge to the receiver and to interpret the knowledge to be exchanged. This study expands the understanding of knowledge translation mechanisms in university–industry research settings. It investigates the concept of projects as powerful knowledge translation mechanism in a dynamic and longitudinal perspective. Our contribution provides insight, reflecting on how the use of projects may represent a way to facilitate knowledge transfer and build up new ideas and solutions.

This study proposed an optimal model to examine the relationship between the Bitcoin price and six macroeconomic variables – the Bitcoin price, Standard and Poor's 500 volatility index, US treasury 10-year yield, US consumer price index, gold price and dollar index. It also examined the effectiveness of the vector error correction model (VECM) in analyzing the interrelationship among these variables. The authors employed the following approach: first, the authors sampled the period August 2010–February 2022. This is because Bitcoin achieved a market capitalization of more than US$1 tn over this period, gaining market attention and acceptance from retail, corporate and institutional investors. Second, the authors employed a VECM with the six macroeconomic variables. Finally, the authors expanded the long-run equilibrium relationship (time-invariant cointegration)-based VECM to develop a time-varying cointegration (TVC) VECM. The authors estimated the TVC VECM using the Chebyshev polynomial specification based on various information criteria. The results showed that the Bitcoin price can be modeled with the VECM (p = 1, r = 1). The TVC approach generated more explanatory power for Bitcoin pricing, indicating the effectiveness of the approach for modeling the long-run relationship between Bitcoin price and macroeconomic variables.

The purpose of this study is to explore the perceptions of teachers’ and undergraduate students concerning the provision of written corrective feedback (WCF) in Saudi EFL writing classrooms in the form of a case study.

The sample consisted of three teachers and five students, whose views on WCF were recorded via individual semi-structured interviews to gather qualitative data.

The results revealed some divergent viewpoints between students and teachers and among peers in both groups.

By offering the best WCF practice and considering students' interests, this research is important for improving the pedagogical approaches used by EFL instructors for teaching writing to university learners. It will, therefore, have more of an impact on EFL writing instruction.