Mapping the knowledge development and frontier areas of public risk governance research

International Journal of Disaster Risk Reduction xxx (xxxx) xxx

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International Journal of Disaster Risk Reduction journal homepage: http://www.elsevier.com/locate/ijdrr

Mapping the knowledge development and frontier areas of public risk governance research Xue Lin a, Haibo Zhang a, Hengqin Wu b, *, Dongjin Cui c a

School of Government, Nanjing University, Nanjing, China Department of Build and Real Estate, The Hong Kong Polytechnic University, Hong Kong, China c Shenzhen Key Laboratory of Built Environment Optimization, School of Architecture and Urban Planning, Shenzhen University, Shenzhen, China b

A R T I C L E I N F O

A B S T R A C T

Keywords: Public risk governance Bibliographic analysis Content analysis

The interdisciplinary research field of public risk governance (PRG) emerged in response to the demands of integrative approaches to deal with increasingly complex and systemic public risks, providing intellectual sup­ port for policy frameworks, decision procedures, and institutional arrangements. The research on PRG expanded rapidly, deriving a wide range of interrelated concepts and theories; and growing into an arena of diverse knowledge domains. However, the disparate ontological and discoursal systems by scholars can hinder inter­ disciplinary dialogue, and impede practical implications. The study conducted an integrative analysis of current scholarly research to build a shared interdisciplinary ontology of PRG. A mixed-method design was employed, combining bibliographic analysis, unsupervised text-mining, and in-depth content analysis, to examine the scholarly research extracted from the Web of Science Core Collection database. The findings provided a holistic map of the PRG research across time and spatial distribution, identifying how diverse knowledge domains emerged, evolved, diffused, and integrated into the present PRG research realm. The future research agendas to which the PRG research field might move forward were also discussed.

1. Introduction Conventional risk analysis techniques that calculate possibilities and consequences have limitations in handling systemic risks [1]. It is especially true for public risks, the subjects are general public, whereas the governors are institutions and agencies. Distinct from individual or commercial risks, public risk is defined as the risks that are “broad­ ly-distributed, often temporally remote, and largely beyond individual risk bearer’s direct understandings” [2]; p. 259). It covers a variety of natural and man-made events, such as climate change, catastrophic di­ sasters, air pollution, terrorist attacks, epidemic diseases, and food se­ curity, which can hardly be controlled by individuals or single organizations. Notably, some cross-boundary risks emerging from complex interconnected systems require a systemic approach by considering multiple expertise and stakeholder perspectives [3]. Thus, an interdisciplinary research field on public risk governance (PRG) driven by internal and external stressors has emerged to respond to emerging challenges. The concept of governance originated from political and social sci­ ences and has proliferated across many disciplines, including risk

sciences [4]. Influenced by governance theory, risk governance focuses on the constraints and guidance of collective activities to integrate multiple expertise and perspectives in dealing with systemic risks [5]. Public risk issues go beyond the simple utility functions of benefits and costs. It requires broader considerations of transparent decision-making, deliberative participation, and institutional arrangements, which fall into the research scope of PRG. The research body has received expanded popularity and experi­ enced an explosive development in numbers in recent years. The complexity and interconnected nature of cross-scale risks do not adhere to disciplinary boundaries [6]. Numerous studies were published in diverse disciplines, generating disparate knowledge domains. The di­ versity in methodologies and epistemologies creates scientific pluralism. Nevertheless, the ontological discrepancy and disparate discourses bring theoretical tensions and hinder effective transboundary research. Thus, a commensurability of current PRG knowledge is in need to bridge disciplinary gaps. The present study aims to synthesize existing scholarly research, which were scattered around disciplines, to build an integra­ tive ontology of the PRG research field for informed and constructive dialogue across disciplines.

* Corresponding author. E-mail addresses: [email protected] (X. Lin), [email protected] (H. Zhang), [email protected] (H. Wu), [email protected] (D. Cui). https://doi.org/10.1016/j.ijdrr.2019.101365 Received 29 May 2019; Received in revised form 10 October 2019; Accepted 11 October 2019 Available online 17 October 2019 2212-4209/© 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Xue Lin, International Journal of Disaster Risk Reduction, https://doi.org/10.1016/j.ijdrr.2019.101365

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Presently, several attempts have been made to review the literature on the governance of specific risks. For instance, review articles have been written about climate change adaptation [7–10], food security governance [11,12] and environmental governance [13]. However, how diverse knowledge domains emerge and interact to form the entire interdisciplinary PRG research realm is still underexplored. Knowledge evolution processes are often dynamic and invisible, which makes them difficult to track directly. The present study has adopted a bibliographic analysis to investigate the research develop­ ment footprints indirectly [14,15]. Analysis of bibliographic data can produce relatively unbiased results; however, such analysis can hardly provide in-depth views [16]. We have also combined a full-text content analysis to provide a supplementary rigorous and profound analysis. The findings show visualized maps of how diverse knowledge domains have emerged and interacted over time and across knowledge domains. The results provide an integrative view of the interrelationships among otherwise independent knowledge domains spread in the PRG field, creating a shared ontology for cross-disciplinary dialogue.

