Sustainability Science

, Volume 13, Issue 1, pp 235–254 | Cite as

An integrated framework for resilience research: a systematic review based on citation network analysis

  • Li Xu
  • Yuya Kajikawa
Review Article
Part of the following topical collections:
  1. Concepts, Methodology, and Knowledge Management for Sustainability Science


Resilience thinking has been widely used as a tool for interdisciplinary studies in addressing disturbance and change. However, not many studies have been taken to synthesize resilience as an interdisciplinary concept being applied across different disciplines and to investigate the common issues among them. This paper explores a conceptual framework for resilience research in an interdisciplinary perspective. In doing so, we first illustrate the academic landscape for resilience research according to the citation network of publications. After that, we categorize resilience studies into ten core research domains by their inner citation relationships. And then, we propose a framework which synthesizes principles of resilience from different research fields embracing key components (behaviors, capacities, influencing factors, interventions, and system dynamics). Based on four theoretical features—conceptions, characteristics, influencing factors, and intervention strategies, we extract key points from each domain. As resilience is a creative theory for sustainability science, this study is expected to contribute to sustainability science by generalizing resilience as an interdisciplinary concept.


Resilience framework Knowledge integration Interdisciplinary study Literature review 


Why do some individuals recover in a short time following psychological adversities such as loss and trauma, while others take months or even longer time? Why do some areas resist from extreme events with success, while others fail? Why do some animals and plants survive from fires, while others die? Can people and nature live with the imperative climate changes by the current status? These concerns have given birth to the prosperity of resilience research in numerous fields (Xu and Marinova 2013; Baggio et al. 2015). There is increasing recognition that resilience can be a useful framework for various systems to address changes and disruptions (Brand and Jax 2007). Resilience has close relations with sustainability. Systems without resilience are hard to achieve sustainability. It is thus a creative approach for sustainability science (Kajikawa et al. 2014; Xu et al. 2015). Despite the fact that resilience research has been dramatically developed, the knowledge is still dispersed in preexisting studies and is bunched in a few domains, especially in ecological and psychological research and in coupled social–ecological contexts.

Efforts have recently been made to synthesize resilience across different research fields. For example, Cretney (2014) incorporated resilience interpretations in academic, political, and activist areas and pointed out the possible directions for future research of resilience in geography; Brown (2016) linked up resilience in developmental psychology with anthropology and sociology; Quinlan et al. (2016) integrated measurements of resilience through multidisciplinary approaches. However, a framework that depicts resilience as a science being used across different disciplines is still a lack. Given that a framework which links different ideas and identifies relevant factors can be used to support the examination of complex problems (Pickett et al. 2007), there is a strong need to bring together key ideas from broader disciplines into such a framework for a more comprehensive understanding of resilience. In this paper, we use a combined citation network and text-based literature analyses on preexisting resilience studies and propose a conceptual framework to synthesize key knowledge of different research fields.

Citation network analysis is not a new approach which is used to detect the development of resilience research. Janssen et al. (2006) illustrated the development of research on resilience, vulnerability, and adaptation in global environmental change arena. Baggio et al. (2015) extended the citation analysis to present the roles that resilience concept played in connecting disparate fields. Yet, these studies did not answer a critical question of what resilience has offered various fields in terms of sharing its core conception and ways of thinking. This is also claimed as a barrier for resilience research because the shared parts of resilience are still loosely defined or standardized (Baggio et al. 2015). Therefore, we shed greater light on identifying the structure of resilience research and exploring common ideas within the same field as well as across different fields. To this end, we concentrated on common concepts of resilience research among different disciplines by direct citation network analysis. After that, we devoted attention to the inner structure of resilience by qualitative and heuristic reviews. This is expected to contribute to sustainability science by generalizing resilience as an interdisciplinary concept.

More specifically, we first collected bibliographic records of research, which published during the time period of 1933–2015 with “resilience” as the topic, from the Web of Science. And then, we classified these publications into several clusters and treated the top ones as core research domains. The core domains were illustrated in different colors in a large graph layout (Adai et al. 2004). To extract key information from each domain, we proposed an integrated framework, which embraces four features of resilience (conceptions, characteristics, influencing factors, and intervention strategies), and selected popular papers from each cluster for the further review. Those papers contain at least one of the most frequent terms (top 5) emerged in their clusters, or “resilience” frequently appears in their abstracts. The final 332 papers were selected and further reviewed. The detailed explanations of the methods of this paper can be found in Appendix 1.

Core domains of resilience research

As we concern about the direct citation relations of publications, the total of 19459 papers were selected as nodes and 86028 citations were extracted to generate the component of the citation network. The network was categorized into 212 clusters by the modularity maximization algorithm (Newman 2006). However, most of the clusters contain relatively small proportion of papers. We therefore treated the top 10 clusters as core domains of resilience research, containing 92% of the component. Next, we analyzed contents of the core domains and named each cluster according to its main research focus (Table 1). Specifically, we analyzed the research fields of papers which were frequently cited by others through comparing the top ten key terms of each cluster with titles of papers and their keywords, abstracts, and journals in which they were published. The key terms were generated by a natural language processing approach explained in Kajikawa et al. (2007).
Table 1

Classification of resilience research

Cluster (#)



Top 5 Journal

#1 Psychology and social science



Children and Youth Services Review


Development and Psychopathology


Child Abuse and Neglect


Social Science and Medicine


American Journal of Orthopsychiatry


#2 Social–ecological systems and management



Ecology and Society




Global Environmental Change


Coral Reef


Marine Ecology Progress Series


#3 Ecological and environmental sciences



Forest Ecology and Management


Freshwater Biology




Plos One


Ecological Applications


#4 Business systems and engineering



Reliability Engineering and System Safety


Lecture Notes in Computer Science


Natural Hazards


Risk Analysis




#5 Telecommunication systems



IEEE Transactions on Circuits and Systems for Video Technology


International Society for Optics and Photonics


Signal Processing: Image Communication


IEEE Image Processing


IEEE Transactions on Image Processing


#6 Psychiatry and brain science



Biological Psychiatry


The Journal of Neuroscience






Behavioural Brain Research


#7 Water systems engineering



Water Resources Management


Water Resources Research


Journal of Hydrology


Journal of Water Resources Planning and Management


Proceedings of The Institution Of Civil Engineers


#8 Livestock and animal health



Veterinary Parasitology


Small Ruminant Research


International Journal for Parasitology


Australian Journal of Experimental Agriculture




#9 Marine science and fishery



ICES Journal of Marine Science


Marine Ecology Progress Series


Fisheries Research


Marine and Freshwater Research


Fish and Fisheries


#10 Biological and material sciences





Journal of Experimental Biology


Textile Research Journal




Fibers and Polymers


Since the first four clusters embrace a large number of papers, we conducted recursive clustering for them to produce their sub-clusters. For sub-clusters, we chose the top ones which cover more than 80% papers of each cluster as the main research fields for the further analysis. The process of classification of resilience research is described in more detail in Appendix 2. We presented, herein, the citation network of resilience, which we call academic landscape (Fig. 1), to illustrate how resilience research is connected as an interdisciplinary concept.
Fig. 1

