Post on 07-Apr-2023
ORI GIN AL PA PER
Examining collaborative disaster response in China:network perspectives
Xuesong Guo1 • Naim Kapucu2
Received: 28 May 2015 / Accepted: 3 August 2015� Springer Science+Business Media Dordrecht 2015
Abstract Effective disaster response requires well-coordinated efforts among individuals
and agencies. Although collaborative disaster response increases in popularity, little has
been accomplished within the hierarchical, centralized command and control context of
China. This study examined collaborative disaster response in China based on the case of
extraordinary serious cryogenic freezing rain and snow disaster. In addition, public man-
agers were surveyed to investigate network establishment, with preliminary analysis on
whole network using centrality measures. Subsequently, the blockmodel was employed to
discuss the whole network structure followed by analysis on structural holes and inter-
mediaries. Lastly, issues such as obstacles to effective collaboration and propositions
proposed for further research were discussed.
Keywords Collaborative disaster response � Interorganizational networks �Network analysis � Disasters � China
1 Introduction
Recently, China received attention from scholars and practitioners of emergency man-
agement worldwide because of the impact from the following disasters: 1998 Yangtze
River Floods, SARS, Sichuan Earthquake, and 2008 Chinese winter storms (Bai 2008).
Following a top-down model, with the aim to form a comprehensive disaster response
& Naim Kapucukapucu@ucf.edu
Xuesong Guoguoxues1@163.com
1 School of Public Policy and Administration, Xi’an Jiaotong University, Xi’an, Shaanxi,People’s Republic of China
2 School of Public Administration, University of Central Florida, Orlando, FL, USA
123
Nat HazardsDOI 10.1007/s11069-015-1925-1
system (Shan and Chen 2007; Zhang 2003), Chinese government began to update the
traditional single management agency system with new emergency plans and institutional
structures in 2003 (Gao 2008). According to the master emergency plan, the system
includes public sectors, private sectors, and non-governmental organizations (NGOs).
Although issues on collaborative disaster response have been addressed extensively
(Abbasi and Kapucu 2012; Comfort and Kapucu 2006; Hu et al. 2014; Hu and Kapucu
2014; Kapucu 2009; Kapucu and Demiroz 2011; Powley and Nissen 2012), most of the
researches were conducted in the context of western democratic systems. Disaster response
in the hierarchical, centralized command and control context of China is different from
those discussed in the current literature (Col 2007). For example, ‘‘in China’s Communist
system, the single political party exercises great influence over the parallel government
administrations and operates to reduce disagreement’’ in disaster response (Col 2007,
p. 116).
Some researchers have focused on the context from network perspectives, but only
some cases on major catastrophes were discussed (Kapucu 2011; Liu and Xiang 2005,
2006). For example, Liu and Xiang (2005) argued the availability concerning the Chinese
model and suggested flexible organizational structures for better performance. Kapucu
(2011) examined disaster response in the Sichuan earthquake and recommended further
researches using network analysis methods.
On the other hand, networks play important roles as policy instruments when dealing
with disasters (Hu et al. 2014; Hu and Kapucu 2014; Kapucu and Demiroz 2011; Mitchell
2006), with powerful and important actors identified through centrality measures including
degree, closeness, betweenness, eigenvector, and so on (Hu et al. 2014; Kapucu and
Demiroz 2011). However, networks are ‘‘structures of interdependence involving multiple
organizations’’ (O’Toole 1997, p. 45). Researchers utilizing network analysis tools
emphasize ‘‘linkage and structural properties of types of social relationships’’ (Mandell
and Keast 2007, p. 585) with the caveat that ‘‘every method used in network studies has its
pros and cons’’ (Kapucu and Demiroz 2011, p. 552).
In the research, we assume the overall structure of network should be addressed besides
for particular actors and introduced blockmodel to facilitate analysis. Aside from con-
ventional analysis on powerful and important actors, the following issues were discussed:
(a) specification of structural position (core, etc.); (b) identification of the actors in each
position and temporal shifts in those locations; and (c) structural relations among positions
(exploitative links between core and periphery).