and reviews) published from 1993 to 2018. The results covered a variety of interdisciplinary subjects according to the Web of Science Categories (Fig. 1). The majority of the extracted papers fell into the disciplines of public administration, environmental/ecological science, and environ­ mental studies. The distribution of subjects shows the genes of the PRG research field under study. The final dataset also encompassed the collectively cited 67,646 references, which extended the document types, including articles, reviews, book chapters, and proceeding papers. 2.2. Computer-assisted bibliographic analysis The central part of data analysis was the document co-citation analysis (DCA) performed by a widely used bibliographic mapping software CiteSpace developed by Chen [21]. DCA has a specialty in detecting critical points in the scientific development route, exploring emerging trends and research clusters [22]. We were able to identify the milestones and transformative points in the knowledge evolutionary history, as well as the major knowledge domains through DCA. A document co-citation network was built based on the co-citation activities between the 67,646 collectively cited documents in the data. The nodes in the network were the top 50 most co-cited documents per slice (set as two years). The documents were linked based on the fre­ quencies that they were co-cited by same research papers in the dataset. Thus, the link strengths show the connectivity of the documents in the research development history. In this study, the centrality metric was used to identify the degrees of importance of documents in the knowl­ edge development path. Also, the clustering algorithm was adopted to explore diverse knowledge domains based on co-citation cohesiveness. The analysis unfolded in three steps: First, the documents with high co-citation frequency were identified as intellectual milestones in the knowledge development path. Cocitation frequencies were calculated by the number of times that a reference was co-cited with all other references in the dataset. Widely co-cited documents mostly had significant intellectual contributions in influencing research development paths [23]. The co-citation frequency of node Xi is the sum of weights of its connected links: n P wij , wij is the number of times that node Xi was co-cited FðXi Þ ¼

2. Research methods This study combined computer-assisted bibliographic analysis, un­ supervised text-mining, and manual content analysis to visualize and explore the scientific evolution and knowledge diffusion of the PRG research field. The mixed-method design can increase the breadth and depth of the review and reduce personal biases:(1) bibliographic anal­ ysis focuses on evaluative elements (author, journal and affiliation), knowledge entities (keyword, topics and subject categories) and struc­ tural associations between them (citing, being cited, co-citation and cooccurrence) [17,18]; and (2) text-mining and content analysis can further reveal detailed knowledge in full texts [19,20], thus providing a necessary complement to bibliographic analysis. The research process is discussed as follows. 2.1. Collection of bibliographic data Data collection entails retrieving detailed bibliographic records of relevant research literature, including titles, authors, sources, subjects, funding projects, publication year, keywords, abstract, and cited refer­ ences. The Web of Science Core Collection was selected as the database, which contains scholarly research of more than 250 subjects published since 1990. The theme search was adopted by a Boolean operator combining commonly observed terms representing PRG. The filters based on document types and Web of Science categories were used to refine the search results further and to reduce noises. The search strategy performed was as follows: Database: Web of Science Core Collection, 1900–2018. Topic search: Search by topic including the following strings in the Boolean operator “(PUBLIC EMERGENCY OR PUBLIC CRISIS OR PUB­ LIC RISK OR CATASTROPHE OR DISASTER OR HAZARD) AND GOVERNANCE”1; Results filter: Further filter the search result by document type (ARTICLE and REVIEW) and Web of Science category (ENVIRON­ MENTAL STUDIES, PUBLIC ADMINISTRATION, POLITICAL SCIENCE, PLANNING & DEVELOPMENT, SOCIOLOGY, SOCIAL SCIENCE INTER­ DISCIPLINARY, SOCIAL ISSUES, and SOCIAL WORKS); Final dataset: 1354 research papers and 67,646 collectively cited references.2 The search results contained a total of 1354 research papers (articles

j¼1

with Xj ; Second, the documents with high betweenness centralities were identified as the knowledge transition points. Betweenness centrality was usually deployed in DCA to identify pivot points that potentially shifted the development path and drove knowledge evolution [24]. Betweenness centrality measures the extent to which the focal nodes control the flows in the network. Nodes with high betweenness cen­ trality acting as brokers who bridged documents that were otherwise disconnected. The calculation of betweenness centrality was based on the metrics conceptualized by Ref. [25], which is the number of shortest paths that go through the focal nodes divide by the number of all path that go through the focal nodes: CB ðXi Þ ¼

n X n X gjk ðXi Þ ; j < k and i 6¼ j 6¼ k gjk j k

Third, the research clusters in the PRG research were identified based on co-citation cohesiveness. The segregation of knowledge do­ mains formed gradually through citing behaviors during the develop­ ment of the PRG research field. The spectral clustering algorithm in CiteSpace helped to identify the clusters. The automatic labels were calculated on the basis of the terms of document titles by Log-likelihood Ratio (LLR).

