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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.

This research study aims to inquire into the technostress phenomenon at an organizational level from machine learning (ML) and artificial intelligence (AI) deployment. The authors investigated the role of ML and AI automation-augmentation paradox and the socio-technical systems as coping mechanisms for technostress management amongst managers.

The authors applied an exploratory qualitative method and conducted in-depth interviews based on a semi-structured interview questionnaire. Data were collected from 26 subject matter experts. The data transcripts were analyzed using thematic content analysis.

The study results indicated that role ambiguity, job insecurity and the technology environment contributed to technostress because of ML and AI technologies deployment. Complexity, uncertainty, reliability and usefulness were primary technology environment-related stress. The novel integration of ML and AI automation-augmentation interdependence, along with socio-technical systems, could be effectively used for technostress management at the organizational level.

This research study contributed to theoretical discourse regarding the technostress in organizations because of increased ML and AI technologies deployment. This study identified the main techno stressors and contributed critical and novel insights regarding the theorization of coping mechanisms for technostress management in organizations from ML and AI deployment.

The phenomenon of technostress because of ML and AI technologies could have restricting effects on organizational performance. Executives could follow the simultaneous deployment of ML and AI technologies-based automation-augmentation strategy along with socio-technical measures to cope with technostress. Managers could support the technical up-skilling of employees, the realization of ML and AI value, the implementation of technology-driven change management and strategic planning of ML and AI technologies deployment.

This research study was among the first few studies providing critical insights regarding the technostress at the organizational level because of ML and AI deployment. This research study integrated the novel theoretical paradigm of ML and AI automation-augmentation paradox and the socio-technical systems as coping mechanisms for technostress management.

This paper aims to develop a framework for critical information infrastructure (CII) protection in smart government, an alternative measure for common cybersecurity frameworks such as NIST Cybersecurity Framework and ISO 27001. Smart government is defined as the government administration sector of CII due to its similarity as a core of smart technology.

To ensure the validity of the data, the research methodology used in this paper follows the predicting malfunctions in socio-technical systems (PreMiSTS) approach, a variation of the socio-technical system (STS) approach specifically designed to predict potential issues in the STS. In this study, PreMiSTS was enriched with observation and systematic literature review as its main data collection method, thematic analysis and validation by experts using fuzzy Delphi method (FDM).

The proposed CII protection framework comprises several dimensions: objectives, interdependency, functions, risk management, resources and governance. For all those dimensions, there are 20 elements and 41 variables.

This framework can be an alternative guideline for CII protection in smart government, particularly in government administration services.

The author uses PreMiSTS, a socio-technical approach combined with thematic analysis and FDM, to design a security framework for CII protection. This combination was designed as a mixed-method approach to improve the likelihood of success in an IT project.

This paper systematically reviewed research work on drivers of teamwork, which will reinforce construction work teams to enhance workers’ safety performance. This study adds to the existing but limited understanding of teamwork drivers on construction workers’ safety performance. This paper presents scholars and industry-based professionals with critical initiatives that have to be implemented in organisations to get positive results in safety while working in teams with an emphasis on systems drivers of teamwork on safety performance at the organisational level, which will help in providing information on the functioning of the teams and contribute towards improved safety performance of team workers.

This study aims to systematically examine the existing body of knowledge on drivers of teamwork by analysing 53 publications from the years 1997–2021. The Scopus search engine was used to conduct a systematic review and germane publications were collated.

According to the findings of the review, since 1997, there has been a burgeoning concern in the research of drivers of teamwork and its impact on workers’ safety performance. After performing a systematic review, 37 drivers of teamwork were identified. The top five drivers are effective communications, team workers’ relations, leadership, shared knowledge and information, and team training. Moreover, it was noted that the United States and Australia have been the international regions of focus for most of the research in the area of drivers of teamwork from the years 1997–2021. The 37 drivers of teamwork are distributed into six major socio-technical components: people drivers; culture drivers; metrics drivers; organisational and management practices and procedures drivers; infrastructure drivers and technology drivers.

The results reported present research scholars and professional practitioners with an overview of the drivers of teamwork that could be implemented in the construction industry to streamline potential implementations and improve safety performance of construction workers.

A list of teamwork drivers has been developed to ratify potential empirical research in the area of construction safety. The results would contribute to the existing but restricted understanding of drivers of teamwork in the construction industry.

Despite the opportunities of digital twins (DTs) for smart buildings, limited research has been conducted regarding the facility management stage, and this is explained by the high complexity of accurately representing and modelling the physics behind the DTs process. This study thus organises and consolidates the fragmented literature on DTs implementation for smart buildings at the facility management stage by exploring the enablers, applications and challenges and examining the interrelationships amongst them.

A systematic literature review approach is adopted to analyse and synthesise the existing literature relating to the subject topic.

The study revealed six main categories of enablers of DTs for smart building at the facility management stage, namely perception technologies, network technologies, storage technologies, application technologies, knowledge-building and design processes. Three substantial categories of DTs application for smart buildings were revealed at the facility management stage: efficient operation and service monitoring, efficient building energy management and effective smart building maintenance. Subsequently, the top four major challenges were identified as being “lack of a systematic and comprehensive reference model”, “real-time data integration”, “the complexity and uncertainty nature of real-time data” and “real-time data visualisation”. An integrative framework is finally proposed by examining the interactive relationship amongst the enablers, the applications and the challenges.

