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Railways are a well-known example of complex critical infrastructure, incorporating socio-technical systems with humans such as drivers, signallers, maintainers and passengers at the core. The technological evolution including interconnectedness and new ways of interaction lead to new security and safety risks that can be realised, both in terms of human error, and malicious and non-malicious behaviour. This study aims to identify the human factors (HF) and cyber-security risks relating to the role of signallers on the railways and explores strategies for the improvement of “Digital Resilience” – for the concept of a resilient railway.

Overall, 26 interviews were conducted with 21 participants from industry and academia.

The results showed that due to increased automation, both cyber-related threats and human error can impact signallers’ day-to-day operations – directly or indirectly (e.g. workload and safety-critical communications) – which could disrupt the railway services and potentially lead to safety-related catastrophic consequences. This study identifies cyber-related problems, including external threats; engineers not considering the human element in designs when specifying security controls; lack of security awareness among the rail industry; training gaps; organisational issues; and many unknown “unknowns”.

The authors discuss socio-technical principles through a hexagonal socio-technical framework and training needs analysis to mitigate against cyber-security issues and identify the predictive training needs of the signallers. This is supported by a systematic approach which considers both, safety and security factors, rather than waiting to learn from a cyber-attack retrospectively.

According to regulations, aircraft must be in an airworthy condition before they can be operated. To ensure airworthiness, they must be maintained by an approved component maintenance organisation. This study is aimed to identify potential errors that may arise during the final inspection and certification process of aircraft components, categorise them, determine their consequences and quantify the associated risks. Any removed aircraft components must be sent to an approved aircraft component maintenance organisation for further maintenance and issuance of European Union Aviation Safety Agency (EASA) Form 1. Thereafter, a final inspection and certification process must be conducted by certifying staff to receive an EASA Form 1. This process is crucial because any errors during this stage can result in the installation of unsafe components in an aircraft.

The Systematic Human Error Reduction and Prediction Approach (SHERPA) method was used to identify potential errors. This method involved a review of the procedures of three maintenance organisations, individual interviews with ten subject matter experts and a consensus group of 14 certifying staff from different maintenance organisations to achieve the desired results.

In this study, 39 potential errors were identified during the final inspection and certification process. Furthermore, analysis revealed that 48.7% of these issues were attributed to checking errors, making it the most common type of error observed.

This study pinpoints the potential errors in the final inspection and certification of aircraft components. It offers maintenance organisations a roadmap to assess procedures, implement preventive measures and reduce the likelihood of these errors.

The paper aims to discuss error detection and correction in Kashmiri carpet weaving (KCW), mediated by cryptographic code, Talim which is held to guarantee accurate information transference from designing to weaving, even after hundred years. Yet, carpets often show errors on completion.

Human factors analysis revealed error emergence, detection and correction in this practice whose task domains are distributed over large geographies (from in-premises to several kilometers) and timescales (from days to decades). Using prospective observation method, production process of two research carpets from their design, coding and weaving was observed while noting the errors made, identified and corrected by actors in each phase.

The errors were found to emerge, identified and corrected during different phases of designing, coding and weaving while giving rise to fresh errors in each phase, due to actors’ normal work routines.

In view of this, usual branding of “weaver-error” behind flawed carpet turns out to be misplaced value judgment passed in hindsight.

Although the innovative behaviour of public employees is critical for the creation of public value and meeting of public interests, the authors are uncertain about the role of the human resource (HR) system in affecting individual behaviour as past studies tended to discuss innovation at the organisational level of analysis. Based on corporate human resource management (HRM) literature, the authors draw from the ability-motivation-opportunity (AMO) model to examine the influence of innovation-based HR practices on work-related risk propensity and innovative behaviour and the moderating role of perceived error tolerance of public sector organisations.

Dyadic data were collected from supervisors and their subordinates in various public sector organisations in the UAE. The authors collected valid responses from 100 managers and 200 employees.

This study's findings demonstrate that the HR system in the public sector shapes employees' behaviour at the individual level of analysis, consistent with the corporate HRM literature. The authors reveal that innovation-based HR practices significantly promote employees' innovative work behaviour because they trigger their inclination and disposition to take risks. Furthermore, the authors provide evidence that such risk-taking propensity at work is heightened under the conditions of a high level of error tolerance by the organisational management.

This study's findings point out the importance of implementing innovation-based HR practices, such as recruitment, reward and training, to drive public sector employees' innovative work behaviour as they could galvanise their risk-taking propensity and, subsequently, innovative behaviour. Public sector managers also need to develop an innovation culture tolerant toward employees' mistakes to further foster employees' work innovativeness. Policy wise, this study's findings could be integrated into the national innovation strategy to drive the national growth in the UAE.

This study sheds light on the drivers behind innovative behaviour among public employees, which is a less researched area, especially in a non-Western context.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

This paper investigates whether error management orientation (EMO) of hospitality employees influence their service recovery performance (SRP) through self-efficacy.