Overview of academic landscape of resilience science

Although the research on resilience has been applied in a wide range of fields, the overall academic landscape showed that the studies are closely knitted and centered into a few domains. The first four clusters dominate the network, while some fields are isolated from the giant components of the network, i.e., studies on telecommunication systems (#5), livestock and animal health (#8), and biological and material sciences (#10), implicating that these fields are seldom linked to others and have fewer academic commons with other fields. In particular, the cluster #2—social–ecological systems and management—seems to play a central role in resilience research. In order to extract information of resilience in different fields, we propose a framework as described in the next section.

A proposed framework for resilience research

As a system approach, resilience research needs to consider both social and natural systems as well as mental health of individuals and the impacts of technological innovations when dealing with changes in environment. Three dimensions (humanity, technological, and natural) are thus defined and incorporated into four conceptual features (Fig. 2). Accordingly, we propose an integrative framework to describe key issues which should be taken into account in resilience research (Fig. 3).
Fig. 2

Outline of developing the framework

Fig. 3

Integrated framework for resilience analysis

In the proposed framework (Fig. 3), we treat resilience and its cognate concepts as the behaviors that the focal system could have in the face of disturbances. Resilience analysis starts with the question about what possible states (behaviors) the system could have in response to different types of disturbances. For example, there are several potential states for individuals who are suffering from mental stresses or instant shocks. One state is that individuals would be able to recover to the pre-disturbance level. In such a case, resilience analysis should focus on managing the factors that help the studied system recover over a certain timescale. Another state is that individuals would not be able to recover and a new state could be possible. In this case, transformability is critical, and resilience analysis could concentrate on leading to a successful transformation. Therefore, several questions have to be answered in the first place, including what disturbance the system is facing or may face with, what system’s state needs to be maintained and to what extent, what possible states of the system, for whom the system needs to be resilient, and to where if the system is not resilient.

After defining system’s behaviors, we view characteristics of resilience (i.e., sources of resilience) as the system’s capabilities to adapt to disturbance and change. We suggest resilience analysis consider system’s abilities to withstand different sorts of disturbances. These abilities include the capabilities which are embedded inside the system or retrieved from external forces, namely flexibility, redundancy, diversity, and connectedness.

The system has more potential to absorb disturbance under the impacts of key influencing factors. As a result, we recommend further considerations to identify those factors and their possible influencing paths at different time and space scales. Factors are those which have impacts on system’s resilience and states through changing their characteristics. The factors can be triggered by external events or deeply rooted in system itself. For example, fishing activities threaten marine biodiversity and reduce resilience of marine systems to climate change (Bellwood et al. 2004); individuals’ religion and faith as well as locus of control generate impacts on their ability to withstand life adversities (Richardson 2002; McAllister and McKinnon 2009). Also, these factors sometimes exhibit in a relatively rapid rate, while in other cases they are comparatively slow. For instance, extreme weather events such as storms and tornados influence social–ecological resilience in a quick rate; long-term phosphorous release from agricultural fields in a catchment is slow, which could lead to the regime shift of the lake downstream to an eutrophication state (Carpenter 2005). These factors could be categorized into humanity, technological, and natural dimensions according to their sources of energy. Therefore, resilience analysis should classify different factors according to the scales and scopes, i.e., extrinsic or intrinsic, slow or fast factors, and identify the dynamics among these factors, system’s state, and characteristics of system resilience.

In addition, interventions can also be recognized through the identification of those dynamics. The interventions can be divided into three stages—pre-disturbance, during disturbance, and post-disturbance. Monitoring and observation activities are suggested in the pre-disturbance stage. Assessment and response strategies should be taken during the disturbance. Long-term management and governance can be the crucial approaches to improve system’s resilience in the post-disturbance period.

While resilience has emerged as an applicable notion in many research fields, the definition still remains confusion because of the unclear distinction to its cognate concepts and different focuses of studies. In the following, we start with the discussion about cognate concepts of resilience, which are actively discussed in the core research domains, and their relations with resilience.

Explication and reorganization of cognate concepts

In general, resilience and its cognate concepts are terms used to describe systems’ different behaviors, states, and their possible trajectories in response to disturbances. In ecological contexts, resilience is usually treated together with stability and resistance, while resilience in material sciences pays more attentions to systems’ recovery and elasticity. When linking social systems to ecological systems, resilience is referred to the capacity of systems to absorb recurrent natural and human perturbations in order to maintain their function and regeneration ability or to transform to a new desirable state. This explores systems’ adaptability and transformability.

In order to mine the most relevant cognate concepts, we screened out potential terms, which frequently appear in the core research domains, through reading the selected popular papers (332 in total). These terms describe systems’ behaviors, states, and their possible trajectories in the face of disturbances. We then statistically compared these terms in the key term list generated by a natural language processing approach (Kajikawa et al. 2007) and nominated the most frequently used ones to be the relevant cognate concepts of resilience science. It should be noted that we combined those terms which express similar meanings. For example, the term “adaptability” is the combination of adapt, adaptation, adaptive, and adaptability.