The findings could guide facility managers/engineers to fairly understand the enablers, applications and challenges when DTs are being implemented to improve smart building performance and achieve user satisfaction at the facility management stage.

This study contributes to the knowledge body on DTs by extending the scope of the existing studies to identify the enablers and applications of DTs for smart buildings at the facility management stage and the specific challenges.

No study examines the role of human resources management (HRM) and information technology (IT) in stimulating supply chain learning (SCL) and operational performance. The purpose of this study is to empirically examine the impact of HRM and IT on SCL (i.e. internal, supplier and customer learning) and operational performance using socio-technical systems theory.

On the basis of data obtained from 213 Chinese manufacturing firms, the authors apply structural equation modeling to test the conceptual model.

This study finds that HRM improves all three dimensions of SCL, whereas IT improves internal and supplier learning only. The authors also observe that internal and customer learning improves operational performance. Supplier learning, on the other hand, has no influence on operational performance.

This study offers new guidelines that help managers to better understand how to design sociotechnical systems to improve SCL and operational performance.

The results of this study provide a novel framework to recognize linkages between socio-technical systems, SCL and operational performance.

This study aims to develop a hierarchical model that uncovers the relationships between challenges confronting Africa's organ transplant supply chain systems.

Eleven challenges (variables) were identified after a comprehensive review of the existing literature. The contextual interactions among these variables were analysed from the perspectives of health-care stakeholders in two sub-Saharan Africa (SSA) countries (Nigeria and Uganda), using Delphi-interpretive structural modelling-cross-impact matrix multiplication applied to classification (MICMAC) techniques.

The findings reveal that weak regulatory frameworks, insufficient information systems and a lack of necessary skills make it challenging for critical actors to perform the tasks effectively. The interaction effects of these challenges weaken organ supply chains and make it less efficient, giving rise to negative externalities such as black markets for donated organs and organ tourism/trafficking.

This paper establishes a solid foundation for a critical topic that could significantly impact human health and life once the government or non-profit ecosystem matures. The MICMAC analysis in this paper provides a methodological approach for future studies wishing to further develop the organ supply chain structural models.

The study provides valuable insights for experts and policymakers on where to prioritise efforts in designing interventions to strengthen organ transplantation supply chains in developing countries.

This study is one of the first to empirically examine the challenges of organ transplant supply chains from an SSA perspective, including theoretically grounded explanations from data collected in two developing countries.

This paper aims at examining how different types of shop floor performance feedback affect employee motivation and engagement. Based on this dataset, the authors analyzed the relationship between five types of performance feedback (safety, quality, productivity, improvement and individual performance) and three types of human-related outputs (motivation, role clarity and engagement) in light of socio-technical systems theory.

The authors designed a survey instrument and collected data from 492 employees of a large beverage distribution company. The authors used robust construct validity tests and multiple regression analysis to test the hypotheses.

The findings indicated significant positive effects of feedback on overall production, improvements and individual performance on all human-related outputs. In turn, safety and quality performance feedback had no statistically significant impact in motivation or engagement. These findings highlight the mixed nature of the impact of performance feedback on human-related outputs that have been neglected in the literature.

The joint analysis of the social and technical portion of performance feedback is unusual despite its clear relevance, which characterizes an original contribution of the authors’ work. Although previous literature supports the effect of performance feedback on motivation and engagement, previous studies have not tested how different types of feedback affect those social constructs.

Organizational change is a key mechanism to ensure the sustainability of healthcare systems. However, healthcare organizations are persistently difficult to change, and literature is riddled with examples of failed change endeavors. In this chapter, we attempt to unravel the underlying causes for failed organizational change. We distinguish three types of change with different levels of depth that require different change approaches. Transformations are the deepest forms of change where beliefs and principles need to be modified to successfully influence routines. Renewals are deep forms of change where principles need to be modified to successfully influence routines. Improvements are shallow forms of change where only modifications at the level of routines are needed. Using deoxyribonucleic acid (DNA) as our metaphor, we propose a theory of “organizational DNA” to understand organizations and these three types of organizational changes. We posit that organizations are made up of a double helix consisting of a so-called “social string,” which contains the “soft” interaction or communication among the organization's members, and a so-called “technical string,” which contains “hard” organizational aspects such as structure and technology. Ladders of organizational nucleotides (i.e., Routines, Principles, and Beliefs) connect this double helix in various combinations. Together, the double helix and accompanying nucleotides make up the DNA of an organization. Without knowledge of the architecture of organizational DNA and whether a change addresses beliefs, principles, and/or routines, we believe that organizational change is constrained and based on luck rather than change management expertise. Following this metaphor, we show that organizational change fails when it attempts to change one part of the DNA (e.g., routines) in a way that renders it incompatible with the connecting components (e.g., principles and beliefs). We discuss how the theory can be applied in practice using an exemplar case.