Organizational Learning In Adapting To Dynamic Disaster Environments In Southern Turkey

Abstract

Using a theoretical framework based on complex adaptive systems and organizational learning, the study compares and contrasts the network structures of two disaster response systems following the 2006 avian influenza crisis and the 2011 Van earthquake in Turkey. This study emphasizes the reorganization of Turkish disaster response in 2009 and its impact in response to 2011 Van earthquake. The research utilizes data from content analysis of news reports from the Turkish daily newspapers Cumhuriyet and Sabah from 28 December 2005 to 17 January 2006 for the avian influenza, and Hurriyet from 23 October 2011 to 8 November 2011 for the 2011 Van earthquake, respectively. The research has used social network analysis and small world ratio based on the content analysis data to compare and evaluate the network structures of the two response systems. Findings indicate that the Turkish disaster system, to some degree, learned from the previous disaster and was therefore better managed. However, the system still remained very centralized and multi-sectoral involvement is still weak.

Keywords

Avian influenza earthquake disaster management social network analysis small world analysis organizational learning

Disaster: a challenge for policy-makers

Disasters are times of profound uncertainty and challenges to problem solving capacities of socio-political order (Brändström et al., 2004; Moynihan, 2005). Disasters change the regular flow and order of organizational and societal structure, cause great damage, destruction, and suffering (Islam et al., 2014). The dynamic and complex characteristics of disaster environments compel policy-makers to replace linearly designed policies with more flexible plans and create disaster response systems that can learn from and adapt to their environments.

Linear systems for routine operations

Traditional organizational forms based on linear assumptions are helpful for routine activities during normal times. Linear organizational structure can be represented by the machine metaphor (Morgan, 1997). Command and control along with coordination through hierarchy are common features of this approach. Linear organizational design assumes existence of well-structured problems, limited or no change in external environment, and significantly low uncertainty (Jaffee, 2001).

If an organization or response system operates under low uncertainty, it can make predictions for the short and long run. The control and command oriented system can deal effectively with problems, as long as personnel comply with rules and orders (Weber, 1996). As people especially at lower levels are not expected to use initiative, information flow between organizations and personnel follow hierarchical lines. High level decision-makers or central organizations tend to coordinate resources, people and activities.

Complex characteristics of disaster environments

As disasters are complex and unpredictable events, formally designed structures cannot have sufficient capacity for adaptation to changing circumstances (Butts et al., 2007). Disasters significantly alter the routine conditions in the organizational environments. They damage buildings and technical infrastructure necessary for communication and coordination as well as harm responsible officers and other inhabitants. They involve great time pressure and urgency along with critical resource shortages (Salmon et al., 2011).

High uncertainty requires decreasing the use of formal organizational structures as well as increasing the need for organizational flexibility (Bechky, 2006). The multi-agency, multi-sectoral, and multi-jurisdictional nature of the disaster response creates great challenges for the coordination of organizations (Militello et al., 2007; Salmon et al., 2011). National and international organizations from different jurisdictions and societal sectors, with different work cultures, operate at disaster sites (Comfort, 1999). These response and recovery organizations operate with different authorities, missions, mandates, accountability criteria, and agendas (Bjorn and Olsen, 2012). Moreover, the emerging conditions of disasters bring together the combination of public, private and non-profit organizations that cannot be specified in advance (Leonard and Howitt, 2010).

Traditional management of knowledge and information cannot be optimally configured to support disaster operations (Yates and Paquette, 2011). Routine work life, information flow and decision-making processes easily fail, since technical infrastructure for communication and information processing are degraded or unavailable when a ‘symmetry breaking’ emergency occurs (Kiel, 1994). The limited local response capacity to emerging conditions is partly or totally lost in accordance with the magnitude of the disaster (Schneider, 2005). An important reason for this is that normal assumptions on which decisions are based are no longer valid under new conditions of change and uncertainty. This is especially the case for command and control structures that cannot sufficiently facilitate two-way information processing to help central and local actors in adapting to the new ground conditions. Nohrstedt (2016) argues that collaboration may not operate in even routine emergencies that are foreseeable disturbances which fall within the system’s scope of operations (Comfort et al., 2010).