The statistics showed that the most popular terms include recovery, stability, adaptability, transformability, vulnerability, robustness, resistance, and elasticity. The number of occurrences of these key terms is 9024, 5014, 18207, 3219, 8032, 3198, and 4315, respectively. Adaptability and vulnerability are two key terms that are usually discussed in studies of social–ecological systems and management (cluster #2) and psychology and social science (cluster #1). In clusters #1 and #2, the number of occurrences of adaptability is 3137 and 10061, while vulnerability is 1609 and 4125. Other popular terms discussed in cluster #2 include recovery (2261 occurrences) and transformability (1645 occurrences). Stability and resistance are two popular terms in ecological systems (cluster #3, 1875 occurrences), and the coupled social–ecological systems when the studies have special focuses on ecological dimensions. Robustness is widely discussed in telecommunication systems (cluster #5, 1584 occurrences), while elasticity is popular in biological and material sciences (cluster #10, 1400 occurrences). Accordingly, we nominated these eight terms to be the most relevant cognate concepts of resilience (detailed in S1) and explicated them in the following with the aim to elucidate their relations to resilience.


Recovery is emphasized in psychological and social science (cluster #1), social–ecological systems (cluster #2), biological and material sciences (cluster #10), and engineering contexts including business systems (cluster #4), telecommunication systems (cluster #5), and water systems engineering (cluster #7). It is a process of a system to bounce back to pre-disturbance status. Thus, the time that a system requires to recover (or recovery rate) after the disturbance is used as an indicator to assess its resilience (Fiering 1982; Fritz and Dodds 2004; Kjeldsen and Rosbjerg 2009). Yet, using recovery alone to assess systems’ resilience sometimes may be unreasonable or hard to achieve in that for some cases the disturbed system requires long time or even uncertain length of time to recover after a disruption, for example in studies of psychological hardships and seismic resilience (Bonanno 2004; Bruneau et al. 2003).

Resilience emphasizes that the state of a system in the face of disturbance is uncertain, which means that resilience is the system’s capacity to maintain its function and structure even when it faces unknown circumstances. Accordingly, we suggest studies not equal recovery to resilience. The system’s potential states in front of different types of disturbances are necessary to be considered for the clarification of meanings of resilience and their distinctions from recovery. As well, we propose that recovery is the ex post process of a system responding to external disturbance and its internal impact. A system has ability to get back to the pre-event state either with its internal adjustment or under external interventions. When a system is able to bounce back to the pre-event state, or the system has a single stable state, the system’s resilience can be treated in the way of recovery. Otherwise, the system’s resilience, taking ecological systems for an example, should be discussed in different scenarios as they may have multiple stable states. In such a case, thresholds of critical components of the system have to be identified and carefully monitored.


Stability refers to the equilibrium of a system facing with external changes and disruptions in ecological and environmental sciences (cluster #3). In engineering, equilibrium normally displays as a single and static state of a system. Resilience can be measured by the time that the system requires to recover to the pre-disturbance equilibrium or to sustain a level of functionality (Chang and Shinozuka 2004; Cimellaro et al. 2010), or the probability of the system to keep itself in the same equilibrium when a failure occurs (Najjar and Gaudiot 1990). On the contrary, the stability of ecosystems usually displays as the domain of attraction (a basin) which allows them to absorb changes and sustain the functions before losing their structures (Holling 1973, 2001). A system with high stability thus does not mean that it is highly resilient, but it may be the foundation of resilience, and vice versa. What the role they play in determining system’s state is context-based. For example, the areas, in which the populations fluctuate widely due to the high climatic variability, may have great resilience but low stability (Holling 1973). The study of Seidl et al. (2014) showed that disturbance remnants (trees and patches) contribute greatly to the resilience of forest ecosystems even though climatic variability leads to the fluctuation.

In this paper, we suggest that stability be the in situ process of a system during the disturbance. It emphasizes static state of a system around a point which can be found in some cases such as engineering systems. In other cases, however, the system’s state is not static but dynamic with multiple stable states defined by different thresholds of system’s components. The system approaches to the thresholds under the effects of energy when disturbance occurs. Nevertheless, the system is still stable because the thresholds of system’s components have not been surpassed. When the strength of disturbance goes beyond the tolerance of the system so that the thresholds are surpassed, the system’s state shifts to a qualitatively different regime characterized by a different set of structure and functions. The new regime could be stable but may be desirable or undesirable. The aim of resilience management is to keep the state of a system in a desirable state and being away from undesirable ones. The system’s resilience can be measured by the maximum range that the system can endure before the regime shifts happen.


As a frequently discussed concept in psychology and social science (cluster #1), social–ecological systems and management (cluster #2), interdisciplinary study on public health (cluster #4-4), regional economy (cluster #4-6), and psychiatry and brain science (cluster #6), adaptability (or adaptation and adaptive capacity) refers to the capacity of actors in the system to affect resilience (Walker et al. 2004). The most common attribute of adaptability and resilience is that they both deal with dynamics within the existing system or a set of related systems.

In the combined social and ecological systems, research on adaptability is centered on how social actors (individuals, families, communities, and societies) respond to certain environmental changes. The research aims to reduce vulnerability of systems and to manage systems’ thresholds (Lebel et al. 2006; Nelson et al. 2007). Human can manage systems’ resilience by controlling thresholds of systems’ components due to the fact that adaptability and resilience are affected by intentional or unintentional behaviors of human (Walker et al. 2004). That is, how and to what extent humans can manage systems’ features so as to prevent them from crossing thresholds and shifting into an undesirable regime.

We consider adaptability as the internal ability of a system to respond to the disturbance. With such ability, the system can reorganize its internal features. When the system’s adaptability is not capable of reorganizing its features, the external interventions should be taken to prevent thresholds of the critical components from being crossed and such that the enhancement of system’s resilience. Adaptation is also an ex post process as recovery, but they are different on the grounds that returning to the previous state is not the necessary or only solution of system’s adaptation in response to a disturbance.


Transformability is specially emphasized in social–ecological domains (cluster #2). It refers to the ability of a system to generate an opportunity for a new state when components of the system cannot tolerate the ongoing disturbance. Transformability is a critical attribute which forms development tendency and dynamics of social–ecological systems (Walker et al. 2004). It has close relationships with system’s adjustment in the adaptation process. According to Nelson et al. (2007), there are generally three pathways for a system in relation to its evolution processes in the face of disturbances. First, if the system’s adjustment is increasing while its resilience is low, the system will go toward an undesirable state (or collapse) if a threshold is crossed. Second, if the system’s adjustment is increasing while resilience is high, then the system will go toward another acceptable state (transformation happens). Third, if the system’s adjustment is slow while resilience is high, then the system’s state will not critically shift to a different one and management strategies are possible.