1

The searched literature contains the term “governance” and at least one of the terms in the bracket in either title, keywords, or abstract. 2 The search result was based on the database updated by 15th April 2018. The number of publications grew a bit at the end of 2018, but the findings are not significantly changed.

2.3. Unsupervised text-mining and manual content analysis In this section, unsupervised machine text-mining and manual re­ view were combined to explore the research trends in the three frontier 2

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Fig. 1. Top fifteen Web of Science categories of the targeted research papers in the dataset.

areas. Two concepts were adopted, which are intellectual base and research front. Chen [21] noted that the intellectual base of a research domain is a group of highly co-cited documents, whereas the research front was citing articles. Under this definition, full-texts of the docu­ ments in each major area were downloaded and analyzed by a computer-assisted text-mining tool named Leximancer. The knowledge map was generated for each frontier area showing the themes and concepts. As for the research fronts, the citing papers were examined by manual content analysis focusing on the potential research limitations and gaps. We further searched and analyzed the latest publications to investigate the extent to which these limitations have been addressed and what is yet to be studied.

documents (in red at the bottom of the map) published in around 2005, concentrating on ecological risks and natural disasters. Folke, Hahn, Olsson, and Norberg [31] integrated the characteristics of social systems, and proposed an integrative view of adaptive governance of socio-ecological system. A branch of studies were derived focusing on the concepts of vulnerability and resilience, represented by Adger [32] and Folke [33]. Such concepts changed the understandings of equilib­ rium to a dynamic adaptivity under changing environments [34]. The concept stimulates academic enthusiasm to explore and analyze how different levels of entities (e.g. individual, community, society, urban, nation) can absorb, response, and reform under gradual or abrupt changes. These researches integrated social science theories such as psychology and sociology in traditional disaster management research, focusing on collective capacity building. For example, Norris, Stevens, Pfefferbaum, Wyche, and Pfefferbaum [35] as a set of adaptive capac­ ities of communities that form collective readiness and resilience for disasters. Along with the development of the two strands, the PRG research field then evolved into a modern era with expanded research topics including collaborative governance, network governance, resilience, and vulnerability. The research frontier areas (the yellow area on the left) were slowly generated from the interactions of diverse knowledge domains.

3. Results and discussions 3.1. Intellectual milestones Fig. 2 shows the document co-citation network containing 504 nodes with a density of 0.0091. Color represents the publication year of the documents. The network marked by the top ten most co-cited documents shows the development path of the PRG research field. The prevalence of PRG research started at around 2005 (red), increased gradually in approximately 2011 (orange), and proliferated after 2015 (yellow). One of the earliest clusters (a small group in red located in the right upper corner) focused on deliberative participation of citizens in dealing with scientific risks, particularly, nuclear power and genetically modi­ fied food [26,27]. These research integrate political research issues into traditional risk science, which brings focuses such as inequality, power, conflicts, and participatory regime. Renn [4] observed a lack of inte­ gration of socio-political concerns in traditional risk analysis. As shown in the map, the node bridges with a large group of research on the governance of complex risks, such as climate change, disasters, urban catastrophes, and terrorist incidents. Influenced by governance research, the research field started to focus on governance by complex social actors. Ansell and Gash [28] raised the concept of collaborative governance as opposed to adversarial and managerial governance styles, which provided a workable definition and drove the research towards broader discussions on antecedents, implementation, and performance of collaborative governance. A closely located research in the network is by Provan and Kenis [29]. This research focused on how network con­ figurations among governing actors brought different governance per­ formance. A group of research emerged discussing institutional arrangements by using the network governance framework. Moynihan [30] work was co-cited frequently, which pointed out that besides network configuration, the characteristics of processes, actors, and their relationships under-governed are also crucial. Another earliest group is the small cluster with densely co-cited

3.2. Knowledge transition points Besides the milestones that marked the critical works that made notable intellectual contributions, we would also like to know how different domains were driven to connect over the knowledge devel­ opment history. Fig. 3 marks the top ten knowledge transition points. As discussed in the Research Methods section, nodes with high betweenness centralities have critical mediating roles in knowledge transformation and evolution. Although not necessarily frequently co-cited, these doc­ uments mostly raised space for interdisciplinary integration, acting as brokers in knowledge evolution. Interestingly, we also found that a number of the documents with high betweenness centrality are authored by transdisciplinary scholars, who played essential boundary spanners to integrate research communities from different disciplines. One example is Jasanoff [27]; who conducted transdisciplinary research to investigate different politics, policies, and mechanisms to respond to associated risks that come along with advancements in biotechnology. In the network, this node bridged the earlier research on scientific risks to broader political and governance research. Power [36]; based on his observations in accountancy and audit, argued that since real risks were managed by agencies and professionals which could bring secondary risks due to the avoidance of liabilities. Thus, it bridged the research on the duality of primary and secondary risks in risk 3

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Fig. 2. Document co-citation network marked by top ten documents based on co-citation frequency.

