Sociotechnical Resilience: A Preliminary Concept

This article presents the concept of sociotechnical resilience by employing an interdisciplinary perspective derived from the fields of science and technology studies, human factors, safety science, organizational studies, and systems engineering. Highlighting the hybrid nature of sociotechnical systems, we identify three main constituents that characterize sociotechnical resilience: informational relations, sociomaterial structures, and anticipatory practices. Further, we frame sociotechnical resilience as undergirded by the notion of transformability with an emphasis on intentional activities, focusing on the ability of sociotechnical systems to shift from one form to another in the aftermath of shock and disturbance. We propose that the triad of relations, structures, and practices are fundamental aspects required to comprehend the resilience of sociotechnical systems during times of crisis.     

Assessing Engineering Resilience for Systems with Multiple Performance Measures

Recently, efforts to model and assess a system's resilience to disruptions due to environmental and adversarial threats have increased substantially. Researchers have investigated resilience in many disciplines, including sociology, psychology, computer networks, and engineering systems, to name a few. When assessing engineering system resilience, the resilience assessment typically considers a single performance measure, a disruption, a loss of performance, the time required to recover, or a combination of these elements. We define and use a resilient engineered system definition that separates system resilience into platform and mission resilience. Most complex systems have multiple performance measures; this research proposes using multiple objective decision analysis to assess system resilience for systems with multiple performance measures using two distinct methods. The first method quantifies platform resilience and includes resilience and other “ilities” directly in the value hierarchy, while the second method quantifies mission resilience and uses the “ilities” in the calculation of the expected mission performance for every performance measure in the value hierarchy. We illustrate the mission resilience method using a transportation systems‐of‐systems network with varying levels of resilience due to the level of connectivity and autonomy of the vehicles and platform resilience by using a notional military example. Our analysis found that it is necessary to quantify performance in context with specific mission(s) and scenario(s) under specific threat(s) and then use modeling and simulation to help determine the resilience of a system for a given set of conditions. The example demonstrates how incorporating system mission resilience can improve performance for some performance measures while negatively affecting others.     

Selecting Indicators for Assessing Community Sustainable Resilience

Communities are complex systems subject to a variety of hazards that can result in significant disruption to critical functions. Community resilience assessment is rapidly gaining popularity as a means to help communities better prepare for, respond to, and recover from disruption. Sustainable resilience, a recently developed concept, requires communities to assess system‐wide capability to maintain desired performance levels while simultaneously evaluating impacts to resilience due to changes in hazards and vulnerability over extended periods of time. To enable assessment of community sustainable resilience, we review current literature, consolidate available indicators and metrics, and develop a classification scheme and organizational structure to aid in identification, selection, and application of indicators within a dynamic assessment framework. A nonduplicative set of community sustainable resilience indicators and metrics is provided that can be tailored to a community's needs, thereby enhancing the ability to operationalize the assessment process.     

Human resource development (HRD) resilience: a new ‘success element’ of organizational resilience?

ABSTRACT

Endlessly changing business and economic landscapes urge organizations to become resilient to ensure business survival and growth. Yet, in many cases, business world is becoming turbulent faster than organizations are becoming resilient. Relevant research indicates the ways through which organizations could respond to unforeseen events, mainly through suggesting that individual and group resilience could lead to an organizational one. However, research is nascent on how particularly human resource development (HRD) resilience could be built, and thus to contribute to organizational resilience as well. Within today’s business uncertainty and complexity, HRD resilience comes in line with the developmental strategies of organizations. Therefore, the purpose of this perspective article is to set the foundations of the term (HRD resilience) in order to initiate a dialogue around its ability to make a substantial contribution to organizational practice, and thus to be seen as a new ‘success element’ of organizational resilience.     