Therefore, dynamic systems have ability to use disturbances as opportunities and transfer systems’ state toward a new trajectory. A system with transformability would be easier to be intervened because it provides possibility for managers to steer the system to the desirable state. Transformability assumes that the system is hard to recover to the pre-disturbance state while appropriate interventions would enable the system to be resilient or to survive in a new regime rather than collapse.


Vulnerability is the sensitivity of a system in exposure to threats and the system’s potential to changes under the pressure. As its actor-based focuses, vulnerability has been widely applied in psychology and social science (cluster #1), social–ecological systems (cluster #2), ecological issues (cluster #7), and business and economic problems (cluster #4) as well as medical issues (cluster #6). Vulnerability and resilience are interconnected properties of social–ecological systems, but the specific nature of the relation is not clear. In studies of business systems, the effective way to increase resilience of supply chain is not only to increase the capacity but also to decrease vulnerability of the chain at the same time (Pettit et al. 2010). Although vulnerability and resilience share some commons with regard to coping with threats and stresses, they have differences and can be complementary to each other (Miller et al. 2010). Thus, resilience and vulnerability can be simply interpreted as neither antonymic concepts nor interchangeable terms.

The research of vulnerability mainly focuses on responses to hazards and shocks while the long-term adjustment and change that the system may have are seldom discussed. Vulnerability explores how actors of a system are sensitive to and influenced by different sorts of disturbances. Resilience has similar starting point but has its special focus on system’s dynamic adjustment and adaptive capacities in different spatial and temporal scales. As well, resilience emphasizes interaction of social and ecological processes, which is less concerned in traditional vulnerability studies. Hence, vulnerability can be one of microscopic foundations maintaining resilience of macroscopic systems.


Robustness—strength of a system and its units to withstand a given level of stress and maintain their function—is commonly discussed as a measure of resilience in engineering related contexts (cluster #4) and telecommunication systems (cluster #5) (Bruneau et al. 2003; Brandon-Jones et al. 2014). Janssen et al. (2004, 2007) brought such notion into network analysis of resilience in social–ecological systems (cluster #2-1). In their opinion, robustness is more appropriate for the use in relation to social–ecological issues to cope with disruptions in that robustness has advantages in addressing cost–benefits and trade-offs associated with the system (Janssen et al. 2004). Robustness and resilience have the same meaning, and they both evolve in complex adaptive systems and have relationships along with the process of adaptive cycle (Gunderson 2000). However, whether robustness and resilience should be treated in the similar way and used interchangeably or there are essential distinctions between them are still in debate.

We regard, in this paper, robustness as a key property of engineering systems to keep their basic functionality under random failures of components and intentional attacks. In a network, robustness emphasizes connectivity of vertices or edges, and robustness approach aims to avoid the network from disconnection. Resilience has its origin from engineering which refers to the rate at which the system returns to a single stable state. A redundant path in a network can assure system’s resilience after a failure through switching collapsed path to the redundant path. This system’s transition is akin to its transformability. The engineering resilience has similar concerns to robustness assuming that the system has single steady and has ability to resist changing and recovering. Yet, the broader definition of resilience gives its wider applications to look into systematic recovery and adaptation from the disturbance. Another difference is the parts in the system or the system as a whole. In a robust system, parts are interchangeable and their functions maintain the whole system. Transformation has a connotation that involves evolution or replacement of the system as a whole.


Resistance is usually discussed in ecological and environmental sciences (cluster #3) and in those contexts talking about human activities, climate change, and coral reefs resilience (cluster #2-3), psychiatry and brain science (cluster #6), as well as livestock and animal health (cluster #8). It refers to the ability of an individual species or assembled individuals to resist or to survive with intrinsic and extrinsic factors (such as physiological tolerance, initial assemblage, physical and environmental conditions) in a disturbance, such as corals in response to bleaching disturbances (West and Salm 2003). Resistance and resilience can be linked when discussing about the stability of systems. In some ecological contexts, for example soil science (cluster #3-2) and river systems (cluster #3-3), they are two critical components of ecological stability (Neubert and Caswell 1997; Fritz and Dodds 2004; Shade et al. 2012). There exists an assumption in such cases that the systems are able to return to pre-disturbance stable state. However, the stable states in many ecological systems are multiple. Once a threshold of critical variable is crossed, the system’s state will flip into another stable state in which it might be impossible or very hard to recover, such as changes in the state of lakes from clear water to turbid water (eutrophication) (Carpenter 2005), or even in some states the change is irreversible (Scheffer et al. 2009).

On the other hand, resistance is the ability of a system to stay with essentially unchanged structures in the presence of a disturbance, while resilience emphasizes on returning or transferring to reference state after the disturbance (Grimm and Wissel 1997). Resistance is thus a critical aspect of resilience to keep the system in the stable landscape, and it can be used to measure system’s adaptability responding to changes (Walker et al. 2004). That is, the stronger the resistant ability, the higher resilient can be the system, and the greater disturbance the system can tolerate.


Elasticity is more frequently used in biological and material sciences (cluster #10) and sometimes in the research on resilience of forestry (cluster #3-1) and in communication network (cluster #4-3). In biological and material sciences, resilience is connected to elasticity and is defined as the ability of a material (in particular man-made materials) to recover by its energy from deformation which is caused by an applied stress (Elvin et al. 2005; Lyons et al. 2009). The materials which have relative high elasticity are more likely resilient and easier to recover from the deformation. Efficiency or the recovery time which relies on stress–strain characteristics determines resilience of materials. In this sense, elasticity is akin to resilience but is value-neutral. For example, the rubber-like materials, typically like resilin, possess remarkable elasticity and high resilience when stretched (Whittaker et al. 2014). On the other hand, elasticity can be the strongest indicator of the overall resilience of ecological community (Malanson and Trabaud 1987). Studies in the domain of forestry use elasticity to measure resilience of forest systems and vegetation facing wildfires (Westman 1978; Grimm and Wissel 1997). Elasticity therefore refers to the time or speed that is required by forest systems to restore after fires. Resilience thinking accepts disturbances and aims to maintain the system in a desirable state or transfer to a new configuration.