If linearly designed policies cannot address the problems of disaster environments, then, policy-makers must address questions such as how to coordinate a multi-agency operation for a timely response, or how to develop the capacity for inter-organizational response that can learn from and adapt to the dynamic conditions. The answer is related to the alternative perspectives such as organizational learning and complex adaptive systems.

Organizational learning

Creating a self-adaptive disaster management system is critically related to the human ability to recognize and correct mistakes based on information (Comfort, 2007). A disaster response system can adapt to its environment as long as it learns. Management of information and knowledge is critical for organizational learning, despite that it requires a conscious effort. Wellman (2012) argues that people may simply not choose to share knowledge and try to use it as a power base to get personal advantage. Work routines and procedures can be out of date, or beliefs and experiences that underline the social norm may be irrelevant to the conditions in the environment.

The dynamic relationships among physical, social and constructed systems (Mileti, 1999) make individual and organizational learning necessary for inter-organizational integration (Comfort, 1999). Organizational learning can be described as an interactive collective process of change and adaptation through information processing, improved collective knowledge, and understanding (Corbacioglu and Kapucu, 2006).

The conscious effort requires changing shared mental models by valid and timely information through ongoing dialogue (Arygris and Schön, 1996). Disaster organizations can minimize the vulnerability of local communities, when organizations carry out inquiries into contemporary mental models (Turner and Pidgeon, 1997); this can especially be the case when organizations and communities change their theories in use and act accordingly. Ayrgris and Schön (1996) assert that organizations can change the shared mental models through double-loop learning. Double-loop learning helps organizations and their employees to see the misfit between organizational goals and current policies. It thus facilitates inquiry and change in response to new information and altered conditions. When organizations question their governing values, the appropriateness of policy tools is reconsidered (Birkland, 2009).

Updating individual shared mental models based on the reliable information is critical for taking lessons from the past and correcting errors in disaster affairs. Moreover, valuing information sharing, initiative taking and cooperating with organizations with different work cultures and methods are among important building blocks of a self-adaptive complex network (Corbacioglu et al., 2014).

Complex adaptive response networks

A learning and adaptive response network can integrate critical actors and establish an informed decision-making process underlined by timely information search, exchange, and dissemination. The new design requires a complex organizational policy for the ill-structured problems of disaster environments. Comfort (1999) argues that complex adaptive systems theory provides important insights for developing complex policies for disasters. Axelrod and Cohen (1999) argue that complex adaptive systems can facilitate learning between different units or networks through interactions and information flow. According to Koppen and Klijn, networks can learn when shared knowledge, insights and methods of work sustainably increase between parties (Koppen and Klijn, cited in Moynihan, 2005).

Designing a complex disaster response network requires a balance between order and flexibility (Comfort, 1999). A flexible organization structure is necessary for holding and exchanging information (Kauffman, 1993). As lateral communications and local decision-making are encouraged, the control and command focus of the bureaucratic-linear structure are rejected. Decision-makers at the central level primarily focus on developing system level strategies and policies that can help local decision-makers (Comfort, 1994). The network level behaviors emerge as a result of the interaction between critical actors and sub-groups. While actors make informed decisions and respond to each other, network level self-adaptation occurs.

Kapucu (2006) argues that designing response systems as self-adaptive networks creates flexible and redundant modes of connectivity to distribute the information congestion and minimize the possibility of failure. In contrast to a hierarchical structure, organizations can find alternative ways to reach each other to overcome the problem of any failed node (Kapucu, 2006). Therefore, the sub-groups can retain the communications and information sharing.

A complex adaptive network relies on timely and accurate information. As opposed to control and command oriented bureaucracies, complex systems focus on horizontal communications and informed decision-making. Sufficient information can help a disaster response in dealing with asymmetric knowledge and experience, maintaining mutual understanding, and uneven workload distribution (Militello et al., 2007). Although information sharing in and between organizations is one of the most critical issues for adaptive response systems, this does not happen spontaneously. It requires sufficient information infrastructure and cultural openness to information sharing through shared mental models.

