Supply chain clusters achieve viability by exploiting geographical concentration to gain efficiencies in matching supply and demand. Although the benefits of operating in these environments are understood, little is known about how supply chain clusters form and adapt. From a systems theory perspective, causal events lead to this formation and adaptation and facilitate sustained operation in modern business settings. This paper extends this understanding by theorizing key antecedents responsible for supply chain cluster formation and adaptation, conceptualizing individual firms as agents. Through a characterization of agents as either passive or active, we construct propositions and a conceptual model. The modelling, supported by causal loop diagrams, details the interactions between agents that lead to cluster formation and adaptations to contextual changes in supply chain clusters. These findings can guide policy design that is more effective in enabling the emergence and sustained existence of supply chain clusters through facilitated causal interactions.

Open systems possess a dilemma whereby they both depend upon-and are subject to disruptions arising from-their exogenous environments. Organisations display this dilemma through the need to both manage the inbound flow of supply from upstream nodes and outputs towards downstream customers, whilst also managing issues arising from interactions with these other systems. At the centre of this dilemma sits individual agents, whether they be managers of organisations, business-level functions or customer-facing roles. This paper explores how individuals perceive and react towards disruptions and the wider, systemic implications of their actions. This article uses interviews with individuals based on a theoretical framework incorporating general systems theory (GST) and a series of decision theories (namely Protection Motivation Theory) to highlight the commonalities across various actions and strategies that individuals can undertake to address disruptions.

Purpose The paper explores and characterizes antifragility in simple inventory systems exposed to demand variability, providing the initial inroads to operationalizing antifragility in the context of inventory management. Antifragility refers to the feature of a system that can benefit from uncertainty, rather than suffer from it. The paper expands the concept of inventory beyond that of risk mitigation and towards one of enabling antifragility. Design/methodology/approach The study employs analytical and simulation modelling of an inventory system with two classes of demand. To separate the influence of factors, a simple inventory policy with a range of fixed order quantities is modelled, allowing for the identification of antifragile regions in an experimental space. Findings Outputs uncover a variety of performance outcomes, ranging from settings where additional inventory yields no benefit, to areas where additional inventory results in increasing normalized profit with increasing uncertainty, demonstrating antifragility. In between these regions, increases in normalized profit are bounded, and confined to specific regions. Research limitations/implications This research expands academic understanding of inventory as a vehicle to achieving antifragile outcomes. Although this paper does not solve for an optimal policy as typical inventory research does, it instead characterizes the antifragile outcomes within simple inventory systems. Further research should be carried out to investigate antifragility in settings of greater complexity and design ordering policies leveraging inventory towards a gain from uncertainty. Practical implications Typically, inventory is used to buffer against uncertainty, and, with a given amount of inventory, the performance is expected to degrade with increasing variability. In this paper, the authors demonstrate that certain levels of additional inventory can result in antifragility and increase profitability as uncertainty increases, extending beyond traditional conceptualizations of inventory and uncertainty. Originality/value Empirical research into designing antifragile outcomes is limited, with very few examples of increasing performance with increases in uncertainty. This article presents an initial empirical exploration of how additional inventory can result in antifragility.

Hillmann and Guenther provide an extensive review of research into organizational resilience in which they examine the different conceptualisations of the concept and their associated measurement scales. Their article emphasises stability, rather than other domains such as growth, as core to organizational resilience. We argue that this emphasis does not acknowledge the overlap between resilience and associated but distinctly different concepts like robustness and antifragility as observable phenomena in organizational responses to adversity. To extend Hillmann and Guenther's work, we therefore conceptually contrast resilience with robustness and antifragility so that future research might craft a more nuanced understanding of the presence of all three concepts in management research, which is currently dominated by resilience.

Purpose To explore the value and the case for designing antifragile socio-technical information systems (IS) in an era of big data, moving beyond traditional notions of IS design towards systems that can leverage uncertainty for gains. Design/methodology/approach A design science research (DSR) approach was adopted, comprising four stages, including problem identification and solutions definition, conceptual artifact or socio-technical system design, preliminary evaluation, and communication and knowledge capture. Findings A conceptual socio-technical artifact that identifies antecedents to antifragile IS design. When operationalised, the antecedents may produce the desired antifragile outcome. The antecedents are categorised as value propositions, design decisions and system capabilities. Research limitations/implications This research is conceptual in nature, applied and evaluated in a single big data analytics case study in Facebook-Cambridge Analytica. Future research should empirically validate across a range of real-world big data contexts, beyond the presented case study. Practical implications Uncertainty generally results in socio-technical system failures, impacting individuals, organisations and communities. Conversely, antifragile IS can respond favourably to the shocks and stressors brought forth by periods of elevated uncertainty. Social implications Antifragile IS can drive socio-technical systems to respond favourably to uncertainty and stressors. Typically, these socio-technical systems are large, complex structures, with increased connectivity and the requirement to generate, process, analyse and use large datasets. When these systems fail, it affects individuals, organisations and communities. Originality/value Existing IS design methodologies and frameworks largely ignore antifragility as a possible designable outcome. Extant research is limited to abstract architectural design, and approaches based on the proposition of principles. This research contributes to knowledge of antifragile IS design, by deriving a conceptual artifact or socio-technical system based on antecedent-outcome relationships that leverage uncertainty towards performance gains.