Strategic and operational management of organizational resilience: Current state of research and future directions

This article uses both a systematic literature search and co-citation analysis to investigate the specific research domains of organizational resilience and its strategic and operational management to understand the current state of development and future research directions.The research stream on the organizational and operational management of resilience is distant from its infancy, but it can still be considered to be in a developing phase. We found evidence that the academic literature has reached a shared consensus on the definition of resilience, foundations, and characteristics and that in recent years, the main subfield of research has been supply chain resilience. Nevertheless, the literature is still far from reaching consensus on the implementation of resilience, i.e., how to reach operational resilience and how to create and maintain resilient processes. Finally, based on the results of in-depth co-citation and literature analysis, we found seven fruitful future research directions on strategic, organizational and operational resilience.     

Integrating Risk and Resilience Approaches to Catastrophe Management in Engineering Systems

Recent natural and man‐made catastrophes, such as the Fukushima nuclear power plant, flooding caused by Hurricane Katrina, the Deepwater Horizon oil spill, the Haiti earthquake, and the mortgage derivatives crisis, have renewed interest in the concept of resilience, especially as it relates to complex systems vulnerable to multiple or cascading failures. Although the meaning of resilience is contested in different contexts, in general resilience is understood to mean the capacity to adapt to changing conditions without catastrophic loss of form or function. In the context of engineering systems, this has sometimes been interpreted as the probability that system conditions might exceed an irrevocable tipping point. However, we argue that this approach improperly conflates resilience and risk perspectives by expressing resilience exclusively in risk terms. In contrast, we describe resilience as an emergent property of what an engineering system does, rather than a static property the system has. Therefore, resilience cannot be measured at the systems scale solely from examination of component parts. Instead, resilience is better understood as the outcome of a recursive process that includes: sensing, anticipation, learning, and adaptation. In this approach, resilience analysis can be understood as differentiable from, but complementary to, risk analysis, with important implications for the adaptive management of complex, coupled engineering systems. Management of the 2011 flooding in the Mississippi River Basin is discussed as an example of the successes and challenges of resilience‐based management of complex natural systems that have been extensively altered by engineered structures.     

Decision‐Making Analytics Using Plural Resilience Parameters for Adaptive Management of Complex Systems

It is critical for complex systems to effectively recover, adapt, and reorganize after system disruptions. Common approaches for evaluating system resilience typically study single measures of performance at one time, such as with a single resilience curve. However, multiple measures of performance are needed for complex systems that involve many components, functions, and noncommensurate valuations of performance. Hence, this article presents a framework for: (1) modeling resilience for complex systems with competing measures of performance, and (2) modeling decision making for investing in these systems using multiple stakeholder perspectives and multicriteria decision analysis. This resilience framework, which is described and demonstrated in this article via a real‐world case study, will be of interest to managers of complex systems, such as supply chains and large‐scale infrastructure networks.     

Evolutionary Resilience and Strategies for Climate Adaptation

The aim of this study is to develop a framework by drawing on three broad perspectives on resilience, engineering, ecological and evolutionary, and to use this framework to critically examine the approach adopted by the draft London climate change adaptation strategy. The central argument of the study is that the Strategy's emergency planning-centred approach to climate adaptation veers between a standard ecological understanding of resilience and the more rigid engineering model. Its emphasis is on identifying ‘exposure’ and ‘vulnerability’ to risk from climate events and on bouncing back from the consequences of such exposures to a normal state, rather than on the dynamic process of transformation to a more desirable trajectory. The study concludes that fostering resilience involves planning for not only recovery from shocks but also cultivating preparedness, and seeking potential transformative opportunities which emerge from change.     

Rethinking Resilience Analytics

The concept of “resilience analytics” has recently been proposed as a means to leverage the promise of big data to improve the resilience of interdependent critical infrastructure systems and the communities supported by them. Given recent advances in machine learning and other data‐driven analytic techniques, as well as the prevalence of high‐profile natural and man‐made disasters, the temptation to pursue resilience analytics without question is almost overwhelming. Indeed, we find big data analytics capable to support resilience to rare, situational surprises captured in analytic models. Nonetheless, this article examines the efficacy of resilience analytics by answering a single motivating question: Can big data analytics help cyber–physical–social (CPS) systems adapt to surprise? This article explains the limitations of resilience analytics when critical infrastructure systems are challenged by fundamental surprises never conceived during model development. In these cases, adoption of resilience analytics may prove either useless for decision support or harmful by increasing dangers during unprecedented events. We demonstrate that these dangers are not limited to a single CPS context by highlighting the limits of analytic models during hurricanes, dam failures, blackouts, and stock market crashes. We conclude that resilience analytics alone are not able to adapt to the very events that motivate their use and may, ironically, make CPS systems more vulnerable. We present avenues for future research to address this deficiency, with emphasis on improvisation to adapt CPS systems to fundamental surprise.     