A complex adaptive disaster management network must invest in information technology for timely information sharing between public, private, and non-profit organizations (Celik and Corbacioglu, 2012). The technical infrastructure also enhances situational awareness and reallocates the personnel and material resources to the disaster sites as well as re-tasking necessary assets (Hamp et al., 2014); however; the communications network must be flexible enough to handle unexpected situations (Yates and Paquette, 2011). For instance, the 1999 Marmara earthquake in Turkey disrupted almost all communication channels in the region because of the broken fiber optic cable across the region and failure of power (Corbacioglu and Kapucu, 2006). Cellular and traditional phones did not work in most places, and for most of the time, during the first three days. During this time, amateur radio and face-to-face communication were virtually the only available means of communication (Celik and Corhacioglu, 2010). Along with traditional means such as two-way radio, organizations must use alternative communication and information technologies such as satellite based communication systems, global positioning systems, geographic information systems, remote sensing, and interactive databases.

The context of operations in extreme events

This study explores two disaster response systems in response to the 2006 avian influenza (H5N1 virus) outbreak and 2011 Van earthquake, respectively in Turkey. The intent of the study is to analyze each response system in terms of actors and their interactions as well as examining the change in emergency response systems between the two disasters. The remaining parts of the paper are organized as follows: first, the context of each case along with the reasoning behind this research is explained; second, research methodology is explained; third, findings of the social network analysis are presented and discussed; and finally, the paper ends with the discussion and conclusion sections.

The 2006 avian influenza

The avian influenza (H5N1 virus), like other infectious diseases, endangered the lives of humans and animals in Turkey. At the end of 2005, the virus was first identified in a flock of domesticated birds in Manyas, Turkey and caused a health crisis by 2006, spreading to 254 different local communities in 54 provinces (World Health Organization, 2006). The avian influenza crisis as a process can be divided in two phases.

The first phase was between 8 October 2005, when the H5N1 virus first appeared in Manyas, and 1 January 2006, the date that the first patient died in Dogubeyazit. During the first phase, The Ministry of Food, Agriculture and Livestock (MFAL) did not sufficiently educate local public authorities and tended to isolate its surveillance and animal autopsy results from the personnel of local medical schools and health ministry departments as well as the public.

The second phase of the crisis began on 1 January 2006, the point at which the disease became a human health crisis, and extended to the time when outbreaks of the disease were extinguished, by the end of March 2006. During the second phase, along with the MFAL, the Ministry of Health was intensely involved at the central and local levels. When the crisis peaked, four children died and more than two million domesticated birds were culled (Arslan, 2007; World Health Organization, 2006). Although these numbers seem relatively low compared to other disasters, the crisis had a potential to become a pandemic which could have resulted in much higher risk for human and animal life in Turkey.

The Van earthquake

Approximately five years after the avian influenza crisis, an earthquake occurred in Van province. The earthquake, with the magnitude of 7.2 on the Richter scale, epicentered between the cities of Ercis and Van on 23 October 2011 at 10:41 (Greenwich Mean Time). Both field observations and aftershocks indicate that it was at most a distance of 30 km to Van’s city center (CEDIM, 2011; METU, 2011). There were 114 aftershocks with magnitudes 4.0–4.9 and 7 aftershocks with magnitudes greater than 5.0 throughout the week after the earthquake (AFAD, 2011a).

The earthquake essentially impacted Van province with heavy damage to its Ercis district, and was felt by surrounding districts and the provinces (METU, 2011). Along with Ercis and Van cities, towns adjacent to Van Lake were seriously damaged. A total of 604 people died and more than 2000 people were injured in Ercis, Van and nearby towns (AFAD, 2011b; METU, 2011). The number of properties collapsed or heavily damaged was 28,000 (METU, 2011). The total economic losses were estimated as ranging from 555 million USD to 2.2 billion USD (CEDIM, 2011). The natural gas distribution system, water supply systems, power and telecommunications were affected but became functional in hours (CEDIM, 2011). The roads between Van and Ercis cities were also affected due to collapse and cracking of their structures (METU, 2011).

