JOSCM - Journal of Operations and Supply Chain Management - n. 01 | Jan/Jun 2016

SPECIAL ISSUE: Article invited

Self-Sufficient Healthcare Logistics Systems and Responsiveness: Ten Cases of Foreign Field Hospitals

Deployed to Disaster Relief Supply Chains

Michael Naor Professor at Georgetown University - Washington - DC, USA

[email protected]

Ednilson S. Bernardes Professor at West Virginia University - Morgantown - West Virginia, USA

[email protected]

ABSTRACT: Recent disasters around the globe illustrate the unpredictability of their timing and the severity of their impact, making aid operations highly uncertain and complex. The aftermath of sud-den-impact disasters, such as civil conflicts, wars, and natural disasters, are typically characterized by chaos and the urgent need for medical care for a massive number of casualties; however, damage to local healthcare infrastructures usually render them unable to deliver needed services. Foreign field hospitals, innovative self-sufficient emergency healthcare logistics systems deployed outside the hos-pitals’ country, constitute a temporary solution until the local facilities are repaired or rebuilt. These types of healthcare logistics system have been deployed with great success. However, not much is known about factors that may account for their success in the supply chain literature. In this study, we investigate military foreign field hospitals and explore general factors that may account for their effectiveness. Specifically, we look into military healthcare logistics systems, specifically foreign field hospitals (FFHs), to explore factors that may account for their responsiveness. We examine ten success-ful deployments of an experienced and effective military FFH through an exploratory case analysis to shed light into factors that may account for its success. Various propositions and avenues for future research are developed.

Keywords: Flexibility, responsiveness, healthcare logistics, foreign field hospital, humanitarian aid.

Volume 9• Number 1 • January - June 2016 http:///dx.doi/10.12660/joscmv9n1p1-22

1

Naor, M., Bernardes, E. S.: Self-Sufficient Healthcare Logistics Systems and Responsiveness: Ten Cases of Foreign Field Hospitals Deployed to Disaster...ISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 1 p 01 – 222

1. INTRODUCTION

The humanitarian aid efforts after a natural disas-ter can significantly diminish social impact and suf-fering (Tomasini & Wassenhove, 2009a), measured in terms of the loss of life and property. Logistical deficiencies of healthcare providers (DiAquoi, 2011; Herron & Smith, 2011; McClintock, 2009) follow-ing the tsunami in Asia in 2004, Hurricane Katrina in 2005, the earthquake in Haiti in 2010, the chain of disasters in Japan in 2011, and, most recently, the typhoon in the Philippines in 2013 have brought to public attention the need to establish effective hu-manitarian operations designed to be responsive to sudden-impact disasters. The source of disasters can vary to include events such as earthquakes, tsunami floods, typhoons, radioactive events, and humani-tarian crises in war zones (Wassenhove, 2006). While disasters can impact developing countries the hard-est, the recent tsunami-related disaster in Japan ex-emplifies the vulnerability of developed countries as well. This natural event, in addition, shows that the initial source of disaster can have a complex evolu-tion (earthquake, tsunami, flood, and radiation).

Boin, Kelle, and Whybark (2010, p. 1) discuss the dif-ferent challenges faced by humanitarian aid organi-zations and observe that “practitioners have enjoyed only limited help from academics.” Understanding how humanitarian organizations design, imple-ment, and manage their disaster relief efforts to de-liver effective and efficient results will benefit the supply chain operations body of knowledge (Gati-gnon, Wassenhove, & Charles, 2010). One such little-known organization in the supply chain literature in general is the foreign field hospital (FFH). After a large-scale disaster, the immediate survival of the af-fected population is largely dependent on the quick response of healthcare systems within the critical first few days (Guha-Sapir & Panhuis, 2009). If the home-country healthcare services are unable to cope due to damage to hospitals, clinics, and facilities, or to lack of expertise, then international medical aid is essential. In such scenarios, the rapid deploy-ment of foreign field hospitals can provide a tem-porary solution to save lives (Redwood-Campbell, 2011). A field hospital is an independent emergency healthcare facility which is deployed fast, typically in sudden-onset disasters, and represents an inno-vative and complex logistical system that involves rapid shipment of remedies, diagnostic equipment, nurses, physicians, interpreters, and the assembly of needed medical facilities amid the rubble.

Among successful examples is the FFH deployment of the International Federation of Red Cross and Red Crescent Societies (IFRC), based out of Oslo and owned by the Norwegian Red Cross, to Haiti after the 2010 earthquake (Elsharkawi et al., 2010). It had a 20-bed surgical facility with medical supplies, elec-trical generator, and sanitation, along with vehicles. The FFH was initially located in the main University hospital compound at Port Au Prince and started ad-mitting patients four days after the earthquake. The outpatient ward treated about 75 patients daily. In addition, the Red Cross facility engaged in epidemic prevention measures. Another successful example is the Miller School of Medicine at the University of Miami Global Institute and the nonprofit organiza-tion Project Medishare (UMGI/PM), which estab-lished a FFH in Haiti. Due to their proximity to the disaster area, the first medical team of five people ar-rived only 20 hours after the earthquake (Ginzburg et al., 2010). Overall, 425 severely injured survivors were treated by the UMGI/PM field hospital during its first week of the relief operation.