This study builds on and contributes to earlier studies on collaborative disaster response.
Although earlier studies discussed issues revolving around policy tools for enhancing
collaborative disaster response (Haveman et al. 2005; Pine 2004; Shaw and Harrald 2004),
other scholars recommended additional systematic researches in different contexts
(Comfort and Kapucu 2006; Kapucu 2011, 2012b; Liao 2012). This study provides
additional insight on the understudied collaborative disaster response issues in the context
of communist system of China. Alongside analysis of centrality measures, the study
examined the structural properties of interorganizational networks and surfacing issues,
such as obstacles to effective collaboration. The theoretical insight from a blockmodel
based on social equivalence provides another contribution in methodology.
1.1 Literature review and background
This section provides brief information on disaster response along with the use of network
analysis and emergency management.
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1.2 Models of disaster response
In close association with civil defense, military doctrines were applied to develop the
command and control model for emergency response earlier. Command and control sys-
tems are defined as ‘‘the exercise of authority and direction by a properly designated
commander over assigned and attached forces in the accomplishment of the mission’’
(Alberts and Hayes 2006, p. 318). It can be regarded as a prototypical example of classical
management thinking (Buck et al. 2006). In this system, decision making is temporarily
centralized and functionally specialized to ensure resources, task allocation, and emphasis
on efficiency and effectiveness (Alexander 2008). The model envisions a strict division
between those who decide and control (policy and management) along with those who act
and execute (frontline responders). The management’s role is to collect information from
the field, plan, forecast, coordinate, and control, while frontline units provide operational
information to higher echelon decision makers and follow orders from above (Drabek and
McEntire 2003).
In the 1960s and 1970s, researchers argued traditional command and control models
were inappropriate for managing large-scale disasters (Dynes and Quarantelli 1969). Dynes
and Quarantelli (1969) claimed these events are highly dynamic and complex, so flexibility
and initiative among the participating organizations is required. In opposition to command
and control model, Quarantelli (1988) and Dynes (1994) proposed the collaborative model,
in which no artificial authority structure is created apart from the structure of the pre-
emergency authority. Recently, this model received more attention (Comfort and Kapucu
2006; Kapucu 2009). Emergency management tends to cross-jurisdictional boundaries due
to the broad geographic scope and range of activities (Hermann and Dayton 2009). Mul-
tiple regional local government agencies, including emergency management, law
enforcement, transportation, public health, housing and welfare, and NGOs, are involved in
the disaster response (Simo and Bies 2007).
Despite the recent developments, challenges and controversies still exist. The com-
plexity of the mobilization raised important questions on management and leadership,
especially within emergency management networks (Waugh and Streib 2006). Further-
more, the difficulties in leadership are accentuated given the network’s diversity (Waugh
and Tierney 2007). Recent research from Florida and Louisiana illustrated this complexity
(Kapucu 2008). Actors from diverse sectors and policy areas may have a variety of
assumptions about the nature of emergencies, appropriate forms of coordination, com-
munication, and cognition (Comfort 2007). It is natural to wonder whether the volatility
(the focus of the previous work on issue networks) varies by organization. Moreover, many
studies underemphasized the complexity of coordination processes operating within net-
works over time (Herranz 2010). Powley and Nissen (2012) also argued trust and orga-
nizational design influences strong interactions. Especially in situations where managers
deal with threat assessment without the benefits of high trust levels, they must strive to
create or maintain hierarchy forms.
As mentioned earlier, collaborative disaster response has been addressed extensively, in
the context of western democratic systems. Disaster response systems in the hierarchical,
centralized command and control context of China are very different from those discussed
in the current literature (Col 2007). According to Col (2007), US local governments rely on
FEMA, and Chinese local governments rely on the Ministry of Civil Affairs. ‘‘In China’s
Communist system, the single political party exercises great influence over the parallel
government administrations and operates to reduce disagreement’’ in disaster response
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(Col 2007, p. 116). Some researchers have focused on the context of China (Kapucu 2011;
Liu and Xiang 2005, 2006), but only some major catastrophes, such as Tangshan earth-
quake (Col 2007), Sichuan earthquake (Kapucu 2011), and so on, were discussed. Similar
controversies exist in this context. While some scholars confirm the strengths of the sys-
tem, and believe effectiveness and efficiency can be improved following the current
command and control model (Gao 2008; Shan and Chen 2007), Liu and Xiang (2005)
argued the availability concerning the Chinese model and suggested flexible organizational
structures for better performance.