Evaluation of the Turkish disaster management system and need for the study

The linearly designed Turkish disaster management was caught insufficiently prepared, especially at the local level, for the avian influenza crisis (World Health Organization, 2006). The system was unable to successfully coordinate organizations and resources during the first seven to ten days. A heavily centralized organizational structure, to some extent, was insufficient to respond effectively to the crisis. In order to address this problem, policy-makers made significant organizational policy changes by establishing the Prime Ministry Disaster and Emergency Management Presidency (AFAD), in 2009. Major policy and infrastructural changes were made in technical as well as organization capacity to improve coordination, response and recovery during operations. The legislation (with number 5902) gathered almost all centrally operated disaster organizations such as the Turkey Emergency Management General Directorate of Prime Ministry, the General Directorate of Civil Defense, and the General Directorate of Disaster Affairs under one body, AFAD.

These policy changes resulting from the 2006 avian influenza crisis created the need for the current study. The Van earthquake occurred following these changes in 2011. Therefore, the study allowed us to compare two response systems to identify whether the changes in organizational and technical structure undertaken between two disasters produced a more adaptive response system.

Methodology

This research is an exploratory small-n case study (Yin, 2013). The research seeks to reveal the evolution of two response systems: the 2006 avian influenza Crisis; and the 2011 Van earthquake. In this respect, the study analyzes and compares the two response system network structures to see whether the differences, if any, prove organizational learning in adapting dynamic and complex conditions of disaster environments. The study is centered on two research questions:

What organizations participated in each response system, and what patterns of interaction developed among the participating organizations for each system?

Did the reorganization of the Turkish disaster management system in 2009 facilitate organizational learning in response to the 2011 Van earthquake?

Data and coding procedures

The study uses news reports from three daily Turkish newspapers: Cumhuriyet; Hurriyet; and Sabah. These are the major national newspapers in Turkey, though representing different political leanings. For the avian influenza crisis, the research involves content analysis of the daily news reports from Cumhuriyet and Sabah between 28 December 2005 and 17 January 2006, and for the 2011 Van earthquake, it uses the daily news reports of Hurriyet between 23 October 2011 and 8 November 2011. All these newspapers observed the same events through their local branches and significantly depended on the reports of major news agencies. In this respect, the extent of the coverage of a newspaper made a critical difference. Considering the funding limitations, the research used only one newspaper and preferred Hurriyet for the Van earthquake case since it had better coverage of response operations in comparison to the others.

Using standard content analysis methods, we searched thoroughly the newspapers in order to identify organizations that participated in response operations in the three weeks’ period after the disasters. We, first, identified organizations which initiated any response activities. Second, we identified organizations which collaborated with initiating organizations. Third, we categorized the participating organizations based on their jurisdictions and funding source. The content analysis includes all transactions such as communications, coordination, knowledge and material sharing, and meetings organized by participating organizations.

The data from the content analysis were used to determine the interactions among organizations. After the initial coding, an interaction matrix was created for the avian influenza and Van earthquake response operations. A binary nominal level of measurement was used for the matrices that included only the names of interacting organizations and the frequency of interactions. An interaction between two organizations was coded as 1. If an interaction did not exist between two organizations, it was coded as 0. If the same organizations interacted again, we increased the previously assigned value by the number of new interaction/s. Later we imported the matrices from Microsoft Office – Excel 2010 to the UCINET 6.0 Social Network Analysis program (Borgatti et al., 2006) to conduct the network analysis.

Methods of analysis

We used mixed methods of analysis to address the research questions, fitting methods of analysis appropriate to the data available for each question.

Descriptive statistics of social network analysis

In order to analyze two response networks, we ran the UCINET 6.0 Social Network Analysis program to get basic network measurements. These measurements such as density, degree centrality, clustering coefficient and cliques’ data allow us to identify and compare network features of the two crisis response systems. Density is the ratio of number of connections that are actually presented in the network over all possible connections (Borgatti et al., 2006) and shows how well a network is connected. According to Borgatti et al. (2006), degree centrality shows to what extent actors in the network have strong relations or interactions with each other. Watts and Strogatz (1998) define clustering coefficient as the average ratio of links between nodes that are actually present divided by the total number of possible links among all nodes in the network. Finally, cliques’ data provide us with information about subgroups that have closer connections within the groups than to agents outside the group.