Fig. 3. Document co-citation network marked with top ten documents by betweenness centrality. 4

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governance. The increasing regulation could cause “risk colonization,” where regulation processes were defined based on institutional risks faced by regulators to defend their constrained ability to regulate real risks [37]. Johnson and Priest [38] then extended the debate on liabil­ ities of risk bearers and regulators in the policy strategies to cope with flood risks in the UK. The knowledge transition points were those who step “out of the zone” and crossed traditional disciplinary boundaries. For example, in environmental science, the limitations of CCA are due to the ecological and physical limits. Adger et al. [39] contended against this view by integrating insights of social science, indicating that adaptive capacities are mainly constrained by social limits, including values, cultures, knowledge, and individual characteristics.

3.4.1. Cluster #0 private actor Fig. 6 shows the knowledge map generated by Leximancer. The bubbles and nodes on the map represent the themes and concepts coded automatically by the text-mining algorithm. Linkages represent the se­ mantic proximity between concepts/themes. The theme hits on the right show a rank of frequency coded from the texts. In this cluster, the top theme is Climate Change Adaptation (CCA), showing that research on engaging private actors mainly focuses on CCA and related risks. The second top theme is Institution, Policy, and Process. The core research issues are policies, institutional arrangements, and processes to engage private actors. Regional and Local Actions is in between the top two themes, showing that the central units of analysis are at the municipal or local levels. The theme Flood and Water Risk is also coded as a topranking theme, showing that flooding risks currently attract the most concerns among all the other types of climate change risks. From the in-depth content analysis of the citing papers in the cluster, the main research gaps raised were identified and summarized in Table 1. Research on the adaptive governance abilities, such as institu­ tional learning [40], governmental reforms [41], and diversified local actions [42] is limited. Due to the increasing complexity of public risks, the inevitable shift to a public-private co-governance system became visible from the research fronts [43,44]. The research gaps called for exploring governance arrangements and approaches to achieve cross-sectoral collaboration. Future research is in need to study effective methods in driving citizen engagement in CCA actions [8,45].

3.3. Research clusters The research domain on PRG diffused while it evolved. Fig. 4 shows the nine clusters identified based on the co-citation cohesiveness. Numbers in front of the labels show the ranking of clusters in terms of sizes. The colors represent the average publication year. The two earliest clusters (in red) are #4 Building Capacity (size ¼ 30, silhouette ¼ 0.986, PY ¼ 2006)3 and #5 Radioactive Waste (size ¼ 25, silhouette ¼ 0.969, PY ¼ 2003). It readmitted the previous findings that management of scientific and ecological risks triggered the development of the PRG research field. Afterward, several branches emerged including #2 Dependent Variable Problem (size ¼ 33, silhouette ¼ 0.825, PY ¼ 2008), #8 Cross-scale Interaction (size ¼ 19, silhouette ¼ 0.888, PY ¼ 2006), #6 Ecosystem Management (size ¼ 21, silhouette ¼ 0.907, PY ¼ 2006), and #10 Integrating Public Participation (size ¼ 14, silhouette ¼ 0.915, PY ¼ 2008). Through interactions of these branches, three frontier areas were derived, which are #0 Private Actor (size ¼ 54, silhouette ¼ 0.832, PY ¼ 2011), #1 Managing Disaster Network (size ¼ 38, silhou­ ette ¼ 0.824, PY ¼ 2009), and #7 Measuring Resilience (size ¼ 20, silhouette ¼ 0.864, PY ¼ 2011). The first two are the first- and secondlargest clusters, whereas the latter has received intense research atten­ tion recently despite not being prominent in size. The following part of this article will focus on the three frontier areas to explore the research trends and future agenda in the PRG research. Fig. 5 shows the timeline view of the co-citation links within and across clusters over time. It shows that the Cluster #0 Private Actor started in 2005 and has continued to be active until recently, with intense co-citations within the cluster. Cluster #1 Managing Disaster Network appears with a number of highly co-cited documents (large nodes) and was passionately co-cited before 2013, then became rela­ tively inactive afterward. Another frontier area, Cluster #7 Measuring Resilience, has a few highly co-cited works. Compared with other clus­ ters that have active internal co-citations, this cluster has more crosscluster co-citation links. It reveals that this cluster receives more inter­ disciplinary attention than the other two.

3.4.2. Cluster #1 Managing Disaster Network The second frontier area is labelled as Managing Disaster Network (Fig. 7). In this cluster, the most frequent theme is the Organisational Network, showing a research concentration on institutional structures. From tracing the links that radiated from the theme, we can see several research topics, including network governance, network forms, network actions, and organization control. The theme that comes alongside is Collaborative Governance. One important performance criterion for network governance is to improve collaboration. To look back at Fig. 2, documents on collaborative governance are located closely with network governance documents. The map also shows the pivots that link research themes. From the map, Network Governance is associated with Relationships and Communication, between which the link is Social Ca­ pacity. The research topics radiate from the second top theme CrossSector Interaction including public participation, local support, disaster management, and complex system, which are the key research topics of disaster network management. Table 2 shows the research fronts in Cluster #1. Bryson et al. [46] suggested that networks can be used to analyze the antecedents, pro­ cesses, and structures of collaborative governance. Network configura­ tions provide a graphic perspective to explain complex relationships in collaborations [48]. As recommended, additional studies should be conducted to analyze different network structures and effectiveness under different socio-political contexts [47,51]. Furthermore, the con­ nections of network features with governance outcomes, such as collaboration, efficiency, and accountability, requires further studies [48,50]. Instead of staying in a static view, the evolution of governance networks and their sustainability after disasters also needs to be further explored [49].