As the examples suggest, FFHs have been success-fully used in the aftermath of disasters; however, we know little about the factors that account for their performance from the supply chain literature. As such, we adopt an inductive grounded case study approach to explore structural design factors that may account for the effectiveness of such self-suffi-cient healthcare logistics systems. In this study, we investigate military foreign field hospitals, self-suf-ficient healthcare logistics systems aimed at provid-ing emergency health services in disaster sites. Past literature suggests that responsiveness is critical in the aftermath environment of sudden-impact disas-ters (Guha-Sapit & Panhuis, 2009; Tomasini & Was-senhove, 2009b). Therefore, we look into a military healthcare logistics system (FFH) to explore poten-tial design traits that fit the chaotic, complex, and uncertain environment of healthcare disaster relief (Drazin & Ven, 1985).

Specifically, we focus on a particular type of aid with-in the “humanitarian assistance” category (Byman, Lesser, Pirnie, Bernard, & Waxman, 2000): emer-gency health services. While there are many types of disasters (Day, Melnyk, Larson, Davis, & Whybark, 2012; Wassenhove, 2006) and response phases (Hea-slip & Barber, 2014; Heaslip, Sharif, & Althonayan, 2012; Kovács & Spens, 2007), our primary focus is on sudden-onset disasters in general. The case data shows that a FFH achieves responsiveness by hav-


ing an organizational structure that confers it the re-quired flexibility to fit the general characteristics of the aftermath of sudden-impact disasters. The rest of this study is organized as follows. First, we briefly discuss humanitarian operations, foreign field hos-pitals, and healthcare service responsiveness. Next, we elaborate on the methodology used, followed by the description of a series of ten case studies over three decades. We then propose a set of propositions. Finally, we conclude with the limitations and future research opportunities that can further develop this emerging field of study.

2. HUMANITARIAN OPERATIONS, HEALTH-CARE DISASTER RESPONSIVENESS, AND FFHS

An area that has gained prominence in practice and research within disaster relief is the improvement of the management of humanitarian aid (Diaz & Tachizawa, 2015; Kovács & Spens, 2009). Humani-tarian aid involves “the process and systems in mo-bilizing people, resources, skills and knowledge to help vulnerable people affected by disaster” (Was-senhove, 2006). Tomasini and Wassenhove (2009a) present three principles for disaster relief initiatives accepted by most of the humanitarian organizations: humanity, neutrality, and impartiality. The princi-ples mean that the aid should be provided without discrimination to the people in need and without bias toward parties that could cause conflict in the suffering countries.

Tomasini and Wassenhove (2009b) characterize ev-ery humanitarian aid initiative as having ambigu-ous objectives, limited resources, high uncertainty, urgency, and a politicized environment. Balcik, Beamon, Krejci, Muramatsu, and Ramirez (2010) argue that the management of humanitarian aid is difficult, because different types of organizations are involved in the process, such as international and local humanitarian relief organizations, local governments, the military, and the private sector. Each of these organizations has different structures, capacities, and goals; thus, coordination among these disparate partners is an important challenge in the relief process (Stephenson & Schnitzer, 2006; Tatham & Kovács, 2010). Some of the difficulties in building an effective humanitarian aid network in-volve identifying the need and accessing the dam-aged area, the impact and level of influence of the aid, funding, political relations, and security of the aid force on site.

As the recent example provided by the disaster in Japan illustrates, the timing and level of impact suf-fered by areas in need of humanitarian aid pose high uncertainty and complexity. In that instance, a chain of natural disasters started with an 8.9 magnitude earthquake, which caused a tsunami responsible for damaging supposedly highly reliable nuclear fa-cilities. As a result, an extensive area of the country was exposed to damage from radiation as well as structural failure from the earthquake and the cor-responding tsunami. We see that, even though some geographical zones may be known for their vulner-ability to natural disasters, their timing, complexity, and ensuing impact are difficult to predict. Impor-tantly, the sources of sudden-onset disasters are not only natural events but also man-made (Wassen-hove, 2006); however, independent of the sources, they are typified by the speed of onset. Therefore, a responsive logistical system is of critical importance. This performance characteristic fits Day et al.’s (2012) argument that disaster relief supply chains operate under conditions of extreme environmental uncertainty and dynamism.

In these scenarios of uncertainty and dynamism, the availability of emergency medical care in the aftermath is critical; however, damage to the local healthcare infrastructure usually renders it unable to deliver needed healthcare services. One solution to this problem is the dispatching of military foreign field hospitals as a temporary solution to address emergency medical needs until the affected country is able to rebuild and repair its healthcare facilities.

Byman et al. (2000) categorize military disaster relief supply chain operations missions into: a) humani-tarian assistance, b) protection to humanitarian as-sistance, c) assistance to refugees and displaced persons, d) peace agreement enforcement, and e) restoring order. Within these categories, prior re-search indicates that the military has historically cooperated horizontally with relief agencies – that is, with other organizations at the same level within the disaster relief supply chain – by coordinating airlifts, sharing storage facilities, providing logistics assets, providing information on infrastructure and security, and setting up communications networks (Balcik et al., 2010). Correspondingly, the literature identifies the key military missions as establishing a secure environment and aiding relief organiza-tions, a mission that is fulfilled by providing assets and capabilities and creating conditions to permit the return of the disaster area to normality (Barber,


2012; Cross, 2014; Heaslip & Barber, 2014; Rietjens, Voordijk, & Boer, 2007).