1.3 Network analysis perspectives
Network analysis is an alternative method to address issues on collaborative disaster
response (Abbasi and Kapucu 2012; Comfort 1994, 1999; Hu et al. 2014; Hu and Kapucu
2014; Kapucu 2009). Comfort (1994) argued emergency management networks can be
understood as self-organizing systems, which is an important corrective to the assumptions
of central planning. According to Comfort (1994), mobilization involves a set of actors
who vary in terms of prior disaster experiences, organizational sectors, and other char-
acteristics. Hence, sufficient structure to hold and exchange information, sufficient flexi-
bility to adjust behavior to dynamic changes, shared goal among participants, recurring
opportunities for interactions, and capacity for integrating information are vital to facilitate
self-organization (Comfort 1999).
Kapucu (2009) studied the Federal Response Plan (FRP), the National Response Plan
(NRP), and the National Response Framework (NRF) from the perspectives of interor-
ganizational networks and complex adaptive systems. Kapucu and Demiroz (2011) dis-
cussed structural differences between the planned networks and actual networks through
identifying important and powerful actors. Subsequently, using Hurricane Charley’s
coordination data, Abbasi and Kapucu (2012) analyzed the evolution of interorganizational
response networks and structural changes over a period of time. In recent studies, Hu and
Kapucu (2014) investigated whether centrality of organizations in emergency management
networks relates to information communication technology utilization. Hu et al. (2014)
identified top organizations within the networks through normalized degree centralities.
Moreover, additional dimensions or concepts, such as cognitive accuracy (Choi and
Brower 2006) and power base (Choi and Kim 2007), were introduced as a theoretical
framework.
1.4 Context of the study
Xi’an, located in central-northwest China, was called as Chang’an (meaning the eternal
city) in ancient times. It is one of the birthplaces of the ancient Chinese civilization in the
Yellow River Basin area. As the start point of Silk Road and the site of the famous
Terracotta Warriors of the Qin Dynasty, the city has won a reputation all over the world.
More than 3000 years of history including over 1100 years as the capital city of ancient
dynasties have endowed the city with an amazing historical heritage. Now, as one of the
biggest metropolises with dense population, Xi’an has become an important business,
political, and transportation center, and disasters in this city will impact Midwest area of
China significantly.
In November 2012, this city encountered extraordinary serious cryogenic freezing rain
and snow disaster, which is the most serious freezing rain in the past 100 years. The results
show that this case is associated with snow storm and anomalous atmospheric circulation.
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The cold air moved down from the northeast side of Tibetan Plateau and produced the low-
level temperature inversion over central China, which caused the continuous cryogenic
freezing rain and snow weather. Especially in the city of Xi’an, Shaanxi Province, the
disaster caused great economic loss and social impacts. Agriculture production, trans-
portation, and so on were impacted during the disaster. When confronting the public events
caused by the freezing rain and snow, the local authority of the city of Xi’an activated
Municipal Natural Disaster Relief Emergency Plan to coordinate disaster response
involving various agencies.
2 Method
Overall, most current research deduces structure of network through identifying powerful
and important actors (Abbasi and Kapucu 2012; Hu et al. 2014; Hu and Kapucu 2014;
Kapucu 2009). Each analytically separable type of relationship must be taken into con-
sideration (White et al. 1976). According to White et al. (1976), the general theme is the
actors’ behaviors being influenced by positions in a social structure is venerable, and has
generated various methods to represent structure and position empirically.