3.4. Frontier areas Targeting on the three frontier areas, which were Cluster #0, Cluster #1, and Cluster #7, unsupervised machine text-mining and full-text content analysis were conducted to reveal the intellectual base and research fronts. As introduced in the Research Methods section, Lex­ imancer was employed to analyze the full-texts of the co-cited docu­ ments to generate knowledge maps (intellectual base). Manual content analysis was conducted on citing articles to identify the limitations and gaps (research fronts).

3.4.3. Cluster #7 measuring resilience The third frontier area is related to resilience. Fig. 8 shows that the top theme Environmental Vulnerability covers research topics including vulnerability, climate change, and environmental hazards. The theme of Disaster Reduction is closely connected to Environmental Vulnerability. From the knowledge map, resilience research in the PRG field has a root in environmental and disaster studies. The area is starting to integrate with administrative and political research such as the themes of Gover­ nance and Public Activity. The next frequently coded theme is Adaptative Capacity, which is surrounded by Institutional Infrastructures, Resilience,

3 Size shows the number of documents in the cluster; silhouette scores greater than 0.8 means that the clusters have relatively high homogeneity; PY means average publication years.

5

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Fig. 4. Research clusters labelled based on LLR algorithm.

Fig. 5. Timeline view of the research clusters.

and Community. It demonstrates a research emphasis on adaptive ca­ pacity building. Among all the scales of analysis, community resilience has attracted the most scholarly attention, especially on Water and Health-related risks. The resilience of other scales such as individual, household, regional, and urban are not coded as frequent. With respect to the research fronts (in Table 3), Shim and Kim [55]

recommended developing a dynamic, robust, and accurate measure­ ment for cross-scale resilience. Besides focusing on post-assessment, increased research efforts are needed to investigate procedural resil­ ience, such as linking resilience-building practices with disaster response outcomes [56] and evaluating procedural resilience [53]. Furthermore, interdisciplinary approaches in studying resilience are 6

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Fig. 6. Knowledge map of the intellectual base of Cluster #0.

public and private sectors should share responsibilities and cope with collective risks [57]. However, how to build shared responsibilities re­ mains a territory left to explore. With regard to what roles should be played by public and private actors, there is no consensus in the literature, and with limited discus­ sions on contextual determinants. In climate change practices, local authorities are reported as a dominant actor in the driving seat to steer, particularly in the policy formation process [44]. Nevertheless, gov­ ernments acting as the central authority can obscure the creativity and innovations of bottom-up endeavors [43]. Also, it could cause over-burden of local governments and failures to provide customized and fair services for all communities and citizens at risk [44]. Along with the prevalence of neoliberal policies, transferring state responsibilities to individuals and private sectors has been a rising trend. Responsibilities in risk preparedness, mitigation, response, and recovery are increasingly taken by private actors, non-governmental organiza­ tions, and communities [58]. Engaging individuals and private sectors to be responsible for risk management is advantageous; nevertheless, such actions can lead to undesirable outcomes. If the affected citizens and house owners do not have sufficient knowledge and resources, then transferring responsibilities to them can increase their vulnerability [38]. Although participatory governance is highly appraised, collabo­ rative efforts dominated by powerful actors can intensify biases and inequality if participatory actors were not equipped with due power [59]. Also, in certain risk governance activities, the engagement of in­ dividuals may not bring benefits to themselves. Their efforts could even be counteracted by other individuals [60]. Transferring public re­ sponsibilities to private sectors and citizens is not a simple fit-for-all solution without considering the contextual characteristics. Determining what should be done by whom in coping with public risks is a complicated task. The arrangements should bring efficiency, fairness, and legitimacy that are accepted and committed by multiple actors [61]. It is argued by Mees [44] that a promising alternative is to create a regime for the shared public-private responsibilities, that both sectors decide and implement governance arrangements collaboratively based on local conditions. The responsibility divisions are tailor-crafted by both sectors based on full consideration of risk contexts and char­ acteristics. In such a way, adaptive capacity can be enhanced by

Table 1 Research fronts of Cluster #0. Author (year)

Suggested research limitations

Bosomworth [40] Howes et al. [41] Dilling et al. [42] Hegger et al. [8] Klein et al. [43]

Institutional learning of public sectors

Mees [44] Mees et al. [45]