Characteristics of resilience

As discussed above, resilience has a number of connotations and cognate concepts. These cognate concepts are behavior-oriented and useful for the behavioral design of systems. However, they are intangible in operation for enhancing systems’ resilience. The further investigation of characteristics and properties of systems’ resilience can be helpful for practical implementations. In this part, we focus on characteristics of resilience in the ten clusters pursuing synthesis of the core characters that contribute to resilience in various systems, i.e., the sources of resilience. Through systematic review of the selected 332 papers, we first identified seven characteristics of resilience (flexibility, redundancy, diversity, resourcefulness, preparedness, connectedness, and social capital). As some characteristics overlap with others in terms of the inclusive components, we then used the same method, i.e., statistical analysis of the key terms (Table 2), to validate our selection and reduce duplications.
Table 2

Statistics of numbers of key terms in each cluster



































































Social capital






















It can be seen from Table 2 that flexibility, redundancy, and diversity are viewed as the main sources of resilience in most research fields. While social capital, preparedness, and resourcefulness are important characteristics of resilience, they are emphasized in fewer fields, probably because many overlaps exist between them and other characteristics and researchers tend to use broader terms to describe the similar meanings. For example, resourcefulness can be viewed as the property of a system to support its flexibility (Walsh 1996). Therefore, we integrated social capital, preparedness, and resourcefulness into other four characteristics in our further discussions, namely flexibility, redundancy, diversity, and connectedness. We note that some of these characteristics may still have overlaps. However, they have different scopes and emphases when applied in different fields in addressing different issues. We also note that different research fields may use different terms to describe similar concepts, e.g., agility vs. flexibility, and our study focuses on those which appear in the key term list retrieved from collected papers.


Flexibility is an asset of resilience in organizational, structural, and institutional frameworks in which it means a system’s ability to quickly and easily change inputs and outputs when disruption occurs. It can be attained by adjusting configuration of components in a system during the disturbance. Flexibility is a common issue discussed in many research domains including social–ecological systems and management (clusters #2), ecological and environmental science (cluster #3), business systems and engineering (cluster #4), and marine science and fishery (cluster #9). One may doubt that flexibility is very close to elasticity or even the same in some cases. However, they are different because elasticity describes system’s behaviors to disturbance, while flexibility is system’s capacity to enable such behavior to occur. The system is flexible if output variation is able to be kept in a desired range of configuration when input is changed (Dinh et al. 2012). It can include flexibility in sourcing and order fulfillment in supply chain systems (cluster #4) such as alternative distribution channels, rerouting of requirements, and multiple modular product designs (Pettit et al. 2010), flexibility in informal institutions (Olsson et al. 2004), and flexible learning-based natural resources management systems (Tompkins and Adger 2004) (cluster #2). A flexible system, in this sense, requires the availability of resources and the system’s accessibility to them, which refer to system’s resourcefulness (Walsh 1996). A system within flexibility could be resilient, and the recovery to the pre-disturbance conditions is not the essential process as long as the system can keep itself in a desirable range of configuration.


Redundancy is advocated as another important characteristic of resilience in most of research fields: social–ecological systems and management (cluster #2), ecological and environmental science (cluster #3), business systems and engineering (cluster #4), and telecommunication systems (cluster #5). It generally stands for the capability of a system and its subsystems to satisfy functional requirements and keep them operating when facing disturbance and component failure. Redundancy sheds the light on systems’ function in ecology, urban infrastructure, and telecommunication networks. In ecology, high functional redundancy associated with high degree of metabolic flexibility could contribute greatly to microbial community’s resilience (Allison and Martiny 2008), and the loss of redundancy may reduce ecosystem’s ability to withstand disturbance (Peterson et al. 1998). It is recommended that the redundancy of technology, organization, and socioeconomic systems can and should be embedded in the quantitative measurement of community resilience in response to seismic disturbances (Bruneau et al. 2003). Also, redundancy is used as an effective technique to deal with disruptions in telecommunication networks and to compensate for the random uncorrelated failure of components in computer networks (Sterbenz et al. 2010). The error resilience of networks can be enhanced by adding a certain amount of redundancy in the coded bit streams, the video coding layers for an example (Wenger 2003). Hence, the more redundant elements input into the system will increase its resilience. This is because those elements can provide compensations for the loss of functionalities in order to avoid system’s collapse. Although redundancy is the source of system resilience, the contribution of a single source is limited while combination of different resilience characteristics is needed. For example, redundancy for fault tolerance in communication networks is not sufficient for their resilience and geographic diversity is also required (Sterbenz et al. 2010). In coupled social and ecological systems, extra social capital is the essential.


Diversity serves systems with options and new opportunities in the face of hazards. Genetic, species, response, and functional diversities are critical for the biodiversity such that resilience of forest and many other ecological systems (homogeneity vs. heterogeneity in ecological and environmental sciences—cluster #3) (Walker et al. 1999; Elmqvist et al. 2003; Drever et al. 2006; Schaberg et al. 2008). What is more, diversity is the state of systems’ components and contributes to social–ecological resilience (cluster #2): diversity in local adaptations, governance and decision participants, and diversity in livelihood of choices and institutions including diverse species, human opportunities, economic choices, and politics (Folke et al. 2002; Adger et al. 2005; Berkes 2007). The communities which are dependent on commodity agriculture are less resilient than those which have more diverse choices in traditional farming systems when natural disasters occur (Holt-Gimenez 2002). Ullsten et al. (2004) emphasized the importance of diversification of local economies in building resilience to the impacts from resource developments at larger scales. Diversity is closely related to redundancy but has differences in keeping systems away from sharing the same outcomes. This is because diversity (spatial, temporal, topological, geographic, and operational) provides alternatives to systems in finding new adaptive ways rather than falling into normal operations (discussed in telecommunication systems—cluster #5) (Sterbenz et al. 2010). In comparison, redundant system is designed before disturbance while diversity of the system does not require detailed design. Diversity is expected to offer redundant and alternative channels and resources during a disturbance. Although it has overlaps with redundancy, their overlapping functions could play key roles in creating opportunities such as diversity in institutions for reorganization and learning while coping with disturbance and risks.