Small world ratio analysis

To address the questions, whether the network structure of emergency response systems changed from 2006 to 2011 and what changes in the system had an impact on the operational capacity of the Turkish emergency management system, we conducted a small world analysis.

According to Watts and Strogatz (1998), small world networks have high clustering coefficients and relatively short average path lengths. Shorter path lengths create an opportunity for a network in which most nodes can be reached from every other in a few steps. High clustering coefficients and shorter path lengths also give advantages to small world networks in communications and information processes (Watts and Strogatz, 1998). Therefore we need to have information about average path length and clustering coefficient of a network to evaluate whether the network has characteristics of small world property or not. Average path length is the average shortest distance between two nodes which indicates how long (i.e. how many nodes) it will take for one actor to reach the other in the network. The global clustering coefficient is the mean ratio of existing connections between the directly linked nodes and the total possible connections between all nodes in the network.

Contributions

The study has three methodological contributions: first, social network analysis helps us understand the informal characteristics of an evolving disaster response system that cannot be comprehended from formal organizational structures; second, different from the conventional ways of analysis, social network analysis maps the relations and interactions among disaster actors and provides important insights for the nature of the inter-organizational response system; and third, small world analysis is especially valuable to see the change between two disaster response systems operated at different times. This analysis is critical to infer whether the disaster management system has learnt from the previous experience.

Limitations

The study has some limitations: first, the content analysis was collected from different prestigious daily Turkish newspapers; nevertheless the news coverage for both disasters could not possibly cover all interactions. Even though these newspapers are national newspapers, they also include local news. Their proximity to local communities and events increases the justification of their news; second, since the analyses are based on these data, they also reflect the limitations of the content analysis. Despite the limitations, social network analysis based on the content analysis of the news reports for three weeks after a disaster is an endorsed methodology to analyze the multi-organizational response (Comfort and Haase, 2006; Comfort and Kapucu, 2006); moreover, the research also gathered information from official reports and documents, post-disaster critiques by participating organizations, and previously conducted on-site observations to compare to our content analysis to validate the results of network analysis.

Although the two events studied are different types of disasters, they represent some similarities that make it possible to compare and contrast the structure of response systems that emerged from the response operations: the first similarity is that the two events created threats in approximately the same geographic region within the period of five years; and the second similarity is that most of the same actors were involved in the response systems. These similarities present opportunities to analyze whether the changes introduced to the technical and organizational structure following the 2006 avian influenza crisis resulted in any significant improvements in the response capacity of the Turkish disaster management system. Moreover, there was not any significant health crisis similar to the avian influenza case after the 2009 policy changes. The 2011 Van earthquake was the first and only major disaster that let us explore the impacts of these changes.

Network analysis: characterization of two disaster response systems

Organizations involved in response operations

Table 1 shows the distribution of organizations involved in the two response systems, and it illustrates that mostly national public agencies (79.2%) played more important roles in the avian influenza response system in comparison to the Van earthquake response system (45%). In the avian influenza response system, provincial (34.6%) and district departments (29.5%) formed the majority since the avian influenza spread to many provinces and districts around the country. Therefore, provincial and district public organizations were actively involved in the operations, as expected. In the Van earthquake response system public central (21%) and provincial (14%) and public international organizations such as national governments and international bi-lateral aid agencies (29%) played more important roles. Compared to the avian influenza response system, the Van earthquake response system involved more international organizations. This is also expected, since earthquakes usually require more help from outside.

Table 1.

Distribution of organizations in two response systems.