Governmental reforms for collaboration Actions by local governments under certain types of hazards Ways to engage residents in CCA Approaches to drive the shift from public responsibility to public-private responsibility Governance arrangements for public-private responsibilities An advanced framework for citizen co-production in CCA

recommended. According to Berkes and Ross [52]; community resil­ ience drove the integration of individual and group psychology with existing research on socio-ecological systems. However, in the PRG study, compared with strong rooting in ecology and disaster studies, resilience research on social science aspects is still a short slab. 4. Future research agenda Since the bibliographic analysis was based on historical citing be­ haviors, there could be some latest studies were excluded from the data. We further searched and analyzed recently published literature (from 2017 to 2019). The objective was to find out the extent to which the identified limitations from the research fronts had been addressed, how many problems had been solved, and what had been left to be further explored. After investigating the latest progress, we then conclude with three future agendas. 4.1. Mechanism for shared responsibility Proper responsibility division is essential for effective collaboration among multiple sectors in handling public risk issues [54]. Re­ sponsibility is not coded as a frequent concept in the knowledge maps, although it is crucial. It has been widely supported and advocated that 7

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Fig. 7. Knowledge map of the intellectual base of Cluster #1.

network metrics, centrality received the most popularity in studying governance networks. Research shows contradictory findings in terms of whether a centralized or decentralized network is preferred. Kapucu and Garayev [66] compared two emergency management systems in the US and concluded that a decentralized network such as emergency support function (ESF) is superior in flexibility and collaboration. In addition, a centralized system like incident command system (ICS) has higher effi­ ciency with clear roles and responsibilities. However, this conclusion is hardly generalized. Decentralized governance does not always bring collaboration in hierarchical and authoritative systems, and a central­ ized network may also fail to respond efficiently under certain condi­ tions. In Uganda, Maes et al. [67] argued that the loose structure of decentralized platforms as proposed by Hyogo and Sendai Frameworks brought blame resolution and further increased vulnerability in the highly hierarchical system. Jovita, Nurmandi, Mutiarin, and Purnomo [68] verified the failure of a centralized network in crisis response to Typhoon Washi within a region in the Philippines. Also, it was demon­ strated by Hermansson [69] that collaboration could also be built in a highly centralized network due to the specific political and cultural environment in Turkey. Therefore, we argue that contextual factors are essential to determine the effective and favorable network governance structures. The investigation of network structure should be extended from centrality. Structural reforms have been implemented in various places over the world to build capacity in governing public affairs. Nowell, Steelman, Velez, and Yang [70] proposed the optimal type of gover­ nance as a core-periphery structure, which is neither characterized as centralized nor spread. Directly linking a structural pattern with governance outcomes is over-simplified. Thus, the adoption of network governance must be rectified. As Moynihan [30] stated that “the benefit of a network governance perspective is to move the debate beyond centralized versus collaborative approaches” (p.910). Research should focus on the functionality of a widening array of governance structures, which informs different political, administrative, cultural, and social constraints. Comparative studies are valuable in explaining why certain networks work in one condition but fail in another? And how do

Table 2 Research front of Cluster #1. Author (year)

Suggested research limitations

Bryson, Crosby, and Stone [46] Cheong [47]

Antecedents, process, structures, and outcomes of collaborative network Decentralization and centralization in local disaster management activities Multiplex governance structure

Kapucu and Demiroz [48] Kapucu, Garayev, and Wang [49] Koliba, Mills, and Zia [50] Vasavada [51]

Measurements of network sustainability Hybrid accountability regimes for the trade-off between bureaucracy and collaborative accountability Network structures in developing countries

governors to flexibly adjust their roles and interactions for achieving common objectives. Advanced knowledge in how shared responsibilities among multiple actors could be built is still lacking. Empirical studies are in demand to exploring how multiple sectors overcome contextual constraints and collaboratively deal with uncertainties and unpredictability. Besides, accountability regime is essential prerequisite for the de­ livery of collective actions, especially when governance structures are complex [50,62]. In social systems, collaborations are not simple to achieve because of power imbalance, self-interests, conflicts, and reluctance. Further studies on building formal and informal account­ ability instruments are needed to improve multi-actor governance effi­ ciency. For example, the role of the general public becomes relevant in governing public risks in today’s Internet era [63–65]. 4.2. Diversified network governance The research fronts revealed a limitation in understanding how network structures function in different cultural and political contexts. From a theoretical perspective, the configurations of governance net­ works differ in functionalities and constraints [29]. Among all the 8

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Fig. 8. Knowledge map of the intellectual base of Cluster #7.