In this study, we focus on a particular type of aid within the humanitarian assistance category (By-man et al., 2000): emergency health services. While there are many types of disasters (Day et al., 2012; Kovács & Spens, 2007; Wassenhove, 2006) and re-sponse phases (Heaslip & Barber, 2014; Heaslip et al., 2012; Kovács & Spens, 2011), our primary focus is on sudden-onset disasters in general. We exam-ine a military healthcare logistics system to explore potential structural design factors that fit the chaot-ic, complex, dynamic, and turbulent environment of healthcare disaster relief (Balcik et al., 2010; Day et al., 2012; Drazin & Ven, 1985). These organiza-tional environment characteristics are contingen-cies or threats that typify the highly uncertain envi-ronment of sudden-onset disasters in general (Day et al., 2012).

A FFH is an independent healthcare facility, which is deployed rapidly for emergency purposes, fol-lowing the request of the affected country (World Health Organization/Pan-American Health Organi-zation [WHO/PAHO], 2003. The WHO/PAHO (2003) defines a field hospital as “a mobile, self-contained, self-sufficient healthcare facility capable of rapid deployment and expansion or contraction to meet emergency requirement for a specified period of time.” In actuality, we can approach a foreign field hospital as a self-sufficient emergency healthcare logistics system rapidly deployed upon the request of a country submitted to sudden-impact disaster. The effectiveness of such logistics systems hinges on their responsiveness to areas in which a disaster has compromised the delivery of healthcare services (Besiou, Stapleton, & Wassenhove, 2011; Day et al., 2012; Guha-Sapit & Panhuis, 2009; Tomasini & Was-senhove, 2009b).

Building on the theoretical insights and conceptual development from Bernardes and Hanna (2009), in this study, we define healthcare disaster responsive-ness as the ability of a self-sufficient healthcare logis-tics system, such as a military foreign field service, to address current or emerging healthcare needs of an affected area in the aftermath of a disaster. The healthcare needs can demand that the logistics sys-tem varies its state to accommodate changes in the demand levels for services (e.g., number of casual-ties), service mix (e.g., level of primary and second-ary care), demand for new services (e.g., from ba-sic wards to infectious disease wards), and service

delivery schemes (e.g., to war zones, conflict areas, neutral areas, underdeveloped regions, etc.).

3. RESEARCH DESIGN AND CASES

We selected a qualitative research design, and a case study approach, because in emerging research phe-nomena, like humanitarian aid operations, a qualita-tive approach not only can generate new theoretical ideas but also allow us to “examine a contemporary phenomenon in its real-time context” (Yin, 2013). Our review of extant literature on management in humanitarian aid shows the use of a case-based ap-proach to understand field-vehicle fleet management operations (Martinez, Stapleton, & Wassenhove, 2011), humanitarian logistics (Tomasini & Wassen-hove, 2009b), and management of supply chains in humanitarian logistics (Wassenhove, 2006).

Following Eisenhardt (1989) and Miles, Huber-man and Saldana (2013), we adopt an inductively grounded case study approach to fulfill the im-portant exploratory quest of better understand-ing how a little-known organizational structure (a FFH) achieves success (responsiveness) in a critical and complex environment (aftermath of a sudden-impact disaster). According to Miles et al. (2013), a highly inductive study design is appropriate when experienced researchers are exploring an under-studied phenomenon, such as the use of a FFH in disaster relief and the organizational characteristics that confer it responsiveness in a chaotic, complex, and uncertain environment.

Our research design is based on multiple cases and involves multiple investigators; thus, allowing for replication logic (Yin, 2013), where the cases can be somewhat treated as “a series of independent ex-periments that confirm or disconfirm emerging con-ceptual insights” (Brown & Eisenhardt, 1997, p. 227). Studies following a similar research design identify the development of testable theory that reduces researcher bias and gives a close correspondence between theory and data as two salient features of replication logic (Amit & Zott, 2001; Ravenswood, 2011). Since the research on the topic of humanitar-ian operations is in the early stages and an in-depth understanding of the subject is considered urgent and necessary (Day et al., 2012), an inductive case study approach is a good research strategy (Edmon-son & McManus, 2007; Creswell, 2012).

Our purposeful sampling involves the ten FFH de-ployments of the Israel Defense Forces (IDF) in the


last three decades (see Appendix I), which allow for different perspectives on the deployment of these self-sufficient healthcare logistics system to different areas of operation – purposeful maximal sampling (Cresswell, 2012). The selection of the IDF missions to investigate factors that may account for the success of FFHs is based on access and traits of the cases. We gained access to high-ranking and experienced of-ficials directly involved with the deployments over the years. This allows our study to benefit from data provided by experience of over three decades in providing humanitarian healthcare aid at disasters areas. In addition, both the military strength and ex-penditure as a percentage of gross domestic product position the Israeli military within the top group of countries that typically deploy FFHs (Central Intel-ligence Agency, 2016).

After each mission, the IDF held a rigorous post-mission comprehensive investigation, in order to derive lessons about functional and structural ad-aptation, manpower selection and training, medi-cal equipment and general facilities, transportation, communication systems, medical data storage, cre-ativity, mission termination, etc. As such, a body of knowledge has been accumulated over the years by the IDF Medical Corps from deploying numerous relief missions to both natural and man-made disas-ters, in different regions of the globe (Africa, Asia, Caribbean, Europe, and Middle East). Access to this type of data, which juxtaposes different humanitar-ian missions, is regarded as helpful for the purposes of our study. Indeed our study shows an evolution-ary pattern with improvements implemented from one mission to the next.