Connectedness to social environment, as discussed in psychology and social science (cluster #1), is a source for psychological resilience of people who are working in stressful working circumstances (McAllister and McKinnon 2009). Connectedness (e.g., family and school) can help adolescent obtain social support and prevent young people from depressed mood and health risks (Rew et al. 2001). Social connectedness such as neighbor-to-neighbor reliance is critical for community resilience because it can help communities to exchange resource, to support each other, and to respond and recover together (Chandra et al. 2013). In this sense, social connectedness is a property for effective social networks thereby social–ecological resilience (cluster #2). Relational connectedness exists in social networks will be helpful for maintaining long-term social relations so that collaborative actions could be possible. Gunderson and Holling (2002) used connectedness as an indicator to describe how adaptive cycle happens in ecological systems and then social–ecological systems (cluster #3) (discussed in next segment). Connectedness generally has positive impacts on resilience, but when considering the complex adaptive systems the over connectedness with stickiness and rigidity might reduce systems’ adaptability. To find out the trade-offs between connectedness and resilience, Simmie and Martin (2010) suggested that it is a need to analyze them in evolutionary perspectives such as adaptive cycle analysis at multiple levels.

Again, any single characteristic cannot independently contribute to systems’ resilience. This is because changes are increasing and lots of them are unpredictable, and the effects of these changes on systems’ components are complex being influenced by various uncertain variables. Managing systems’ resilience should focus on the enhancement of the combination of these characteristics. For those systems in which threats, changes, disasters, and disruptions are anticipatable, such as in engineering safety and production (Shirali et al. 2013; Azadeh et al. 2014), preparedness could be a better indicator for measuring resilience than others. Well-organized social capital, including social networks, leadership, norms, values, trust, and social memory, should be flexible for learning knowledge and sharing information, diverse in options, and connected to individuals resilience (Folke 2006; Walker et al. 2006; Berkes 2007; Ungar 2011; Chandra et al. 2013), and thus it can be a crucial element with the complement of other characteristics for community resilience.

Influencing factors of systems’ resilience

There are several sources for the improvement of resilience as discussed above. Nevertheless, these sources are influenced by various factors via the dynamics within systems and the interplay with conditions outside. We therefore further explored the factors that have potential impacts on systems’ resilience and their influencing patterns.

Influencing factors

In this paper, we classified influencing factors of resilience by the time they exert on systems’ resilience and the roots in where they are built. In doing so, all factors and their influencing paths discussed in the selected 332 papers were screened out manually by authors in the manner of text-based surveys, and then we categorized these factors into different groups according to the definitions given below. Specifically, according to the rate and frequency of changes, factors were divided into two types—slow factors and fast factors. Fast factors are defined as those which can help systems to quickly respond to the disturbances so that affect systems’ resilience. Those factors which are normally formed in the long run or have impacts on systems’ resilience in longer timescales are identified as slow factors, such as education and local ecological knowledge. On the other hand, factors were grouped into intrinsic and extrinsic ones on the basis of where they come from (i.e., their roots). Intrinsic factors stand for those factors which are embedded inside systems and affect systems’ resilience through internal mechanisms. Extrinsic factors are those which derive from outside of systems and affect systems’ resilience through interacting with systems’ components. For example, personality of individuals, systems’ self-organization, local ecological knowledge, and learning ability are defined as intrinsic factors, while social support, financial security, social connections, natural disasters, and resources accessibility are classified as extrinsic factors. The distinction between the terms “intrinsic” and “extrinsic” is dependent on boundary definition of systems, while other factors could also be recognized according to systems of interest.

In addition, different factors have different influencing scopes to systems’ resilience because of their diverse attributes. For instance, the psychological resilience of a person is influenced by personal mental situation and social competence around him/her. Resilience in soil systems may be influenced by microbial composition and physiology. We therefore categorized all the identified factors by three dimensions: humanity, technological, and natural dimensions (see S2). Factors in humanity dimension refer to those which derive from human characters including social, economic, psychological, and political factors, whereas factors in technological dimension are composed of those which are generated by technological development and changes. In natural dimension, factors include those which are symbolized with ecological and environmental features.

As listed in S2, some factors can be grouped into not only one defined group. For example, the changes and disturbances brought about by human actions in many cases display in a relatively slow rate such as impact of fishing activities on marine biodiversity. In some other cases, however, they generate quick disruptions because of human mistakes such as chemical spill. These quick events could result in instabilities of social and economic systems in a very short period of time (Adger 2000). Besides, extrinsic factors derived from societies can be either fast or slow according to the forms of disturbances and their interactive dynamics with system components. Communities which have low level of social support may be quickly affected by the emergent disasters and recover very slow until interventions are applied in a relatively quick way (Bonanno et al. 2007). However, growing economy and continuing education can help them recover through self-adjustment. Their resilience is thus relative high. For the better illustration, we synthesized these overlapping factors into four categories and illustrated in Fig. 4: fast and intrinsic factors (I), slow and intrinsic factors (II), fast and extrinsic factors (III), and slow and extrinsic factors (IV).
Fig. 4

Classification and examples of influencing factors by time and scopes

Influencing path

How the afore-discussed factors impact on systems’ resilience is largely reliant on the mechanisms of how systems evolve along with their internal properties and the responses to the disturbance. For example, resilience of individuals can help them escape from deleterious effects of stress according to the molecular mechanisms rooted in their brains and mechanisms between pressures from the stress on their brains and behaviors as well as interactions between neural processes and biological mechanisms (Franklin et al. 2012). While species diversity contributes to ecological resilience, it does not always direct systems to the right way because response diversity can also influence systems’ resilience through affecting their renewal and reorganization (Elmqvist et al. 2003). Therefore, we discuss prevailing methods to explore possible ways of impacts from those factors on systems’ resilience.

Adaptive cycle

Adaptive cycle is prevalently applied in psychological resilience, ecological resilience, and social–ecological resilience (Folke et al. 2002; Walker et al. 2002; Allison and Hobbs 2004). The adaptive cycle is originally derivative from ecological studies and explains system dynamics with the focus on process of destruction and reorganization by self-organization ability of the system itself. Such self-organization ability depends on the system’s flexibility of structures, diversity of choices, connectedness among system components and other systems, and availability of resources. They are susceptible to influencing factors at different time and space scales. In the adaptive cycle, the system evolves through four phases—growth (exploitation), conservation, release (collapse), and renewal (reorganization) (Gunderson and Holling 2002; Walker et al. 2002). It is more likely a descriptive model but also provides a theoretical fundamental for empirical studies on the measurement of resilience (Cabell and Oelofse 2012).