Economic shifts, disruptive innovations, and competitive rivalries continuously reshape the operating environment of organisations. Such uncertainty impacts organisations and raises significant challenges. While many organisations tend to respond to uncertainty by adopting loss minimisation strategies, others see uncertainty as an opportunity to achieve gains. The latter view is exemplified in Taleb’s (2012) concept of ‘antifragility’, a property of systems that gain when exposed to uncertainty. For organisations, the challenge lies in the identification and execution of fundamental artefacts to accomplish work to achieve antifragile outcomes. One such artefact is the organisational routine; repeatable, regular patterns of behaviour and actions that influence performance. This paper conceptualises the intersection between antifragility, uncertainty management, and organisational routines literatures to identify four routine archetypes that can guide actions that contribute to organisational antifragility. Theoretically, this paper identifies how these archetypes arise from the interplay between temporal action (as tendencies towards proactive or reactive action) and risk mitigation strategies (as preference towards redundancies or flexibilities). Developed insights bring forth a foundation for predictive models of performance, and guidance for organisations aiming to thrive, rather than just survive, in uncertain environments. This paper concludes with the identification of further research avenues.

As disruptions and uncertainty have become emergent themes within the supply chain literature, attention has shifted towards the choices enacted to combat the negative effects of disruptions. Extant research highlights the importance of system states - namely robustness and resilience - towards mitigating the probability and magnitude of disruptions, however, there appears to be a lack of conceptual guidance surrounding two popular strategies - redundancy and flexibility - that have been proposed to mitigate the impact of disruptions. To address this issue, we review and investigate contextual factors from empirical research that may offer insight into conceptual strategies that can be utilised to improve post-disruption system performance utilising a methodology based upon an abductive approach to conceptual theory building. In this paper published research on resilience and robustness in supply chains is considered, alongside research in other areas such as risk, systems theory, and ecology. Inference drawn from empirical research is often highly contextualised, but collectively highlights unique generalisable factors. Our research proposes four broad conceptual strategies - insurance, expediting, strategic adaptive capability and reconfiguration - that each uniquely serve to reduce the probability and magnitude of supply chain disruptions. Our research concludes that the difference in utility between redundancy and flexibility as means to enhance resilience and robustness is influenced by interactions between the supply chain, the disruption characteristics (inclusive of speed of onset and time horizon) and the decision maker.

Urban dwellers are increasingly dependent on technological systems to supply goods and services essential for their way of life. Such dependence incurs vulnerability in situations when these goods and services are not available. The systems supplying these essentials are known to have many loci of failure. We consider the exposure of a technological system in terms of the number and type of loci of failure and present this as a metric of urban dweller vulnerability. Such a metric captures the vulnerability to the nonavailability of the specific goods or services provided by a particular technological system. By selecting from goods and services commonly required, this article identifies a representative range of essential goods and services provided to urban-dwelling individuals by technological systems, and examines the nature and extent of exposure associated with each system. Based upon the descriptions of these technological systems, this article classifies the contributors to each technological system's exposure into a small number of categories. The analysis allows the inference of generalized approaches for reducing each category of exposure, and hence vulnerability. Thus, a theory of exposure can be used to inform engineering management approaches applicable to end-user vulnerability reduction, and to identify the feasibility of less exposed technological systems.

Microgrids are interconnected distributed energy generation and storage systems that can act as either an extension of the existing grid or operate independently of the grid, in so-called "island mode" [1]. As a power supply technology, microgrids are attractive as they can be more reliable than existing infrastructure and reduce societies? reliance on nonrenewable energy sources [1]. As a valid alternative to reliance on centralized electricity generation and grid distribution, microgrids have value that materializes when disasters render grids inoperable and microgrids remain as islands of service. It is these specific disaster conditions that reveal the latent value of microgrids as disaster management tools, whereas under nondisaster conditions, their value remains hidden. Microgrids can thus be leveraged to manage disaster relief efforts while remaining connected to donor sources and the broader humanitarian relief supply chain.