National organizations	Avian influenza		Van earthquake
National organizations	Number #	Percent (%)	Number #	Percent (%)
Public – central	43	8.9	22	21
Public – province	167	34.6	15	14
Public – district	142	29.5	2	2
Neighborhood – village	14	2.9	–	–
Municipality	30	6.2	8	8
Non-profit	43	8.9	10	10
Private	6	1.2	17	16
Total national	445	92.3	74	70
International organizations	Number #	Percent (%)	Number #	Percent (%)
Public	28	5.8	30	29
Non-profit	3	0.6	–	–
Private	6	1.2	1	1
Total international	37	7.7	–	–
Total	482	100	105	100

Sources: Sabah and Cumhuriyet news reports, 28 December 2005–17 January 2006; Hurriyet news reports, 23 October–8 November 2011.

However, the Van response system included more organizations from the non-profit and private sectors. During the Avian influenza disaster, only 2.4% of the organizations involved were private organizations and 9.5% were non-profit, both at national and international levels, whereas during the Van disaster operations, 10% of the organizations involved were non-profit and 17% were private organizations.

The results for both response systems are consistent with the fact that public organizations are legally responsible for any disaster events. The Van earthquake response system gets relatively more involvement from different sectors. However, since only municipalities and village organizations are considered as local organizations according to the administrative law of Turkey, we can easily argue that both response systems are heavily centralized.

Inter-organizational network for two response systems

To compare differences in the organizational characteristics of the two disaster response systems, we used the UCINET 6.0 Social Network Analysis (Borgatti et al., 2006) program to identify and map organizational interactions during the response operations. Figures 1 and 2 show the network maps of response systems. The figures do not include organizations that are isolated from other actors or the organizations that have ties with just one actor.

Figure 1.

The avian influenza crisis inter-organizational network.

Figure 2.

The Van earthquake inter-organizational network.

Figure 1 shows the main component of the organizational network of the 2006 avian influenza crisis response operations. From Figure 1, it can be easily seen that the Ministry of Food, Agriculture and Livestock, The Ministry of Health, The Ministry of Environment and Forestry, and the Erzurum, Van and Agri province governments stay at the center of the network and provide connections among the other actors.

Figure 2 shows the main component of the organizational network that emerged following the 2011 Van earthquake. Organizations have different positions depending on how connected they are to each other. The informal network indicates that AFAD, Van Governorate, and Ercis District are at the center of activity. These organizations have a star network around them and connect with others through mediatory organizations.

Comparison of network statistics between avian influenza and Van earthquake response systems

Table 2 shows network level measures of density, degree centrality, clustering coefficient and cliques’ data for two response systems. Although overall density is low for both disaster response systems, it is relatively higher in the Van earthquake response system than in the avian influenza response system. Besides, the increase in average degree centrality in the Van earthquake response system indicates that organizations which participated in the Van earthquake response operations were relatively better connected in comparison to that of the avian influenza operations. The total number of links and cliques with at least three members was significantly higher in the avian influenza response network. These statistics are understandable since the avian influenza response operations spread to more regions and involved approximately five times more organizations. However, the clustering coefficient ratio is relatively higher in the Van earthquake response network which is consistent with density data showing a better connected network. Overall, these findings indicate that the Van earthquake response network was relatively better connected than the avian influenza one.

Table 2.

Network statistics of two response systems.

	Avian influenza	Van earthquake
Overall density	0.003	0.014
Link counts	425	85
Average degree centrality	2.846	3.215
Clustering coefficient	0.21	0.32
Number of cliques (n = 3)	40	5

Sources: Sabah and Cumhuriyet news reports, 28 December 2005–17 January 2006; Hurriyet news reports, 23 October–8 November 2011.

Comparison of central actors in two response systems

To compare central actors in terms of jurisdiction and sources of funding in two networks, the research calculated degree centrality measurements that show which actors have the most ties to which other actors and have positional advantage in the network. As a result, the top ten organizations for each response network emerged and we sought information about their jurisdictions and sources of funding.

Table 3 indicates the lists of organizations that had the most central positions in both response networks and played the most important roles during the response operations. It is clear that public organizations dominated the list. There is only one public international, one district organization and one non-profit (Turkish Red Crescent) that depended on the central government in terms of personnel and financial sources in the Van earthquake response network. Although Table 1 shows that the Van earthquake response system involved more organizations from non-profit and private sectors, we could easily see that these involvements did not play significant roles in both response operations, particularly during the avian influenza crisis.