4.3. Situated and interdisciplinary resilience

Table 3 Research fronts of Cluster #7. Author (year)

Suggested research limitations

Berkes and Ross [52] Daly, Ninglekhu, Hollenbach, Duyne Barenstein, and Nguyen [56] Davies, Robinson, and Ericksen [53]

Integration of social science concepts The differences between resilience building efforts and actual response outcomes

Lane and Hesselman [54] Shim and Kim [55]

Rooted in the ontology of complex systems, resilience provides an important intellectual platform for PRG research, especially in responding to coupled and systemic risks [73]. The concept has been found to have supplementary features to the traditional risk analysis approaches: 1) Resilience highlights the unanticipated nature of risks to prepare for non-predictable hazards instead of evaluating potential consequences based on known causalities; 2) It also drives the changes from ad hoc responses to capacity building of systems, bringing long-term adaptivity toward hazards, especially for those risks with low probability but severe consequences. 3) It interprets risks as external abruptions to be adapted to and aimed for long term persistence of systems under a turbulent environment. Resilience approach has been widely adopted in PRG practices and policies, especially in disaster risk reduction actions. However, besides being used as a metaphorical and heuristic term, the conceptualization of resilience is relatively vague and has limited implications on resilience building and implementation processes [74,75]. How resilience can bring actual governance capacity to adapt and cope with unknown catastrophic hazards still needs further studies. According to IRGC Guideline for Resilience (2016),4 Resilience in­ forms the characterizations of different scales of systems, from in­ dividuals, organizations, societies, to complex hybrid systems, to maintain core functions after hazardous disturbances, or transform into an advanced system that can be immune to certain disturbances. Resil­ ience characteristics have been discussed by scholars, such as flexibility, redundancy, social capacity, participatory regime, and shared

Indicators of the resilience development process Multi-level cross-sector governance system A robust and accurate resilience indicator system

different societies overcome existing constraints and generate collabo­ ration and group efficiency? Social network analysis (SNA) may still be a dominant approach that researchers use to analyze complex governance structures. Network can be used as a framework to depict multiplex and diversified governance structures among multiple actors, including governmental and nongovernmental actors linked by multiplex relationships such as con­ tracts, partnerships, or hierarchy. Advanced network metrics and com­ putations should be applied to integrate the complex, dynamic, and heterogeneous nature of governance structures. In theory, there are still some fundamental questions of network governance raised by Agranoff and Mcguire [71] that remain to be further investigated. For example, the lack of linkage between network structures and governance outcomes. Also, we need to acknowledge the limitations and alternatives of a network perspective in resolving public issues [72]. From a network analytical perspective, some non-structural factors can be neglected. When actors are placed in a network, their intrinsic attributes may be obscured by their structural features [66].

4 IRGC Resource Guide on Resilience Volume 1 (2016) retrieved from https://irgc.org/risk-governance/resilience/irgc-resource-guide-on-resilience/ volume-1/.

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responsibility. However, different from the ecological or engineering systems from which the concept originated, no universally accepted criteria have been agreed to determine favorable status and central functions that should be retained in social systems or systems involving social components [6]. The discussion on resilience cannot be separated from its political and social backgrounds. Research must provide a comprehensive spectrum of features of situated resilience with specific contexts [76]. The results show that measuring resilience has attracted wide attention. Diverse resilience indicators and scales have been deployed in informing policies and practices. Resilience are used as a descriptive, normative, and hybrid concept [77]. Thus, clarifying the ontological basis that was used to build the measurements is necessary. A normative version of resilience comes along with ontological claims about the admissible features of systems, which implying the underlying values and norms that used to construe systems [78]. If a monism of resilience became dominated, the development and implementation of resilience measures could lead to undesirable outcomes [78]. One example is that resilience has been criticized as associated with the governance form of neoliberalism, which is used to provide legitimacy to programs and practices that transfers public responsibilities to private actors [79–81]. Resilience could become a tool for achieving political hegemony on the basis of intentional interpretations [82]. Future research is needed to discuss the implied values behind the various normative measuring in­ struments. Also, developing descriptive measurements to depict the multidimensional or developmental profiles of system resilience is in demand. Resilience research has attracted collaborative contributions across natural and social science disciplines due to its multidisciplinary nature. Comparing the numbers of publications, resilience research receives less popularity in social science disciplines than in natural science disciplines [75]. Resilience has been depoliticized and oversimplified in the current notion of resilience, while neglecting important features that are coherently embedded in social changes, such as inequality, conflicts, power, and agency [76,82]. Also, diverse disciplines have different perspectives and traditions on what constitutes systems, what charac­ teristics infer resilience, and what evidence leads to scientific rigor [83]. The differences constrain interdisciplinary dialogue and success of cross-disciplinary efforts [75]. Providing an interdisciplinary ontology of resilience, which allows scholars from different expertise to collabo­ ratively rectify and enrich the partial understanding of the concept is necessary.