Inherent Costs and Interdependent Impacts of Infrastructure Network Resilience

Recent studies in system resilience have proposed metrics to understand the ability of systems to recover from a disruptive event, often offering a qualitative treatment of resilience. This work provides a quantitative treatment of resilience and focuses specifically on measuring resilience in infrastructure networks. Inherent cost metrics are introduced: loss of service cost and total network restoration cost. Further, “costs” of network resilience are often shared across multiple infrastructures and industries that rely upon those networks, particularly when such networks become inoperable in the face of disruptive events. As such, this work integrates the quantitative resilience approach with a model describing the regional, multi‐industry impacts of a disruptive event to measure the interdependent impacts of network resilience. The approaches discussed in this article are deployed in a case study of an inland waterway transportation network, the Mississippi River Navigation System.     

Probabilistic Multiple Hazard Resilience Model of an Interdependent Infrastructure System

Multiple hazard resilience is of significant practical value because most regions of the world are subject to multiple natural and technological hazards. An analysis and assessment approach for multiple hazard spatiotemporal resilience of interdependent infrastructure systems is developed using network theory and a numerical analysis. First, we define multiple hazard resilience and present a quantitative probabilistic metric based on the expansion of a single hazard deterministic resilience model. Second, we define a multiple hazard relationship analysis model with a focus on the impact of hazards on an infrastructure. Subsequently, a relationship matrix is constructed with temporal and spatial dimensions. Further, a general method for the evaluation of direct impacts on an individual infrastructure under multiple hazards is proposed. Third, we present an analysis of indirect multiple hazard impacts on interdependent infrastructures and a joint restoration model of an infrastructure system. Finally, a simplified two‐layer interdependent infrastructure network is used as a case study for illustrating the proposed methodology. The results show that temporal and spatial relationships of multiple hazards significantly influence system resilience. Moreover, the interdependence among infrastructures further magnifies the impact on resilience value. The main contribution of the article is a new multiple hazard resilience evaluation approach that is capable of integrating the impacts of multiple hazard interactions, interdependence of network components (layers), and restoration strategy.     

Workforce/Population,Economy, Infrastructure,Geography, Hierarchy,and Time (WEIGHT): Reflections on the Plural Dimensions of Disaster Resilience

The concept of resilience and its relevance to disaster risk management has increasingly gained attention in recent years. It is common for risk and resilience studies to model system recovery by analyzing a single or aggregated measure of performance, such as economic output or system functionality. However, the history of past disasters and recent risk literature suggest that a single-dimension view of relevant systems is not only insufficient, but can compromise the ability to manage risk for these systems. In this article, we explore how multiple dimensions influence the ability for complex systems to function and effectively recover after a disaster. In particular, we compile evidence from the many competing resilience perspectives to identify the most critical resilience dimensions across several academic disciplines, applications, and disaster events. The findings demonstrate the need for a conceptual framework that decomposes resilience into six primary dimensions: workforce/population, economy, infrastructure, geography, hierarchy, and time (WEIGHT). These dimensions are not typically addressed holistically in the literature; often they are either modeled independently or in piecemeal combinations. The current research is the first to provide a comprehensive discussion of each resilience dimension and discuss how these dimensions can be integrated into a cohesive framework, suggesting that no single dimension is sufficient for a holistic analysis of a disaster risk management. Through this article, we also aim to spark discussions among researchers and policymakers to develop a multicriteria decision framework for evaluating the efficacy of resilience strategies. Furthermore, the WEIGHT dimensions may also be used to motivate the generation of new approaches for data analytics of resilience-related knowledge bases.     