In the first phase in adaptive cycles (r), the system is developing while resources are accumulatively exploited, which slowly move the system into the second phase (K) in which system’s resilience tends to decline and conservation is necessary. In psychological resilience, a crisis is often viewed as a “turning point” for a family resulting in major change in its structure and functioning patterns. Management for family resilience is to find ways to obtain positive family outcomes (Black and Lobo 2008). According to such mechanisms, protective factors such as promotive and positive emotions can result in positive feedbacks to individuals’ active adaptations when pressures occur. However, people are living in a changing society in where social relationships and external disturbances exert pressures on their protective factors. Alike resources exploitation, accumulated pressures lead to the gradual decline in their resilience, and any unexpected shocks at this stage become hazardous for them to live through.

In the third phase of the adaptive cycles (Ω), a rapid change or disturbance can cause creative destruction (even collapse) in the system and release of resources. And then, the system experiences a short phase of reorganization or renewal (α). If the system is still able to provide essential components for its reorganization in this phase, it will return to the first phase as a cycle afterward; otherwise, the system will be renewed with new actors, institutions, and plans and move to another exploitation phase. The cycle in this stage displays a “backloop” dynamics and evolves in a relatively quick speed. The system’s learning and self-and reorganization abilities play more important roles. Whether the system will return or shift into another configuration is related to whether the threshold of system’s critical characteristics is surpassed (see more discussions in Walker and Meyers 2004; Xu et al. 2015). This is also a good explanation of why some individuals recover in a short time following psychological adversities such as loss and trauma but others struggle for months or even longer, and why some areas resist from extreme events with success while others fail. The similar evolving paths (four phases in adaptive cycles) can also be found in studies of social systems (Pelling and Manuel-Navarrete 2011), regional economic analysis (Simmie and Martin 2010), and engineering safety assessment (Woods 2015).

Although adaptive cycles provides a useful way of thinking about patterns of dynamics between changes and system responses, we do not claim that it can be usable for any cases, but we do suggest that it could be a good indication and an option for the consideration of systems’ dynamics and find out effective interventions (types and timings) for systems to adapt to disturbances.

Cross-scale linkages (Panarchy)

Systems operate at multiple scales of time, space, and social organizations. Their resilience is influenced by such across dynamics. Panarchy as a framework describes hierarchical dynamics within nested adaptive cycles among different systems at multiple levels (Gunderson and Holling 2002). However, it is different from traditional hierarchical analysis which focuses on top-down processes and effects at larger scales. Panarchy emphasizes both top-down and bottom-up effects from small and large scales (Gunderson and Holling 2002; Allen et al. 2014). The model was developed based on observed evidence which demonstrated the cross-scale linkages among different systems and their components.

More specifically, there exist multiple connections, which termed as “revolt” and “remember”, between phases of the adaptive cycle at one level and phases at another level. The “revolt” linkage from lower level to higher level is small and fast, while the “remember” linkage from higher level to lower level is large and slow, which stabilize and conserve accumulated memory of system dynamics. In other words, the larger and slower cycles set up constraints on the smaller and faster ones. The cross-linkages take place during the Ω and α phases in the adaptive cycles, in which collapse that happens in Ω phase at one level can trigger a crisis to the upper level, i.e., “revolt” linkage. In the “remember” linkage, the α phase of the cycles is defined or organized by K phase at a higher level (Resilience Alliance 2015). Changes in variables of lower levels of the system can trigger changes in other variables at higher hierarchical levels of the linked systems. For example, given that demographic effects of fishing on capacity of population to buffer climate variability are substantial, overfishing may increase turnover rate of fishes at local level and reduce diversity of species in marine ecosystems at regional level, which exacerbate the effects of environmental changes at larger levels (Hsieh et al. 2010).

As Panarchy is fundamentally derived from the adaptive cycles, it has potential applications to different research areas. For example, it has been applied in politics and environmental laws for sustainability of social–ecological systems (Benson and Garmestani 2011); relations between environmental changes and social migration (Warner 2011); explanations of the role of thought in the intersection between human psychology and social–ecological systems (Varey 2011). Panarchy is also suggested for the better understanding of regime shifts caused by cross-scale interactions between abrupt changes and systems’ states such as lakes, marine systems, and forests (Allen et al. 2014). Although Panarchy model has been widely applied, these applications are mainly focused on theoretical descriptions of systems’ evolution in response to disturbances. More empirical studies are needed, in particular testing by time series data from long-term monitoring.

Interventions for systems’ resilience

Different stages and extent of disturbance to a system require different sorts of interventions. Effective interventions for systems’ resilience greatly depend on whether the interventions are taken at the right time and in suitable scales as well as to the right objects. By using text-based surveys, we extracted interventions in the 10 core research domains and grouped them into three categories according to different stages of the disturbance.

Pre-disturbance interventions

Indicator-based interventions, including monitoring, assessment, and management, are proposed to foster systems’ resilience before and after disturbances (Bonanno et al. 2007; Cutter et al. 2008). At the early stage, predictors (Koslow et al. 2000) or early warning signals (Scheffer et al. 2001) should be developed in order to detect cautions of regime shifts. Monitoring and observation on characteristics of the focal system and on variables that produce impacts on the system’s resilience thus should be emphasized in this stage, such as preparedness, flexibility, and diversity of system components. These activities must include establishing indicators and the detection of early warning predictors for the system’s regime shifts, recognization of certain and uncertain disturbances (or controllable and uncontrollable stressors), and improvement planning on the basis of pre-risk or early risk assessment (Bonanno et al. 2007).

In psychological domain, the lack of social support, low intelligence and education, family background, prior psychiatric history, and dissociative reactions can be developed as predictors for low resilient functioning of people who suffered from PTSD (Bonanno 2004). In marine science, effective ways for fishery resilience can be focused on detecting vulnerable fishes and predictors of their vulnerability to catastrophes. For instance, low fecundity, age, size, and spatial heterogeneity are possible predictors for resilience of exploited fishes in facing adversity (Koslow et al. 2000).

In addition, building resilience needs to incorporate the knowledge of resource users and combined thinking of human, technology, and nature parts. Stakeholder engagement is a prevalent method to achieve this goal at all stages of the disturbance. In the pre-disturbance stage, stakeholders should be comprised of users from local level in that the knowledge of them can give rise to technological innovations and effective policies for systems (Folke et al. 2002). However, who decides what interventions should be taken to build up resilience to what, for whom, when, and for what purpose are the questions required to answer prior to conducting a stakeholder engagement.

