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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