Does governance confer organisational resilience? Evidence from UK employee owned businesses

Current economic crisis has highlighted the importance of an organization’s ability to withstand economic shocks. This has rekindled interest in organization resilience on the one hand, and the relationship between alternative governance forms such as employee owned businesses (EOBs) on the other. We explore this relationship using performance data on 204 publicly traded non-employee owned businesses and 49 EOBs prior to the economic downturn (2004–2008), and during the economic downturn (2008–2009). This data is complemented with a survey of resilience related governance and organizational practices in 41 EOBs and 22 non-EOBs. Our results show that: (a) employee ownership that is combined with employee involvement in firm governance is associated with greater stability in business performance over a business cycle; (b) EOBs have longer investment payback horizon when compared to non-EOBs across a number of activities; (c) Top management in EOBs are more likely to seek employee input in strategic decision making; (d) EOBs are more likely to use employee involvement to achieve tighter coupling between feedback from operations and the setting of strategic direction for the firm. These results suggest that employee stock ownership programs alone are not sufficient to develop higher levels of organizational resilience. Managers must combine employee stock ownership with employee involvement in governance if they wish to build up resilience in advance of adverse economic conditions.     

Resilience of Cyber Systems with Over‐ and Underregulation

Recent cyber attacks provide evidence of increased threats to our critical systems and infrastructure. A common reaction to a new threat is to harden the system by adding new rules and regulations. As federal and state governments request new procedures to follow, each of their organizations implements their own cyber defense strategies. This unintentionally increases time and effort that employees spend on training and policy implementation and decreases the time and latitude to perform critical job functions, thus raising overall levels of stress. People's performance under stress, coupled with an overabundance of information, results in even more vulnerabilities for adversaries to exploit. In this article, we embed a simple regulatory model that accounts for cybersecurity human factors and an organization's regulatory environment in a model of a corporate cyber network under attack. The resulting model demonstrates the effect of under‐ and overregulation on an organization's resilience with respect to insider threats. Currently, there is a tendency to use ad‐hoc approaches to account for human factors rather than to incorporate them into cyber resilience modeling. It is clear that using a systematic approach utilizing behavioral science, which already exists in cyber resilience assessment, would provide a more holistic view for decisionmakers.     

Sustainability and Business in a Complex World

Sustainability is a topic of growing importance today in all aspects of organizational life. Businesses and managers are increasingly considering ways to incorporate a balance among economic, ecological, social, and cultural value creation into their business models. At the same time, the world is becoming exponentially more complex. Indeed, complexity theory and thinking are now apparent in academic and practice accounts of sustainability in business, as scholars and practitioners recognize the limitations of traditional reductionist approaches to systemic problems. To date, however, a more theoretical framing of sustainability lags behind accumulating practical evidence. The purpose of this article is to address this gap by developing a complexity‐based framework for understanding and managing sustainability in complex adaptive systems. We aim for simplicity, wholeness, and practicality in our approach, taking a qualitative rather than quantitative perspective on complex systems. Using several contemporary case examples, the article describes the important qualities of complex systems and develops them into a working framework that integrates principles and parameters of sustainability. In doing so, we create an approach to sustainability issues and dilemmas called “sustainability thinking.” The article concludes with more generalized sustainability action strategies for managers and recommendations for future researchers.     

The Need to Reconcile Concepts that Characterize Systems Facing Threats

Desirable system performance in the face of threats has been characterized by various management concepts. Through semistructured interviews with editors of journals in the fields of emergency response and systems management, a literature review, and professional judgment, we identified nine related and often interchangeably used system performance concepts: adaptability, agility, reliability, resilience, resistance, robustness, safety, security, and sustainability. A better understanding of these concepts will allow system planners to pursue management strategies best suited to their unique system dynamics and specific objectives of good performance. We analyze expert responses and review the linguistic definitions and mathematical framing of these concepts to understand their applications. We find a lack of consensus on their usage between interview subjects, but by using the mathematical framing to enrich the linguistic definitions, we formulate comparative visualizations and propose distinct definitions for the nine concepts. We present a conceptual framing to relate the concepts for management purposes.     