Interventions during disturbance

Disturbance and change can be either fast or slow to systems’ resilience as discussed in the last section. Fast disturbances require quick responses, while to those slow ones (or ongoing ones), the long-term interventions are more effective. In the stage of during the disturbance, indicator-based interventions are also suggested in particular for those slow ones. Hence, interventions should at first recognize whether the disturbance to the focal system is quick event or the slow one, i.e., identification of disturbance type. Rapid events such as earthquakes are unpredictable requiring quick responses, while long-term activities should be taken to cope with slow ones such as climate change. This is due to the fact that quick responses are helpful for the system to quickly recover during the disturbance, such as pharmacological treatment for people who are suffering from depression and anxiety (Connor and Davidson 2003), cognitive appraisals, and emergency strategies that effectively cope with losses (Bruneau et al. 2003). For slow and ongoing disturbances, thresholds-based management is well advised to be applied following four ways: “…move the current state of the system away from or closer to the threshold; move thresholds away from or closer to the current state of the system; make the threshold more difficult or easier to reach; manage cross-scale interactions to avoid loss of resilience at the largest socially catastrophic scales” (Walker et al. 2004: 3). These activities can be benefited from local and expert stakeholders’ participation.

Post-disturbance interventions

In the post-disturbance stage, concerns are given to how to manage and govern systems to recover, restore, or bounce back from the disturbance, i.e., resilience and transformation. The example given in Nelson et al. (2007) well explained that traditional livelihood systems can no longer be supported by farming ecosystem due to the climate change, while changes in social goals may lead to a policy shift of irrigated agriculture to the development of tourism industry. When a system cannot adjust or restore from the external disturbance, the interventions such as adaptive governance and management are needed to steer the system to a successful transformation.

Adaptive governance is a process of creating adaptability and transformability in social–ecological systems and its process is relatively slow (Walker et al. 2004). In social–ecological resilience, management has to be focused on interplay of gradual (long-term) and abrupt (short-term) changes, which requires expanding analysis into broader temporal and spatial scales and the collaboration of stakeholders from multilevel-based institutions and organizations.

Strategies after the disturbance also need to focus on maintaining factors and components that contribute to the basic functions and structure of systems. They need to embrace diverse ways of observation over longer periods of time to understand more developmental factors, and strategies should be taken to improve systems’ critical assets. For example, building positive and nurturing professional relationships, maintaining positivity, developing emotional insights, and achieving life balance and spirituality are emphasized as long-term promoting strategies for personal resilience exposed to workplace adversity (Jackson et al. 2007). As well, social and academic skills, self-efficacy, and participation in social and community activities are critical developmental factors for adolescents in exposure to risks (Fergus and Zimmerman 2005). Furthermore, systems have to be managed for flexibility in the long run rather than stability as they are facing ongoing changes.


Resilience theory has been dramatically developed by different disciplines over the past decades, and it has now become a prevalent conception in addressing unanticipated disturbances and uncertainties. It is the interdisciplinary nature of resilience that offered different research fields ways of thinking of the dynamic feedbacks between systems and external changes. We, in this paper, took use of a coupled citation network and qualitative analyses on the existing publications in resilience. Citation network was used to illustrate the relations among publications, according to which the network was clustered into different groups. We note that the citation network approach used in this paper was to group research mainstreams of resilience rather than exploring in detail the citation relations. On the basis of the results, qualitative review of key papers in each cluster was conducted to complement citation network analysis with the aim to synthesize resilience research in an interdisciplinary perspective.

To conduct citation network analysis, we first derived data from the Web of Science with the topic in resilience, and then we constructed direct citation network by the retrieved bibliographic data and focused on the maximum connected component and excluded papers which have no citation with the component. At third, we converted those connected components into undirected network and clustered them by the modularity maximization algorithm. The clustered network was visualized as the academic landscape of resilience science.

The visualized academic landscape well exhibited the interdisciplinary applications of resilience thinking and their links. It also showed that the studies are closely knitted and centered into some fields which can be regarded as core domains of the current resilience research, namely psychology and social science, social–ecological systems and management, ecological and environmental sciences, business systems and engineering, telecommunication systems, psychiatry and brain science, water systems engineering, livestock and animals health, marine science and fishery, and biological and material sciences. In order to extract knowledge from each domain, we proposed a framework which embraces different features of resilience including cognate conceptions in resilience science, characteristics of resilience, influencing factors and their impact patterns, and interventions.

Eight most popular terms, describing systems’ different behaviors, states, and their possible trajectories in response to disturbances, were selected from the core research domains and compared with resilience. We suggested that resilience analysis start with the question about what possible states (behaviors) the system could have in response to different types of disturbances, i.e., recovery, stability, adaptability, transformability, vulnerability, robustness, resistance, and elasticity. For those studies which concern about ways to improve resilience, we recommended considering systems’ flexibility, redundancy, diversity, and connectedness. The specific considerations have to be given to the influencing factors on these systems’ characteristics and their influencing dynamics, especially the influencing paths of different factors (extrinsic, intrinsic, fast, and slow ones) at different time and space scales. This is because the identification of these system dynamics can help managers recognize appropriate interventions. We suggested that monitoring and observation activities be introduced in the pre-disturbance stage, assessment and response strategies be taken during the disturbance and long-term management and governance be developed in the post-disturbance period.

In conclusion, our integrated framework is the initial attempt to generalize resilience from different research fields. It should be acknowledged that we did not generalize methodologies which have been applied in resilience studies across different disciplines. The comparison and integration of them could also be helpful for resilience researchers to understand others from different fields. As a consequence, future studies which focus on similar generalization of resilience thinking will be useful for the exploration of sustainable solutions for human-nature systems.



A part of this research is financially supported by the Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (B) JY26285080. We gratefully thank editor and two anonymous reviewers for their valuable comments and suggestions which helped improve the quality of this paper.

Supplementary material

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Supplementary material 1 (DOCX 29 kb)
11625_2017_487_MOESM2_ESM.docx (26 kb)
Supplementary material 2 (DOCX 25 kb)


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Copyright information

© Springer Japan KK 2017

Authors and Affiliations

  1. 1.School of Environment and SocietyTokyo Institute of TechnologyTokyoJapan
  2. 2.Global Institute for Water Security and School of Environment and SustainabilityUniversity of SaskatchewanSaskatoonCanada

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