regime for network governance and resilience. Network governance focuses on structural configurations of institutional arrangements, exploring what structures can bring collaborations and resilience. The agenda for situated resilience informs adaptive governance and organ­ isational learning, which is related to collaborative capacity and network structures. The study used bibliographic records to track the knowledge evolu­ tion of PRG research. The limitations in bibliographic analysis can hinder the exhaustivity of the findings. The filtering of categories to achieve the readability and accuracy of the results may cause oversights of some important documents. Due to the filtering strategy, research on cybersecurity, public health, psychological crisis, and several other types of public risks were not identified from the document co-citation network. The study proposed predictions for future directions, but knowledge evolution is still, in all fields, unpredictable. New issues may be generated through increasing interdisciplinary integration, leading to the diffusion of new research agendas. Declarations of interest None. Acknowledgement This research is supported by the National Natural Science Founda­ tion of China (Grant no. 71701090) and the Natural Science Foundation of Guangdong Province (Grant no. 2017A030310357). References [1] O. Renn, A. Klinke, M. van Asselt, Coping with complexity, uncertainty and ambiguity in risk governance: a synthesis, Ambio 40 (2) (2011) 231–246, https:// doi.org/10.1007/s13280-010-0134-0. [2] P.J. May, Addressing public risks: federal earthquake policy design, J. Policy Anal. Manag. 10 (2) (1991) 263–285, https://doi.org/10.2307/3325175. [3] I. Linkov, B.D. Trump, E. Anklam, D. Berube, P. Boisseasu, C. Cummings, Comparative, collaborative, and integrative risk governance for emerging technologies 38 (2) (2018) 170–176, https://doi.org/10.1007/s10669-018-96865. Decisions. [4] O. Renn, Risk Governance: Coping with Uncertainty in a Complex World, Routledge, 2008. [5] A. Klinke, O. Renn, The coming of age of risk governance, Risk Anal. 0 (0) (2019), https://doi.org/10.1111/risa.13383. [6] D.F. Lorenz, The diversity of resilience: contributions from a social science perspective, Nat. Hazards 67 (1) (2013) 7–24, https://doi.org/10.1007/s11069010-9654-y. [7] R. Bierbaum, J.B. Smith, A. Lee, M. Blair, L. Carter, F.S. Chapin, L. Verduzco, A comprehensive review of climate adaptation in the United States: more than before, but less than needed, Mitig. Adapt. Strategies Glob. Change 18 (3) (2013) 361–406, https://doi.org/10.1007/s11027-012-9423-1. [8] D.L.T. Hegger, H.L.P. Mees, P.P.J. Driessen, H.A.C. Runhaar, The roles of residents in climate adaptation: a systematic review in the case of The Netherlands, Environ. Policy Gov. 27 (4) (2017) 336–350, https://doi.org/10.1002/eet.1766. [9] A. Hunt, P. Watkiss, Climate change impacts and adaptation in cities: a review of the literature, Clim. Change 104 (1) (2011) 13–49, https://doi.org/10.1007/ s10584-010-9975-6. [10] M.J. Vink, A. Dewulf, C. Termeer, The role of knowledge and power in climate change adaptation governance: a systematic literature review, Ecol. Soc. 18 (4) (2013) 18, https://doi.org/10.5751/es-05897-180446. [11] J.J.L. Candel, Food security governance: a systematic literature review, Food Secur. 6 (4) (2014) 585–601, https://doi.org/10.1007/s12571-014-0364-2. [12] L.H. Wu, P.P. Liu, Y.X. Lv, X.J. Chen, F.S. Tsai, Social Co-governance for food safety risks, Sustainability 10 (11) (2018) 14, https://doi.org/10.3390/su10114246. [13] R. Plummer, D.R. Armitage, R.C. de Loe, Adaptive comanagement and its relationship to environmental governance, Ecol. Soc. 18 (1) (2013) 15, https://doi. org/10.5751/es-05383-180121. [14] S. Shen, C.X. Cheng, J. Yang, S.L. Yang, Visualized analysis of developing trends and hot topics in natural disaster research, PLoS One 13 (1) (2018) 15, https://doi. org/10.1371/journal.pone.0191250. [15] X.L. Zhang, H. Li, Urban resilience and urban sustainability: what we know and what do not know? Cities 72 (2018) 141–148, https://doi.org/10.1016/j. cities.2017.08.009. [16] J. Yun, L. Jing, J. Yu, H. Huang, A multi-layer text classification framework based on two-level representation model, Expert Syst. Appl. 39 (2) (2012) 2035–2046. [17] J. Choi, S. Yi, K.C. Lee, Analysis of keyword networks in MIS research and implications for predicting knowledge evolution, Inf. Manag. 48 (8) (2011) 371–381, https://doi.org/10.1016/j.im.2011.09.004.

5. Conclusions and implications The diverse knowledge domains show different perspectives that scholars use to approach PRG. Instead of choosing an isolated perspec­ tive, an integrative view of the research field is provided in the present study. A platform for shared ontology of PRG was created by depicting the PRG research territory over its development history and showing the interrelationships of diverse knowledge domains. The findings provided a holistic map of how relevant concepts and theories were generated and interacted. The study helps to bridge the disciplinary divide and build commensurability for cross-disciplinary dialogue. For public agencies and risk analysts, the findings can reduce confusion of the complicated and interrelated discourses and provide a better understanding of the PRG research frontiers. Along with the convergence of the knowledge domains, the three future agendas that proposed in the study are mutually dependent and inherently connected. In the document co-citation network, the three frontier areas are merging through increased co-citations. Intersectional parts among the three areas exist, and each area focuses on different aspects of PRG. The shared objective is to build an effective, sustainable, and flexible governance to respond to complex and systemic risks. The research agenda on shared responsibility emphasizes collaborative ar­ rangements to engage complex actors, which lays the fundamental 10

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