Table 3.

Central actors in the 2006 avian influenza and the 2011 Van earthquake.

Number	Avian influenza, 2006	Sources of funding	Van earthquake, 2011	Sources of funding
1	Ministry of Food, Agriculture, and Livestock (MFAL)	P-C	Van Province Governance	P-P
2	Ministry of Health	P-C	AFAD	P-C
3	Agri Province Governance	P-P	Ercis District Governance	P-D
4	Ministry of Environment and Forestry	P-C	Erzurum Province Governance	P-P
5	Igdir Province Governance	P-C	Turkish Red Crescent (Kizilay)	N
6	Van 100.Year Hospital	P-C	Ministry of State	P-C
7	Turkish Grand Assembly	P-C	Turkish General Staff	P-C
8	Istanbul Directorate of Agriculture	P-P	Prime Ministry Office	P-C
9	Erzurum Province Governance	P-P	Azerbaijan	P-I
10	Van Province Governance	P-P	Istanbul Province Governance	P-P

P-C: public – central; P-P: public – provincial; P-D: public – district;

P-I: public – international; N: non-profit; AFAD: Prime Ministry Disaster and Emergency Management Presidency.

Sources: Sabah and Cumhuriyet news reports, 28 December 2005–17 January 2006; Hurriyet news reports, 23 October–8 November 2011.

Small World Analysis: flow processes through the networks

Due to its characteristics previously mentioned, a small world network provides a structural model for a disaster management system which enables actors to exchange information and resources rapidly. Information and resources move quickly due to short paths and increase the level of interactions among actors. This also increases the strength of the network, since if one actor becomes unworkable, the others still have options to use other short paths to reach their target. We calculated average path length and global clustering coefficient for both response networks. Based on their size and density, we also generated average path length and global clustering coefficient values for random graphs for each response network.

Table 4 shows that the Van earthquake response network has a better small world ratio than avian influenza response network. According to the generally accepted argument, the critical value of 4.75 is a threshold point for networks to show as small world network features (Kilduff et al., 2008). The Van earthquake response network has a higher ratio (5.71) than 4.75 whereas the avian influenza response network (4.65) is just under the threshold point. These findings are consistent with descriptive and social network analysis that indicated the Van earthquake response system had relatively better coordination of response operations. However, consistent with the same results, this improvement is not substantially higher.

Table 4.

Small world properties of avian influenza and Van earthquake response system.

	Avian influenza, 2006	Van earthquake, 2011
C	0.21	0.32
C_random	0.04	0.05
L	4.3	1.7
L_random	3.8	1.52
C/C_random	5.25	6.4
L/L_random	1.13	1.12
Small world ratio	4.65	5.71

Sources: Sabah and Cumhuriyet news reports, 28 December 2005–17 January 2006; Hurriyet news reports, 23 October–8 November 2011.

Discussion

Based on the research questions, we discuss the analysis of the results.

1. What organizations participated in each response system, and what patterns of interaction developed among the participating organizations for each system?

The content analysis of newspapers and UCINET analysis revealed some findings which characterized both response systems.

First, public organizations took a significantly high proportion of the whole network in both response systems. Combining national and international organizations, the avian influenza response system had 85% of involvement while the Van earthquake response system had 74% of involvement from public sectors. Therefore, the Van earthquake response system had more involvements from other sectors.

Second, public central organizations and public provincial organizations are the main component of both response systems. Considering that public provincial and public district organizations are also totally dependent on the central government in terms of personnel, financial sources and equipment, we can argue that both response systems are highly centralized.

Third, there was more involvement from international organizations in the Van earthquake response operations than the avian influenza operations. This is mostly due to the fact that earthquakes normally require more assistance from outside organizations since local actors can become a victim of disasters.

2. Did the reorganization of the Turkish disaster management system in 2009 facilitate organizational learning in response to 2011 Van earthquake?