We collected primary data through interviews and secondary data through consultation to documents available on the missions. We conducted a series of interviews with several high-ranking career officials who led IDF missions over the years and have vast experience in humanitarian aid from their service in the Pikud Oref (Israel Home Front Command). Most of them are high-ranked officers in the IDF who served as the Chief Medical Officers of the missions described in the cases. We triangulated the primary data with secondary data published on the mis-sions and other documents, conducting within and across-case analysis to identify patterns. Due to the nature of the organization and the sensitivity of its missions, names are not disclosed to preserve ano-nymity, but Appendix 3 presents general informa-tion on the interviewees. Appendix 4 describes the

interview questionnaire protocol.

The summary of the cases in Appendix 1 illustrates the rapid tempo of the IDF FFHs, typically opera-tional within 24 hours of deployment, and the range of services provided, which vary from primary care to advanced surgical and medical services. As these humanitarian aid missions take place across the globe, these type of healthcare logistic system faces operations in different cultures. The cases illustrate the need for healthcare services responsiveness even in terms of cultural diversity and provide examples of actions in this respect, such as the accommoda-tion of Indian or Arabic traditions. The scale of the healthcare logistics system also responds to the di-mensions of the impact and needs of the area affect-ed, varying from small outpatient clinics to inpatient hospital structures. Appendix 2 shows the range of types of disasters, the duration of deployments, and the capacity of the logistic systems in terms of num-ber of beds, personnel, and specialty areas. Each type of disaster is unique and imposes a different set of challenges and needs that reveal the paramount role of disaster responsiveness, which is achieved through various types of flexibility.

4. CASE ANALYSIS AND PROPOSITIONS

We performed our interpretation of the data in light of the main general precepts of the contingency the-ory. The contingency theory (Drazin & Ven, 1985; Lawrence & Lorsch, 1967) asserts that organizational characteristics should fit their environment in order to achieve high performance. Several studies in the literature build on the contingency theory to explain the relationship between the environment, organiza-tional structure, management practices, and perfor-mance (Strasser, 1983). In the context of humanitarian aid, a foreign field hospital is a self-sufficient health-care logistics system that requires structural flexibili-ty to be responsive (Bernardes & Hanna, 2009) and, as such, to fit the highly uncertain and complex external environment that is the aftermath of sudden-impact disasters (Besiou et al., 2011).

One of the insights that emerges from our inter-views and data analysis refers to organizational knowledge accumulated and diffused over the years by the IDF. This seems to have facilitated the iden-tification and development of processes and capa-bilities that contributed towards the flexibility of the self-contained healthcare logistics systems. The evo-lution of IDF humanitarian operations started with


the deployment of mobile clinics to disaster areas (Kasis et al., 2001). This initial arrangement was later transformed into full-blown foreign field hospitals with their scale tailored to the type of disaster and area needs (echelons). The founder of the emergency medicine discipline in Israel, and a senior IDF Of-ficer in charge of the humanitarian missions during the 1980s and 1990s [#4, appendix 3], describes the evolution and explains how it took place:

The first full-scale IDF FFH was in Armenia and later Turkey, India, and Haiti. The evolu-tion happened in terms of four factors: struc-ture, mode of operations, deployment, and assessment. The concept of the Israeli FFH is built based on the model of FFHs that were be-ing used by the IDF during war times in the Middle East, such as the Yom Kippur War (1973) and Operation Peace Galilee (1982).

The IDF FFH adopted the IDF military model of treating soldiers wounded in the battlefield using the medical principle of echelons, which refers to roles or levels of medical care. Echelons describe the stratification of the tiers in which medical care is or-ganized to conduct treatment and is defined on the basis of capabilities and resources that they possess (Church, 1990). Following this principle, the IDF or-ganizes the FHH in the first echelon (capable of triage in the field), second echelon (capable of functioning as a referral center providing secondary care), and third echelon (capable of performing evacuation for tertiary care). This allows the field hospital to pri-oritize patients based on the initial assessment of the severity of their injuries by the lower echelon. Dur-ing the evolution of the humanitarian missions and development of the self-contained healthcare logis-tics system currently adopted by the IDF, there has been a great deal of learning and initiatives geared towards preserving such knowledge. A senior IDF Officer [#6, appendix 3] in charge of the humanitar-ian missions during the last decade elaborates on this point and highlights some important features of the process:

The Israeli government has been investing in costly humanitarian outreach around the globe for over three decades. The staff of every Israeli FFH is com-posed two-thirds of participants from past missions and one-third of newcomers, in order to transfer knowledge gathered between missions and create an organizational body of knowledge on humani-tarian aid.

In addition to the procedural rotation of person-nel, there are formal processes in place to codify and derive lessons learned. Another senior Officer [#4, appendix 3] indicates that the IDF humanitar-ian missions “conclude with a rigorous process of post-mission comprehensive investigation in order to derive lessons about what type of equipment to bring, how to airlift it, and where to unload it.” He further states that “[a] body of knowledge was ac-cumulated over the years by IDF from deploying in numerous relief missions.” Such practices seem to assure the creation, transfer, and preservation of organizational knowledge, which, ultimately, seems to reflect on the identification and development of the capabilities that contribute towards the respon-siveness of this type of self-contained healthcare logistics system. Therefore, the development, pres-ervation, and transfer of organizational knowledge about humanitarian logistics may ascertain the fit between the FHH structure and the uncertainty and complexity of sudden-impact disasters.