In the research, we introduced blockmodel to facilitate analysis on structural properties
of network. Motivated by the seminal work on structural equivalence (Lorrain and White
1971), this method incorporates statistical procedures for clustering or ‘‘blocking’’ rela-
tional data (blockmodel). Up to now, it has been largely applied to network analysis
(Doreian et al. 2005; Newman 2012; Peixoto 2014; Zhao et al. 2011). Besides the
advantages demonstrated by White et al. (1976), one reason behind the preference of
blockmodel analysis to alternative methods is it constitutes more than simply a technique.
In providing concrete statements concerning ‘‘structure,’’ ‘‘position,’’ ‘‘role,’’ and relations
among these constructs, blockmodel analysis contains the elements for a formal (though
still very abstract) theory of social structure (Scott and Carrington 2011). Positions
(‘‘blocks’’) are aggregates of actors who manifest similar patterns of interaction in network.
This feature begins to differentiate blockmodel analysis from cliques, which consists of
aggregation of relations rather than indispensable attributes to blockmodels, with more
details seen in White et al. (1976).
In terms of research methodology, analysis of blockmodel and central actors is com-
plementary. The research procedures included a two-tiered process. The first consisted of
analysis through centrality measures using density, degree centrality, and betweenness
centrality. Density suggests the average linkages among organizations (Kapucu et al.
2014). In a network with higher density, organizations interact more frequently and
achieve more effective coordination. Basic assumption of degree centrality is the more
connections an actor has the more powerful and important the actor will be (Analytic
Technologies 2008; Borgatti et al. 2013). A degree of node is the number of nodes adjacent
to it (Scott and Carrington 2011). Actors who display high out-degree are often said to be
influential actors (Wasserman 1994). Betweenness is another basic concept of centrality
(Freeman 1979). This concept measures the extent to which a particular node lies ‘‘be-
tween’’ the various other nodes. A node of relatively low degree may play an important
‘‘intermediary’’ role and be very central to the network (Scott and Carrington 2011).
Betweenness centrality, by contrast, reveals the positional power of actor if it provides
communication linkage between two other actors or two subgroups, and its nonexistence
might cause a serious communication breakdown (Comfort and Haase 2006).
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The second tier consisted of analysis on structural properties. We investigated the
structure of network using blockmodel, which is a view of social structure. Blockmodel is
obtained directly from aggregation of the relational data without imposing any prior cat-
egories or attributes for actors (White et al. 1976). In the research, we consider the general
logic, procedures, and substantive utility of blockmodel are sufficiently established and
need no further discussion (Lorrain and White 1971; Wasserman and Galaskiewicz 1994;
White et al. 1976). The identified participants with similar patterns can be analyzed
through blockmodel. Furthermore, we discussed issues on coordination by analyzing
structural holes and intermediary (Burt 1992). According to the theory on structural hole by
Burt (1992), the advantage of an actor in a network is based on its control over the links
between him as an ego and his alters as well as between the alters. ‘‘A structural hole exists
where two points are connected at distance 2, but are otherwise separated by a long path’’
(Scott 2013, p. 87). An actor bridging such a structural hole has a position of advantage. In
other words, the actor might constrain the other actors. Networks rich in structural holes
imply access to mutually unconnected partners and many distinct information flows.
2.1 Data collection
Disasters in metropolitan areas impact the surrounding areas and will trigger massive
collaborative disaster response (Kapucu 2012a). We examined collaborative disaster
response in China based on the case of extraordinary serious cryogenic freezing rain and
snow disaster to facilitate argument on collaborative disaster response in China. This
disaster, which impacted Xi’an, China, in November 2012, is rare according to the regional
meteorological records.
First, we determined candidates of participating organizations through Municipal Nat-
ural Disaster Relief Emergency Plan (XAEP), which was activated to coordinate the
response, along with several experts’ suggestions. In the current emergency management
system of China, emergency plan is not only the guidance, but also the legal document for
disaster response coordination and accountability identified by governmental bodies (Gao
2008). Emergency management plans are the most authoritative source for actor identifi-
cation. This is the main reason why we surveyed the listed agencies in XAEP.