The UCINET network level measures and small world network analysis indicated that the Van earthquake response system was relatively better coordinated and managed. Although the improvements in the system among both disasters did not change structural features of the whole response system, some improvements revealed that to some extent, the system managed to learn from previous disasters.

First, higher average centrality, higher clustering coefficient, higher density data show that actors in the Van earthquake response system are relatively better connected to each other. However, this difference is not significantly higher since the data are very close.

Second, small world analysis shows that both response systems have ratios that are close to the generally accepted threshold critical value of 4.75. The Van earthquake response network has a value of threshold while the avian influenza response network has a little lower ratio than threshold. To some extent, the Van earthquake response network shows small world features which allow rapid communication and information exchange in the system.

Conclusion

The content analysis, descriptive statistics, UCINET analysis and small world network findings show that changes in organizational and technical capacity of the system between the two disasters, somewhat improved the effectiveness of the disaster management system in Turkey. The reorganization gathered major central actors operating under different ministries coordinated by AFAD and abolished nonfunctioning coordination committees. Moreover, the Province Civil Defense Departments were reorganized to establish Province Disaster Management and Emergency Aid Departments that would function as the local branches of AFAD. Despite some continued flaws in self-adaptive response, the new system created a better mechanism to reallocate resources and coordinate critical actors.

Based on analyses, we recommend some actions for policy and practice.

First, the overall disaster management system is highly centrally governed. More investments in increasing technical and organizational capacity of the local disaster organizations should be prioritized.

Second, even though the Van earthquake disaster response system comprises more non-profit and private actors, there is very little contribution from sectors other than public. The organizational structure and policies should be more flexible to allow non-profit and private sectors’ involvement during all phases of disaster mitigation.

Third, the response system is not densely connected. In that respect, advanced information and communication technologies present an opportunity to create connections among disaster actors. More investments in information infrastructure in advance will help to create channels that facilitate information and resource exchange before and after a disaster happens. New and valid information through information channels will change governing values of actions that eventually increase organizational double-loop learning.

Fourth, one of the most important aspects of disaster management is to have experienced and quality personnel. Changes in risk due to the changes in environments such as demographic changes, urbanization, and technologic developments require continuous training of personnel. In doing so, people will carry their experience to the next disaster response; they will learn new developments and change their mental models to adapt to new conditions.

However, it is important to be cautious when making inferences from this analysis as we are comparing two different types of disasters occurring in different times. This limitation creates an opportunity for further research that can focus on the same type of disasters. Therefore, a future earthquake or health crisis response system can be compared to the 2006 avian influenza and 2011 Van earthquake response systems, respectively.

Natural and man-made disasters require an effective disaster response system that can achieve timely inter-organizational coordination in dynamic disaster environments. Disasters create complex and dynamic conditions in which many organizations with different organizational cultures and from different sectors spontaneously go to help the stricken communities. The complexity of disaster environments requires complex public policies that can foster collective and coordinated inter-organizational responses. Informed collective response requires going beyond linearly designed laws and regulations by increasing the technical, cultural, and organizational capacity for timely and coordinated action; hence, responsible managers need to invest into the system to make substantial improvements in the future.

Footnotes

Acknowledgements

We would like to thank Dr Louis K. Comfort for her valuable comments on the previous drafts of this paper. An earlier version of this article was presented at the 37th Annual APPAM Fall Research Conference 12–14 November 2015, Miami, Florida.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by a quick response grant from the Scientific and Technological Research Council of Turkey (TUBITAK) (grant number: 1002-7269/2009).

Author biographies

Suleyman Celik, is an Associate Professor in the Department of Public Administration at the Gazi University, Turkey. He received his master’s in Public Management at Carnegie Mellon University, and finished his PhD at the University of Pittsburgh in 2006. His research interests cover disaster management, network analysis and public policy.

Sitki Corbacioglu, is a Professor in the Department of Political Science and Public Administration at the Eskisehir Osmangazi University, Turkey. He received his master’s in Public Management at Carnegie Mellon University in 1998, and finished his PhD at the University of Pittsburgh in 2004. His research interests cover disaster management, public policy and organizational learning.

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