Proposition 1: Healthcare logistics systems that over time systematically develop and codify humanitar-ian logistic operations knowledge are associated with higher responsiveness.

Civilian and military foreign field hospitals are self-contained healthcare logistics systems that have been used successfully in both natural and complex disasters, such as civil conflicts and wars. However, emerging from our data analysis is the notion that the military structure may confer advantages to these mobile healthcare logistical systems. One of the aspects raised by a senior Officer [#5, appendix 3] is the selection and training of staff that in the IDF “are selected to join the FFH missions, unlike some other countries’ teams, which are based on volun-teers.” In the IDF FFH, the staff (physicians, nurses, etc.) is recruited in a very selective process. Only se-nior people that are highly ranked and very quali-fied are recruited after rigorous training in emergen-cy medicine. The senior IDF Officer [#6, appendix 3] further elaborates on the selection and training for humanitarian missions:

The Israeli staff trains as a unified military unit for upcoming missions. Training is important because of the uncertainty in the situation. For example, in a disaster area the staff should know who they are supposed to treat and who not: it is an ethical dilemma to prioritize patients. In Israel, the FFH is composed of reservist soldiers drafted for the mission. The army takes care


of their salary, medical insurance, and family while they are deployed overseas.

This suggests that the military structure facilitates preparedness for humanitarian operations through formal training and the selection process. Another senior Officer [#5, appendix 3] states that the “ad-vantage of military compared to non-profit organi-zations is that it has an annual budget to conduct practices.” Yet another ranking official [#1, appendix 3] interviewed elaborates on other points:

The military gives structure and discipline. It recruits people in an organized manner, cov-ers their life insurance to risky disaster areas, and pays their salary during missions. Because the military takes care of the twofold financial compensation and life insurance, staff can fully concentrate on accomplishing the relief mis-sion. The military arranges life insurance for its staff, which is considered a critical factor by candidates for deployment in dangerous disas-ter zones.

The system of benefits, training, and selection em-ployed by the IDF seems very conducive to rapid deployment of mobile healthcare logistics systems. It allows the assembly of a highly trained and co-hesive team suited to the situation and familiar with the processes in a short period of time. There are some unique characteristics to the case in that Israel is a small country; therefore, as a senior IDF Officer explains [#4, appendix 3], team members typically know each other, because “they studied in the same medical school, worked together, or live in proximity.” While there are some nuances that may be unique to Israel, in general, the data seems to suggest that the military structure in terms of re-cruitment, training, and benefits may facilitate the fit between military FFHs and the uncertainty and complexity caused by sudden-impact disasters.

Proposition 2a: Formal recruitment, training and benefit packages have a positive effect on health ser-vices disaster responsiveness.

Proposition 2b: The underlying personnel benefits structure of a military healthcare logistics system are associated with higher health services disaster responsiveness.

We noticed in our cross-case analyses that the IDF staff has knowledge of culture, language, and the health situation of the country affected. After a di-saster, it is paramount that field hospitals arrive

on site and become operational between 36 and 72 hours in order to minimize the number of casualties. The location of the FFH is a strategic decision deter-mined by military logistics considerations. The site is designed to survive aftershocks of earthquakes, and it needs to be located in a safe place if within a war zone, but at the same time, it needs to be accessi-ble to patients. In addition, as the aftermath unfolds, the situation and needs may change.

During the field operations, there is the continu-ous need for air support to transport medicine and equipment. According to an official [#5, appendix 3], the military can provide “logistical support in the form of a self-sufficient supply chain of remedies, equipment, food, and water that civilian or non-profit humanitarian organizations lack the financial means to acquire.” However, one key point that emerges in terms of healthcare services responsive-ness across the cases is the ability to rapidly deploy with incomplete information. The IDF mission Chief Medical Officer to Armenia and Rwanda elaborates [#4, appendix 3]:

A uniqueness of Israeli FFHs is the ability for early arrival of the team after preliminary as-sessment with incomplete information. This logistical flexibility stems from the usage of military airplanes which are prepared in stand-by mode all-year around, ready for immediate call to be deployed. Military personnel can be drafted in short notice by command. Another logistical advantage, military equipment is ac-cessible for usage, such as electric generators, laundry machines, remedies, food, tents, water, and kitchen supply, and soldiers are allocated as guards too in order to ensure security of the staff and equipment during a mission in a cha-otic, uncertain environment.

In this regard, another high-ranking official [#1, ap-pendix 3] indicates that the “military has advanced communications systems, logistical support, and ability for rapid deployment by airplanes.” The IDF Chief Medical Officer for recent missions to the Phil-ippines, Japan, and Haiti [#6, appendix 3] shares a similar view about the military’s ability for quick deployment on short notice:

Responsiveness, the ability to reach the disaster zone rapidly in two to three days maximum, is the major advantage of a military unit drafted to serve in the FFH by military authorization command. The FFH is equipped with advanced


diagnosis equipment like X-ray and radiology machines from the military inventory that ci-vilian organizations usually cannot afford. The IDF FFH is a self-sufficient facility with fuel, water, and food supplies.