Second, we identified actual participants and collected data for network establishment
through a survey assisted by local emergency management office. In the survey, respon-
dents consisting of executive directors or managers were assumed to have access to the
most accurate information on their organizations’ activities along with the authority to
make decisions. We sent questionnaires consisting of two sections. Respondents were
asked whether they were engaged in actual response in the first section. If they chose
‘‘yes,’’ then they were required to identify organizations they cooperated within the second
section. Every respondent sent back the questionnaire with a 100 % response rate.
Hence, participants in actual response were identified based on feedbacks from the first
sections, and network was established according to data from the second sections.
3 Results and discussion
Based on the data from survey, 17 organizations were identified as participants: Municipal
Police Department (PD), Municipal Civil Affairs Bureau (CAB), Municipal Finance
Bureau (FB), Electric Power Supply Company (EPSC), Municipal Transportation Bureau
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(TB), Municipal Communication Bureau (CB), Municipal Meteorological Agency (MA),
Municipal Health Bureau (HB), Municipal Agricultural Bureau (AB), Municipal Propa-
ganda Department (MPD), Local Emergency Management Office (EMO), Municipal
Environmental Protection Bureau (EPB), Urban Council (UC), Urban Appearance Bureau
(UAB), Red Cross Society (RCS), Railway Station (RS), and Xian-yang International
Airport (IA).
Subsequently, we obtained an adjacency matrix, as shown in ‘‘Appendix,’’ where ‘‘1’’
denotes a linkage between the organizations and ‘‘0’’ denotes there was no linkage. We
established network using UCINET network analysis software (Borgatti et al. 2002), as
shown in Fig. 1. We calculated density of network (density = 0.4632 and standard
deviation = 0.4986).
We identified the central organizations by calculating centrality (Table 1). The results
indicate EMO, CAB, and TB were the first three prominent and influential organizations
because of much higher degree centrality and centralization.
The results shown in Table 2 indicate the betweenness centrality of EMO, CAB, and TB
is much higher than others. Therefore, more organizations depended on the three partici-
pants (EMO, CAB, and TB). EMO operated as command center in disaster response. CAB
and TB possessed vital resources for response. CAB was in charge of disaster relief funds
allocation. Since the disaster caused serious traffic jam, the resources possessed by TB,
such as snow removal trucks, special traffic equipment, and so on, were vital for traffic
dispersion. This confirms the conclusion derived from Table 1 that EMO, CAB, and TB
have much more power in disaster response. Also, it suggests EMO, CAB, and TB pro-
vided communication linkage between two other actors or subgroups. So, the network was
vulnerable because faults or errors of minority participants would cause a serious com-
munication breakdown.
Furthermore, we discussed the structure of the network using blockmodel. The cluster
diagram is shown in Fig. 2.
Fig. 1 Disaster response network
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Table 1 Degree centrality andcentralization
Network centralization(OutDegree) = 43.750 %
Network centralization(InDegree) = 57.031 %
Organization OutDegree InDegre NrmOurDeg NrmInDeg
EMO 14.000 16.000 87.500 100.000
CAB 14.000 16.000 87.500 100.000
TB 11.000 10.000 68.750 62.500
RS 10.000 6.000 68.750 37.500
UC 8.000 9.000 50.000 56.250
MPD 8.000 8.000 50.000 50.000
IA 8.000 5.000 50.000 31.250
PD 7.000 5.000 43.750 31.250
MA 7.000 7.000 43.750 43.750
UAB 7.000 7.000 43.750 43.750
RCS 6.000 7.000 37.500 43.750
EPSC 5.000 7.000 31.250 43.750
HB 5.000 5.000 31.250 31.250
AB 4.000 4.000 25.000 25.000
EPB 4.000 5.000 25.000 31.250
FB 4.000 4.000 25.000 25.000
CB 3.000 5.000 18.750 31.250
Table 2 Betweenness centralityand centralization
Network CentralizationIndex = 16.36 %
Organizations Betweenness nBetweenness
EMO 45.708 19.045
CAB 45.708 19.045
TB 14.421 6.009
MPD 9.124 3.802
MA 8.150 3.396
RS 6.479 2.700
UC 5.727 2.386
RCS 3.345 1.394
PD 2.877 1.199
UAB 2.480 1.033
IA 2.236 0.932
EPSC 0.900 0.375
AB 0.751 0.313
FB 0.583 0.243
HB 0.343 0.143
EPB 0.167 0.069
CB 0.000 0.000
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As shown in Fig. 2, the organizations can be divided into six blocks. We defined the
blocks as follows: block 1 includes EMO and CAB; block 2 includes AB, FB, HB, and
RCS; block 3 includes CB, MA, and MPD; block 4 includes TB, EPSC, PD, and EPB;
block 5 includes UAB and UC; block 6 includes RS and IA. We obtained matrix, called
image (White et al. 1976), as shown in Eq. 1. The relationships among blocks are illus-
trated in Fig. 3.