In Study 1, data was collected from 161 hotel managers in the USA. In Study 2, data was collected from 215 restaurant employees in Turkey. Partial least squares (PLS) method using SmartPLS 3.3.3 was used for data analysis.

The results indicated that EMO of hospitality employees increases their self-efficacy beliefs which in turn enhance their SRP. The findings were consistent in both studies.

Hospitality organizations should consider assessing EMO of individuals when making selection decisions. These organizations should also consider providing error management training to employees to develop their EMO, improve error management skills and performance.

To the best of the author’s knowledge, this is the first study that focuses on EMO of hospitality managers and employees. Error orientation refers to how individuals cope with and how they think about errors at work. Errors are part of our work lives, and a positive orientation toward errors (i.e. EMO) can have a significant impact on individuals’ work attitudes, behaviors and performances. This is the first study that examines EMO as an important predictor of SRP. This study also makes a contribution by studying the mediating effect of self-efficacy to understand the underlying mechanism that links EMO with SRP.

Information infrastructures can enable or constrain how companies pursue their visions of sustainability reporting and help address the urgent need to understand how corporate activity affects sustainability outcomes and how socio-ecological challenges affect corporate activity. The paper examines the relationship between sustainability reporting information infrastructures and sustainability reporting practice.

The paper mobilises a socio-technical perspective and the conception of infrastructure, the socio-technical arrangement of technical artifacts and social routines, to engage with a qualitative dataset comprised of interview and documentary evidence on the development and construction of sustainability reporting information.

The results detail how sustainability reporting information infrastructures are used by companies and depict the difficulties faced in generating reliable sustainability data. The findings illustrate the challenges and measures undertaken by entities to embed automation and integration, and to enhance sustainability data quality. The findings provide insight into how infrastructures constrain and support sustainability reporting practices.

The paper explains how infrastructures shape sustainability reporting practices, and how infrastructures are shaped by regulatory demands and costs. Companies have developed “uneven” infrastructures supporting legislative requirements, whilst infrastructures supporting non-legislative sustainability reporting remain underdeveloped. Consequently, infrastructures supporting specific legislation have developed along unitary pathways and are often poorly integrated with infrastructures supporting other sustainability reporting areas. Infrastructures developed around legislative requirements are not necessarily constrained by financial reporting norms and do not preclude specific sustainability reporting visions. On the contrary, due to regulation, infrastructure supporting disclosures that offer an “inside out” perspective on sustainability reporting is often comparatively well developed.

The purpose of this paper is to describe an example of how universities can develop unique synchronous educational opportunities to engage students in an online environment, especially since study abroad opportunities have slowed or halted due to the COVID-19 pandemic crisis.

Two affiliated universities, in the United States and Scotland, selected a simulated root cause analysis (RCA) of a medical error for a collaborative activity.

Together the students analyzed the event within assumed roles of healthcare professions in a systematic way using the “Swiss Cheese Model,” identified opportunities for improvement, debriefed using the Promoting Excellence and Reflective Learning in Simulation (PEARLS) method and submitted a reflective assignment. The RCA resulted in several process improvement suggestions and positive feedback from the participants.

The approach was for graduate students from two affiliated universities, in the United States and Scotland, to simulate a RCA of a medical error as a collaborative activity.

This example using simulation of healthcare roles demonstrates the application of international collaboration, problem-solving models and methodologies and recognition of the similarities and differences between the United Kingdom and the United States approaches to patient safety. Especially with the ongoing COVID-19 pandemic, the students' ability to internationally collaborate can be limited without such unique opportunities.

The students were provided with a side-by-side comparison of the patient safety efforts in each country and written and didactic material about an RCA and the Swiss cheese model.

Students come from different perspectives of health services administration, nursing and nursing leadership from Scotland and the United States to solve patient safety problems.

Integrating artificial intelligence (AI) into various industries, including the financial sector, has transformed them. This paper aims to examine the influence of integrating AI, including machine learning, process automation, predictive analytics and chatbots, on financial institutions and explores its various aspects and areas. The study aims to determine the impact of AI integration on financial services, products and customer experience.

The research study uses quantitative and qualitative methods, as well as secondary data analysis. It investigates four AI subfields: machine learning, process automation, predictive analytics and chatbots.

The research findings indicate that integrating AI, particularly in machine learning and chatbot subfields, holds promise and high strategic potential for financial institutions. These subfields can contribute significantly to enhancing financial services and customer experience. However, the significance of predictive analytics integration and process automation is relatively lower. Although these subfields retain their usefulness, they might necessitate alternative workflows and tools that incorporate human involvement. Overall, AI integration minimizes human interactions and errors in financial institutions.

The research study contributes original insights by exploring the specific subfields of AI within the financial industry and assessing their strategic significance. It provides recommendations for financial institutions to adopt AI integration partially in multiple phases, measure and evaluate the impact of the transformation and structure internal units and expertise to strategize adoption and change.