In Adapazari, Turkey (see Appendix I), after the earthquake in 1999, the availability of air transport for patients in more critical conditions to more appro-priate hospitals, for example, the Istanbul hospital, helped the IDF FFH extend better quality of care to more patients. In general, the data seems to suggest that the access to dedicated and specialized equip-ment may facilitate the fit between military FFHs and rapid deployment required by the uncertainty and complexity caused by sudden-impact disasters.

Proposition 3a: Ready and formal access to dedicat-ed supporting equipment have a positive effect on healthcare services responsiveness.

Proposition 3b: The underlying dedicated equip-ment structure of military healthcare logistics sys-tems is associated with higher health services disas-ter responsiveness.

Another interesting theme that emerges from the data analysis related to health services disaster re-sponsiveness is the ability to cooperate and integrate work with other agencies and organizations. For in-stance, in Armenia, the IDF invited the medical staff from the local hospitals destroyed during the earth-quake to join its field hospital. This collaboration in-creased the capacity of the healthcare logistics sys-tem and added knowledge about the medical history of the region to the efforts. Similarly, in Rwanda, the IDF coordinated efforts with the local authorities for the site selection. The initial primary hospital gradu-ally moved towards a secondary medical center and incorporated local and Dutch Army staff to support the expansion of the services. A high-ranking Officer sheds further light on this aspect [#2, appendix 3]:

In almost every mission, there were partner-ships with other countries. In Rwanda, Kosovo, and Haiti, there was partnership with the USA, France, Germany, and local organizations. The Israeli FFH structural flexibility is demonstrat-ed by the fact it can cooperate with other FFHs sent by different countries and with the local healthcare system. To enable this cooperation, interpreters are part of the staff.

In Kosovo, the IDF partnered with young Albanian students that volunteered to provide translation in

order to help the hospital. The IDF also coordinated with the local hospitals to transfer a few of the pa-tients after initial triage. Another instance of coop-eration and partnership took place in Haiti, where the Israeli field hospital reached maximum capacity in a couple of days. IDF officials coordinated with primary hospitals for the provision of postoperative care. This cooperation enabled the IDF FFH to pro-vide more specialized and surgical services, know-ing that patients would receive postoperative care in other facilities. In addition, another high official [#5, appendix 3] reveals that the Israeli FFH further co-operated with other countries in other ways, “shar-ing medical equipment and coordinating the trans-fer of patients between medical facilities.”

In the Philippines, the IDF FFH combined its physi-cal setup with the local structure and supported the local medical staff to create one integrated medical infrastructure. Although the visiting disaster relief group had 25 physicians representing most medical subspecialties and first-class logistics support, it re-linquished sole decision-making authority and im-provised to establish a model of cooperation with the local healthcare administrators. Open discussions to establish clear lines of responsibility and co-sharing of tasks helped the foreign medical team to gain the trust of the local facility and enabled smoother di-saster operations. The cooperative arrangement al-lowed the Israeli team to provide medical assistance to 2,686 cases in ten days. In general, the data seems to suggest that the ability to cooperate, coordinate, and incorporate or become part of another team may facilitate the fit between the military FFH struc-ture and the high uncertainty, evolving needs, and complexity caused by sudden-impact disasters.

Proposition 4a: Flexible integration and coordina-tion capabilities have a positive effect on health ser-vices disaster responsiveness.

Proposition 4b: The multi-agency experience of the military healthcare logistics system provides flex-ibility that is associated with higher health services disaster responsiveness.

Another aspect that emerges from the cases and data analysis that seems to confer healthcare responsive-ness refers to something akin to modularity and postponement of the healthcare logistical package. The data suggests that the IDF FFH can be partly or fully deployed and substitute or complement the local healthcare system. It can be designed for dif-ferent stages of disaster: the first 48 hours for emer-


gency medical response – the immediate response phase in the disaster management cycle (Heaslip & Barber, 2014) – or three to fifteen days after the di-saster for follow-up care and routine medical care based on the air travel distance from Israel to the disaster location. Asked about this aspect, a high-ranking Officer [#1, appendix 3] further elaborates:

The IDF FFH has a flexible structure to be adaptable to the magnitude and type of the disaster. It can be deployed in versatile modes of operation: primary care (first-aid clinic) or secondary care (with advanced surgery wards such as neonatal intensive care unit, etc.). For example, in Rwanda and Kosovo, the FFH was operated in primary care mode for first-aid treatment and minor surgeries only.

The data seems to suggest an underlying ability to incorporate different capabilities by combining modules of personnel and equipment according to the current or emerging needs in the field. For in-stance, a physician who volunteered in Haiti [#3, ap-pendix 3] discusses the flexibility of the healthcare logistics package:

It is dynamic according to emerging needs in the disaster area. In the beginning, it is a triage facil-ity, and if there isn’t enough space in the FFH for the hospitalization of patients, then new space is opened with tents made of net. For example, the Israeli medical team did outreach visits to treat patients in remote sites if they could not reach the FFH due to lack of transportation. Wards are created according to need in the field. Of course, there are basic wards that always exist like sur-gery, trauma, etc., but based on the need in the field a new ward maybe created. For example, a ward to deal with infectious disease.