Fig. 2 Cluster diagram
Fig. 3 Relationships among blocks
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1 1 1 0 1 1
1 0 0 0 0 0
1 0 1 1 0 0
1 0 1 1 1 0
1 0 0 0 0 1
1 0 0 0 1 1
26666664
37777775
ð1Þ
As shown in Eq. 1 and Fig. 3 above, interactive relationships existed in block 1, block
3, block 4, and block 6. Block 1 was most active in the network, because it interacted with
all other blocks. We also conducted further analysis using indexes on structural holes. The
measures for structural holes developed by Burt (1992) are based on dyadic constraints as
well as redundancy. Dyadic redundancy describes how often a tie between A and B is
redundantly existent by considering further actors. In detail, the indexes discussed in the
research include effective size (EffSize), efficiency (Efficie), constraint (Constra), and
hierarchy (Hierarc), with results shown in Table 3.
Effective size is the number of alters the ego has, minus the average number of ties each
alter has to other alters. Efficiency normalizes the effective size of ego’s network by its
actual size. Hence, the effective size of ego’s network suggests something about ego’s total
impact, and efficiency indicates how much impact ego is getting for each unit invested in
using ties. The results of the two indexes indicate EMO (10.067, 0.629), CAB (10.067,
0.629), TB (6.262, 0.522) were less constrained by other organizations. Constraint is a
summary measure tapping the extent to which ego’s connections are to others who are
connected to one another. According to Burt (1992, p. 54), the idea of constraint is an
Table 3 Results on structuralholes analysis
Organization EffSize Efficie Constra Hierarc
EMO 10.067 0.629 0.229 0.063
PD 2.292 0.327 0.326 0.031
CAB 10.067 0.629 0.299 0.064
FB 1.500 0.375 0.431 0.024
EPSC 2.458 0.307 0.324 0.053
TB 6.262 0.522 0.261 0.063
CB 1.750 0.350 0.371 0.060
MA 4.357 0.436 0.291 0.098
HB 1.600 0.267 0.369 0.051
AB 1.750 0.350 0.398 0.003
MPD 4.000 0.500 0.397 0.027
RCS 3.077 0.440 0.319 0.053
UC 3.588 0.399 0.317 0.031
UAB 2.875 0.357 0.334 0.033
EPB 1.667 0.333 0.370 0.048
RS 6.088 0.507 0.375 0.053
IA 4.077 0.408 0.306 0.052
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important one because it points out actors who have many ties to others may actually lose
freedom of action rather than gain it. The results of the index indicate AB (0.398) was more
constrained and dependent on other organizations, whereas EMO (0.229), CAB (0.299),
TB (0.261), and MA (0.291) have constraints spread in the network. In terms of the case, it
also means a spread of disruption. Faults or errors of the organizations (EMO, CAB, TB,
and MA) may cause a breakdown of the network. The hierarchy measures the important
property of dependency (inequality in the distribution of constraints on ego across alters in
its neighborhood). The results on this index suggest EMO (0.063), CAB (0.064), and TB
(0.063) were in the central positions. Moreover, the results on intermediary are presented in
Table 4.