Experienced system dynamicists commonly conceptualise causal relationships and feedback loops using Causal Loop Diagrams (CLDs). In adhering to best practice, multiple data collection activities may be required (e.g. multiple group model building sessions), resulting in multiple CLDs. To achieve covariation that correctly attributes cause and effect from multiple data sets, aggregation of CLDs may be necessary. Such aggregation must adequately account for attribution variations across constructed CLDs to produce a coherent view of a phenomenon of interest in a 'complete' model. Discourse concerning model completeness should account for the potential for method bias. The data collection method chosen for CLD development will influence the ability to create a model that is fit for the purposes of the study and influence the likelihood of achieving model completeness. So too does the method chosen for model aggregation. Little processual guidance exists on a method for data aggregation in system dynamics studies. This paper examines three data aggregation approaches, based on existing qualitative analysis methods, to determine the suitability of each method. The approaches considered include triangulation, includes all data in the aggregation process; grounded theory, bases aggregation on frequency of occurrence; and synthesis, extends aggregation to include variables based on magnitude of occurrence. Comments are made regarding the relevance of each method for different study types, with final remarks reiterating the consideration of equifinality and multifinality in research and their impact on method selection. This paper enhances the rigour of research aiming at facilitating greater success in studies utilising CLDs.

Purpose The purpose of this paper is to identify key personal and organisational resources that influence the engagement, well-being and job satisfaction of healthcare professionals working in Australia. Design/methodology/approach Using the job demands–resources model, this study investigates how employee resources and organisation resources influence engagement, well-being and job satisfaction of health professionals in Australian hospitals. The authors collected survey data from a sample of healthcare professionals (n=217) working in three hospitals in New South Wales, Australia. Findings The results confirm the importance of the emotional health of employees on their well-being. The results concur with existing research that employees with higher levels of emotional health have more positive emotional and social interactions, and thus exhibit higher levels of well-being at work. The study also uncovers certain aspects of emotional health that can influence a range of employee outcomes. Practical implications The findings link human resource management practices to unique motivators of healthcare professionals which, in turn, are likely to improve engagement, well-being and job satisfaction. Originality/value The study highlights specific resources that support greater levels of well-being, engagement and job satisfaction in Australian hospitals.

Online testing is a popular practice for tertiary educators, largely owing to efficiency in automation, scalability, and capability to add depth and breadth to subject offerings. As with all assessments, designs need to consider whether student cheating may be inadvertently made easier and more difficult to detect. Cheating can jeopardise the validity of inference drawn from the measurements produced by online tests, leading to inaccurate signals and misperceptions about what students know and can do. This paper extends theoretical understanding about cheating behaviours to link online test design choices and their influence on a student’s capability and willingness to cheat. This research reviews the literature on cheating theories and a typology construction methodology to relate common online test design choices to their cheating threat consequence. In doing so, the typology offers educators designing online tests general normative guidance aimed at reducing threats to assessment inference validity, and academic integrity in general, brought about by student cheating. While we admit that cheating cannot be completely eliminated in online testing, the guidance provided by the typology can assist educators to structure online tests to minimise cheating.

This paper explores the design process of robotics and autonomous systems using a co-design approach, applied ethics, and values-driven methods. Specifically, the approach seeks to move beyond traditional risk assessment toward a greater consideration of end-user exposure. The goal of the ethics-based co-design approach is to identify end-user and stakeholder values that guide the minimization of end-user vulnerability associated with the employment of autonomous systems. This design process is also used to identify positive consequences that probably increase human well-being as opposed to simply avoiding harm. We argue that biomedical autonomous systems design, during the preclinical phase, should bring together diverse stakeholders that would not traditionally be involved in design. We also argue that embedding ethical considerations in the engineering design process should bring together a diverse range of stakeholders to more accurately appreciate possible end-user implications of a design. With complex systems design, such as biotechnologies, greater awareness is necessary of the ethical implications of designed autonomy to end-user exposure.

Open systems operating close to maximum capacity are prone to work accumulations when either resources become fatigued and unexpected demand surges occur. As such, decision makers are required to make strategic choices about adjustments to available resources to avoid drift towards entropy and failure. Such work accumulations can continually increase while resource utilisation is maximized, popular risk strategies be applied to assist resilience or robustness. Decision-makers often make choices to implement either flexibility or redundancy strategies, yet little guidance exists as to the relative utilities of each strategy. Adopting a system dynamics (SD) methodology, and a discrete event simulation, this paper evaluates the utility difference between two strategies within a queueing system subject to unexpected demand surges while operating at near maximum capacity. Utilising worker fatigue (as a function of utilisation) as a feedback mechanism influencing capacity, the resultant model suggests non-linearity caused by fatigue influences the utility of redundancy and strategy. The utility is further influenced by the degree of slack built into the system to cope with demand surges. The research offers unique interpretations of the redundancy-flexibility debate and demonstrates the value of a combined SD and discrete event simulation methodology towards determining differences in strategy utilities.