The Chief Medical Officer for recent missions to Ja-pan, Haiti and the Philippines [#6, appendix 3] indi-cates that the FFH is able to adapt the operations, the structure, and the functions according to the emer-gent needs in the area of disaster. For example, he cites that, in Haiti, initially the team treated people suffering from crash injuries as a result of the earth-quake, but after a month they started treating people for cholera. The data indicates that the FFHs com-bine the different resources to fit the situation in the area of disaster. The structure “can be deployed full scale with many wards like in the case of Haiti or it can be a mobile clinic like Japan”, as indicated by one of our respondents. In Adapazari, Turkey, after

the earthquake, it was important for the field hospi-tal to be able to adapt to the situation as the medical needs changed. During the first few days, most of the patients were injured directly as a result of the earthquake and there was a need for surgical, ortho-pedic, obstetric, and gynecology treatment; in the later stages of the disaster effort, the hospital had to be able to provide regular medical care and also deal with infectious diseases. The combination of medi-cal staff in the field hospital was designed efficiently to answer the medical needs based on all the stages after the earthquake. In general, the data seems to suggest that the capability to mix and match mod-ules of personnel and equipment and to adapt in the area of operations facilitates the fit between military FFHs and the uncertainty and complexity caused by sudden-impact disasters.

Proposition 5a: The ability to rapidly reconfigure structure has a positive effect on healthcare services disaster responsiveness.

Proposition 5b: The underlying multidisciplinary logistical structure of military healthcare logistics systems is associated with higher health services di-saster responsiveness.

The data analysis reveals another aspect that seems to confer healthcare responsiveness in disaster situ-ations, which is the creativity and ingenuity of the personnel involved and their ability to accomplish the mission with limited resources. Both of these traits seem to provide flexibility to allow adaptation to situations of high uncertainty and disruption, such as encountered in the aftermath of sudden-impact disasters. The role of those factors was identified in the cases and became apparent during the interviews. For instance, an IDF Officer who volunteered in Haiti [#3, appendix 3] provides an illustration:

It should be noted that the medical staff in Israel is creative in improvising solutions under field conditions – which is exactly the characteristic needed in an uncertain environment post-di-saster. It may be that the condition in Israel in which the staff is regularly working in hospitals with a lack of resources makes people creative.

Another high-ranking official further elaborates [#4, appendix 3], indicating that in Israel: “Due to national security instability and shortage of natural resourc-es, people in general attempt to creatively maximize output with limited capacity and to adapt to difficult situations; being able to operate under these condi-


tions makes IDF FFH staff efficient in uncertainty post-disaster situations.” In addition to these local characteristics, there is the general problem-solving attitude that typically characterizes the military oper-ations and environment in general. Yet another high-ranking Officer [#1, appendix 3] adds that IDF FHH personnel bring prior military field experience, such as: “the ability to adapt to situations involving opera-tions in uncharted terrain, the ability to improvise sleeping quarters outdoors, etc., which fits the post-disaster context.” Once again, while there are some nuances that may be unique to Israel, in general, the data seems to suggest that military training and ex-perience contribute towards personnel adaptability, making the military flexible and able to cope with limited resources, and may facilitate the fit between military FFHs and the uncertainty and complexity caused by sudden-impact disasters.

Proposition 6a: Creative and adaptable personnel has a positive effect on healthcare services disaster responsiveness.

Proposition 6b: The training and field experience of military healthcare logistics system personnel are associated with higher healthcare services disaster responsiveness.

There are other factors that may contribute to mili-tary FFH greater healthcare responsiveness as com-pared to volunteer civilian organizations, such as the chain-of-command and the juridical regulations that prescribe and enforce it. We also note that, ac-cording to a physician [#7, appendix 3], “hospitals in Israel are understaffed and under-budgeted. Conse-quently, medical staff has got used to working un-der pressure, long shifts, and filling multiple roles in rotation. All these characteristics are typical to what they might face in a disaster area.” In addition, the IDF medical staff rotates between tasks and operates under constant pressure, which may also relate to higher levels of emergency healthcare responsive-ness. Figure 1 summarizes the overall propositions.

Figure 1. Summary of the propositions


5. DISCUSSION AND GENERAL TAKE AWAY

Our study adds to a growing stream of research in-vestigating humanitarian healthcare logistic opera-tions. We contribute to this literature by shedding light on how a very effective and experienced hu-manitarian organization designs, implements, and manages its disaster relief efforts, addressing past calls for studies in this area (e.g., Gatignon, Wassen-hove, & Charles, 2010). We accomplish this by ex-amining the ten deployments of the IDF FFH in the last three decades and proposing factors that may account for their operational success.

Extant literature points out the important role fre-quently performed by military forces during sudden-impact disasters, such as civil conflicts, wars, and natural disasters (Apte, 2010; Heaslip, 2011; Heaslip et al., 2012). This literature asserts that the prominent role of the military in disaster relief efforts stems, at least partially, from its strength in logistical and orga-nizational structure (Bjørnstad, 2011; Johnsen, How-ard, & Miemczyk, 2009; Pettit & Beresford, 2005). Our study contributes to this stream a nuanced view of a particular military logistics system, the FFH, and particular design factors that may account for its ef-fectiveness. Specifically, the results of our exploratory case analysis pinpoint categories of structural factors that may contribute to the responsiveness of this lit-tle-known healthcare logistics system in the supply chain management literature. The results from this study suggest that military FFHs are versatile and can be employed in the aftermath of both sudden-onset natural and man-made disasters and conflicts.