Having more structural holes, EMO, CAB, and TB acted as liaisons, which coordinated
organizations in different blocks. Since MPD did not have structure hole, it was one of the
organizations acting as coordinator, that is, MPD acted as information transfer in the block.
Moreover, TB acted as representative for 10 times, indicating it transferred information
from one block to others more frequently. According to the results mentioned above, we
can discuss the structure of network further, as shown in Fig. 4.
Although diverse organizations were involved in disaster response, their positions were
different. In this case, three organizations (EMO, CAB, and TB) were in core positions
controlling main vital resources with more power. Agriculture production was impacted
seriously, but AB was on the periphery. In block 2, only FB interacted with it, because AB
did not possess the funds for disaster relief. Hence, it cannot begin disaster relief and
recovery in agriculture production unless FB provided the funds. This also implies
Table 4 Results of intermediary analysis
Organization Coordinat Gatekeepe Represent Consultan Liaison Total
EMO 0 0 0 17 108 125
CAB 0 0 0 17 108 125
AB 0 1 1 0 0 2
FB 2 0 0 0 0 2
HB 0 0 1 0 1 2
RCS 2 4 3 0 2 11
CB 0 0 0 0 0 0
MA 0 3 2 0 15 20
MPD 2 5 6 0 11 24
TB 1 2 10 1 33 47
EPSC 0 0 0 0 4 4
PD 0 0 0 0 8 8
EPB 0 0 0 0 1 1
UAB 0 1 0 7 3 11
UC 0 3 1 9 6 19
RS 0 1 3 5 18 27
IA 0 0 0 2 9 11
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mismatch between actual responsibilities of organizations and their functions in disaster
response. Similarly, EPB was isolated from other ones, although its task of environment
monitoring was very important during disaster. Hence, the results indicate resources and
tasks were difficult to allocate and use efficiently and effectively.
Originally, Chinese governance structure was established according to the traditional
functional format to facilitate hierarchical, centralized control and command. Each orga-
nization is affiliated to a certain centralized hierarchical/bureaucratic system, which
operates in a hierarchical structure and following orders from central government, as
shown in Fig. 5a. As executive units of central government, agencies are divided into
several systems according to their function definitions, such as police affairs, civil affairs,
and railway transportation. In this case, except for organizations from Local Municipal
Government (LMG), MPD is an agency of Local Communist Party Committee (LCPC),
RS is a branch of National Railway Company (NRC), and IA is affiliated to National Civil
Aviation Bureau (NCAB). So, the centralized hierarchical/bureaucratic system can be
regarded as structure facilitating central governance.
However, the governance structure has to be changed according to the principle of
‘‘Territorial Jurisdiction’’ in disaster response (Gao 2008), as shown in Fig. 5b. That is,
agencies affiliated to different systems are required to be engaged in collaborative disaster
response temporarily, see details in Organizations for Collaborative Disaster Response in
Fig. 5b. But, the mechanisms of different systems, such as administrative regulations,
working procedures, and standards of information system, are different from each other
due to current vertical management structure. Therefore, it is difficult to accomplish
cooperation across boundaries in the context of disaster.
We identified EMO, CAB, and TB as key actors based on the results of network
analysis. Although EMO and CAB were liaisons, it was difficult for them to interact with
RS and IA. TB, RS, and IA are all organizations responsible for transportation, but they
were not in the same block. TB is an agency of local authority, while RS and IA are central
state-owned enterprises (RS and IA are in charge of central government). So, it is hard to
Fig. 4 Structure of disaster response network
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say that they (EMO, CAB, and TB) were lead organizations due to the lack of effective
interactions with other actors. The results also suggest gaps between local authority and
central government in disaster response. In conclusion, it is difficult to facilitate collab-
orative disaster response following current command and control model. Actually, the
obstacles to effective collaboration just root in this model.