PurposeThe purpose of this paper is to construct a typology of a disaster that informs humanitarian-relief supply chain (HRSC) design across the stages of disaster relief.Design/methodology/approachIn addition to an interdisciplinary review of pertinent literature, this paper utilises a typology construction method to propose theoretically and methodologically sound dimensions of disasters.FindingsWhilst semantic arguments surrounding the concept of a “disaster” are ongoing, the authors propose three typologies based upon six dimensions that serve as interdependent variables informing resultant HRSC design considerations. These are speed of onset, time horizon, spatial considerations, affected population needs, perceived probability of occurrence and perceived magnitude of consequence. These combinational and independent relationships of the variables offer insight into key HRSC design-making considerations.Research limitations/implicationsThe study improves conceptual knowledge of disasters, distilling the concept to only the dimensions applicable to HRSC design, omitting other applications. The typologies provide empirical cell types based on extant literature, but do not apply the models towards new or future phenomena.Practical implicationsThis paper provides HRSC practitioners with normative guidance through a more targeted approach to disaster relief, with a focus on the impacted system and resulting interactions’ correspondence to HRSC design.Originality/valueThis paper provides three typological models of disasters uniquely constructed for HRSC design across the various stages of disaster relief.

In many rail networks, infrastructure constraints force the shared usage of lines between passenger and freight movements. Scheduling additional freight movements around existing passenger services and peak traffic based curfews presents significant challenges to commodity industries eager to increase export volumes. This paper addresses the problem of inserting additional freight movements in a constrained railway network. To this end, a railway operations planning model was developed to simulate and insert feasible rail movements in a non-periodic timetable. The simulation modelling platform developed in this paper is called RailNet, which simulates the existing railway network constraints and is capable of adding freight paths for planning and scheduling. The timetable for passenger trains is kept unchanged. The paper also reports a real case study in which RailNet was used to quantify the capacity of the track network at the Port Kembla Coal Terminal in New South Wales, Australia under different scenarios of infrastructure upgrades.

People are reliant on technology systems for their survival and everyday convenience. From access to clean drinking water, to electricity for cooking, to fuel for driving vehicles to and from work. When a technology system that people rely on is inoperable or inaccessible, end-user vulnerabilities can increase acutely and substantially. While end-users are quite resourceful, a few days without water or electricity or fuel, can quickly turn into a humanitarian or security crisis, especially in densely populated areas. This paper's contribution is a measure to study technology systems and the extent of their contribution to end-user vulnerability. A theory of exposure is presented with a corresponding measure of how to determine exposure of any given technology system, agnostic of geography location or socio-economic circumstances. It is argued, that if the exposure of any given technology system can be reduced, that end-user vulnerabilities are also reduced, providing some control over extreme or unintended events. Researchers and practitioners can use these outcomes on existing technology systems toward optimization, or on new technology systems being introduced into a cyberphysical environment. The accuracy, precision and scaling of the proposed exposure measure are also examined in this paper.

There is a strong impetus to commercialize emerging technology, tempered by safety expectations and regulatory compliance requirements [1]. Such is the case for medical implant devices, where successful operation of devices can be life-saving, but while consequences of failure are severe. Recent advances in this technology aimed at enabling remote access to a device facilitate remote and more accurate monitoring of patient health. In doing so, original equipment manufacturers (OEMs) both satisfy market pressures and potentially introduce new avenues of risk that increase end-user vulnerability [2]. Vulnerability contributed by technological systems is known to researchers [3]-[5] as an important consideration in individual vulnerability. This study aims to quantify the contribution of technological configuration to end-user vulnerability, specifically the additional risk of Internet-enabled medical implant devices.

Urban dwellers are increasingly vulnerable to failures of technological systems that supply them with goods and services. Extant techniques for the analysis of those technological systems, although valuable, do not adequately quantify particular vulnerabilities. This study explores the significance of weaknesses within technological systems and proposes a metric of “exposure”, which is shown to represent the vulnerability contributed by the technological system to the end-user. The measure thus contributes to the theory and practice of vulnerability reduction. The results suggest specific and general conclusions.