Design and Assessment Methodology for System Resilience Metrics

By providing objective measures, resilience metrics (RMs) help planners, designers, and decisionmakers to have a grasp of the resilience status of a system. Conceptual frameworks establish a sound basis for RM development. However, a significant challenge that has yet to be addressed is the assessment of the validity of RMs, whether they reflect all abilities of a resilient system, and whether or not they overrate/underrate these abilities. This article covers this gap by introducing a methodology that can show the validity of an RM against its conceptual framework. This methodology combines experimental design methods and statistical analysis techniques that provide an insight into the RM's quality. We also propose a new metric that can be used for general systems. The analysis of the proposed metric using the presented methodology shows that this metric is a better indicator of the system's abilities compared to the existing metrics.     

A Factor Analysis Approach Toward Reconciling Community Vulnerability and Resilience Indices for Natural Hazards

The concepts of vulnerability and resilience help explain why natural hazards of similar type and magnitude can have disparate impacts on varying communities. Numerous frameworks have been developed to measure these concepts, but a clear and consistent method of comparing them is lacking. Here, we develop a data-driven approach for reconciling a popular class of frameworks known as vulnerability and resilience indices. In particular, we conduct an exploratory factor analysis on a comprehensive set of variables from established indices measuring community vulnerability and resilience at the U.S. county level. The resulting factor model suggests that 50 of the 130 analyzed variables effectively load onto five dimensions: wealth, poverty, agencies per capita, elderly populations, and non–English-speaking populations. Additionally, the factor structure establishes an objective and intuitive schema for relating the constituent elements of vulnerability and resilience indices, in turn affording researchers a flexible yet robust baseline for validating and expanding upon current approaches.     

基于线性分段恢复函数的基础设施韧性分析模型——以C县电力系统网络为例

针对近年来一系列突发事件冲击和破坏着城市关键基础设施系统的正常运行,并造成了较为严重的社会后果的现实问题,提出了如何保护关键基础设施系统的研究问题,以使基础设施系统能够对灾害情景做出迅速的响应,并迅速地处理以恢复到常态。本研究基于三种典型的恢复函数提出了线性分段恢复函数,构建了关键基础设施系统韧性分析模型,并用蒙特卡洛模拟的方法应用到C县的电力系统网络加以验证,得到了该韧性分析模型不仅可以帮助决策者在灾害情境下权衡预算成本和韧性的关系,也可以识别关键基础设施系统网络中需要保护的关键节点,从而实现对关键基础设施系统的针对性保护的结论。本研究构建的韧性分析模型有为灾害情境下对电力系统采取针对性保护的现实价值,和开拓了对基础设施系统进行保护研究的分析模型的理论价值。     

Sequential Hazards Resilience of Interdependent Infrastructure System: A Case Study of Greater Toronto Area Energy Infrastructure System

Coupled infrastructure systems and complicated multihazards result in a high level of complexity and make it difficult to assess and improve the infrastructure system resilience. With a case study of the Greater Toronto Area energy system (including electric, gas, and oil transmission networks), an approach to analysis of multihazard resilience of an interdependent infrastructure system is presented in the article. Integrating network theory, spatial and numerical analysis methods, the new approach deals with the complicated multihazard relations and complex infrastructure interdependencies as spatiotemporal impacts on infrastructure systems in order to assess the dynamic system resilience. The results confirm that the effects of sequential hazards on resilience of infrastructure (network) are more complicated than the sum of single hazards. The resilience depends on the magnitude of the hazards, their spatiotemporal relationship and dynamic combined impacts, and infrastructure interdependencies. The article presents a comparison between physical and functional resilience of an electric transmission network, and finds functional resilience is always higher than physical resilience. The multiple hazards resilience evaluation approach is applicable to any type of infrastructure and hazard and it can contribute to the improvement of infrastructure planning, design, and maintenance decision making.