In particular, as an agency of LCPC, MPD transferred information between organiza-
tions in LMG. This perspective supports the proposition ‘‘in China’s Communist System,
the single political party exercise great influence over the parallel government adminis-
trations’’ (Col 2007, p. 116). It also implies that LCPC is an important or even lead
organization in actual disaster response, although few agencies of LCPC are listed in
Actor E
Actor F
Actor G
Actor H
Actor I
System 2
Actor B Actor C
Actor A
Actor D
System1 System 3
Central Government
(a)
Actor E
Actor F
Actor G
Actor H
Actor I
System 2
Actor B Actor C
Actor A
Actor D
System 1
Organizations for Collaborative Disaster Response
System 3
Central Government
(b)
Fig. 5 Comparison betweenstructures in routine work anddisaster response. a Governancestructure in routine work.b Governance structure indisaster response
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emergency plan. Actually, LCPC usually ‘‘operates to reduce disagreement’’ (Col 2007,
p. 116) and coordinates disaster response by some means including official appointment,
official accountability, and emergency resource mobilization.
4 Conclusion
Disaster response in China, one of the biggest communist government systems in the
world, is coordinated in a context characterized as hierarchical, centralized command and
control. From a network perspective, this research discussed the interorganizational rela-
tionships in disaster response followed by analysis on the obstacles to effective collabo-
ration. We discovered resources and tasks are encountering issues with allocation,
efficiency, and effective use within the current command and control model. The obstacles
to effective collaboration were also addressed in this paper.
Overall, the contributions of study incorporate the examination of collaborative disaster
response in the context of China and application of blockmodel to facilitate more com-
prehensive analysis and discussion. This research based on individual survey was infor-
mational, yet had some limitations. First, public managers work with so many different
individuals, making it nearly impossible to understand their entire network. Second,
relationships tend to be fluid. While we believe the collected information allows a good
general understanding of collaborative disaster response in China, it is just a snapshot as
relationships will certainly change with passing time. Since only one case was analyzed,
future research should focus on cases ranging from local level to national level. Moreover,
as a hierarchical, centralized command and control model, China’s disaster response
system can be compared with other model variations, such as the USA (Col 2007). Third,
although emergency plan, the most authoritative source in the current emergency man-
agement system of China, was referred to, it is still possible for some organizations not
listed in XAEP, such as NGOs, private sectors, and volunteers, to be missed. So, these
participants need to be evaluated as well. Similar methods can be used in researches on
them. Questionnaires can be sent to managers of NGOs and private sectors for data
collection. Since volunteers are mainly communicated and mobilized via virtual com-
munities in China, we can collect data from Web sites, e.g., historical records, for network
establishment, with some issues including network structure, trust, and so on discussed.
Acknowledgments The research was sponsored by Chinese Major Project of National Social SciencesFund (No. 11&ZD034). We thank editors and anonymous reviewers for their comments and suggestions.
Appendix
See Table 5.
Nat Hazards
123
Table
5A
dja
cency
mat
rix
EM
OP
DC
AB
FB
EP
SC
TB
CB
MA
HB
AB
MP
DR
CS
UC
UA
BE
PB
RS
IA
EM
O0
11
11
11
11
11
11
11
00
PD
10
10
01
00
00
00
11
01
1
CA
B1
10
11
11
11
11
11
11
00
FB
10
10
00
00
01
01
00
00
0
EP
SC
10
10
01
01
00
00
10
00
0
TB
10
10
10
01
11
01
11
01
1
CB
10
10
00
00
00
10
00
00
0
MA
10
10
01
00
00
10
01
01
1
HB
10
10
00
01
00
11
00
00
0
AB
10
11
00
01
00
00
00
00
0
MP
D1
01
00
01
11
00
10
00
11
RC
S1
01
10
10
01
01
00
00
00
UC
11
10
11
00
00
00
01
11
0
UA
B1
11
01
10
00
00
01
01
00
EP
B1
01
00
00
00
00
01
10
00
RS
11
10
11
10
00
11
10
10
1
IA1
01
01
11
00
01
01
00
10
Nat Hazards
123
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