Past research separates the disaster management cycle (DRC) into different phases: preparedness, response, and recovery (Heaslip & Barber, 2014). While we did not set out to investigate activities that take place in each of the different phases as our pri-mary goal in this study, we argue that the organiza-tional knowledge codified and diffused over time, which we propose contribute to the identification of processes and capabilities that confer structural flexibility and support responsiveness, inform the preparedness phase (Heaslip & Barber, 2014; Kovács & Spens, 2007; Pettit & Beresford, 2009). Among specific activities that can contribute knowledge to that phase are the rotation of personnel and com-prehensive processes of post-mission investigation observed in the deployments we examined.

In terms of the immediate response phase of the DRC (Heaslip & Barber, 2014), the critical point in terms

of logistical support (Akhtar, Marr, & Garnevska, 2012), we observe that the IDF FFH adopts the medi-cal principle of echelons, which refers to roles or lev-els of medical care. The IDF organizes the FFH in the first echelon (capable of triage in the field), second echelon (capable of functioning as a referral center providing secondary care), and third echelon (capa-ble of performing evacuation for tertiary care). This allows the field hospital to prioritize patients based on the initial assessment of the severity of their inju-ries by the lower echelon. This organizational design is akin to and matches Heaslip and Barber’s (2014) observation that the scale of logistical support may be linked to the scope of the disaster. The echelons describe the stratification of the tiers in which medi-cal care is organized to conduct treatment. We note that each echelon of the FFH possesses a defined level of capabilities and resources that confers flex-ibility to the overall structure and supports respon-siveness to the situation on the ground.

While prior literature advances our knowledge by categorizing the disaster management cycle into pre-paredness, immediate response, and reconstruction (Heaslip & Barber, 2014), the case analysis suggests that this useful categorization can be further expand-ed or discriminating in the case of relief health servic-es. As illustrated by some deployments, such as Haiti and the Philippines, the immediate response phase may unfold in unpredictable ways and demand ad-ditional services not directly tied up to the initial event but to its contextual development. For instance, emergency services targeted towards crash injuries in the aftermath of an earthquake may need to adapt to accommodate the demand to contain contagious diseases at a later time. This suggests that the needs during the immediate response phase can evolve in complex ways, requiring an organizational structure of the logistics system that is flexible and can respond to emergent changes. It also suggests that it might be beneficial to sub-categorize the response phase into immediate and evolving stages to better examine the disaster relief supply chain processes.

Among the propositions contributed by our study towards better understanding of healthcare logistics system responsiveness are integration and coordi-nation. Balcik et al. (2010) examine the basic struc-ture of humanitarian relief chains and coordination mechanisms practiced at different stages. In general, our findings seem to concur that the ability to coop-erate, coordinate, and incorporate or become part of another team may facilitate the fit between the mili-


Table 1. Insights for emergency healthcare logistics deployments

1. An advanced team is crucial for defining needs, expectations, priorities, and identifying risks, as well as facilitating legal details with local authorities.

2. Swift deployment providing adaptive operative flexibility is maintained by the delegation’s multidisciplinary heterogeneity of personnel, and readiness for improvisations.

3. Coordination with both the local health system and other aid organizations operations in the disaster area is essential.

4. The global nature of the deployments demands awareness and respect to na-tional cultural diversity of the parties involved.

tary FFH structure and the high uncertainty, evolv-ing needs, and complexity caused by sudden-impact disasters. The effectiveness of such ability should confer flexibility to the healthcare logistics system and support its responsiveness. One key action we identify in this self-contained logistics system is the ability and willingness to hold open discussions to establish clear lines of responsibility and relinquish leadership when needed. This is a critical insight, as past research indicates that the coordination be-tween military and relief partners is frequently prob-lematic (Heaslip, Mangan, & Lalwani, 2007; Heaslip et al., 2012; Pettit & Beresford, 2005).

While each location and type of disaster presents unique challenges, we identify some general in-sights from our analysis that seem associated to the responsive characteristic of healthcare relief logis-tics systems. First, from a logistical perspective, the cross-case analysis suggests that the rapid dispatch of a preliminary team immediately after a disaster to gather tactical information can increase the respon-siveness of the actual logistical system deployed. The information gathered by the preliminary team can inform the assembly of the modules of person-nel and equipment that need to be brought together to provide the required capabilities of the healthcare logistical package. While the preliminary team col-lects information, the healthcare logistical package is assembled. This can allow the delayed customiza-tion of the available resources to the unique condi-tions of the aftermath and the specific emergency needs of the area affected.

Another insight is that the medical staff should be trained in a variety of wards since the uncertainty and complexity of these environments (Besiou et al., 2011) require flexibility. Team members are likely to rotate between tasks due to the shortage of staff and unpredictability of the situations. For instance, IDF

surgical physicians assisted in routine patient care in Turkey, while non-surgical staff treated injured patients in Haiti. As such, it seems that cross-train-ing increases flexibility to the logistical package and supports healthcare responsiveness in aid missions.

Third, creativity and problem-solving orientation seem to be necessary traits to deal with the complex-ity and uncertainty of disaster relief situations and to improvise logistical solutions also in the case of health emergency services. For instance, such traits can help accommodate difficulties in the field, such as shortage of basic surgical instruments as well as the supply of medical devices and advanced blood laboratory services. This is another personnel-relat-ed trait that supports or adds various characteristics of flexibility to the logistical package. Ultimately, this trait of the healthcare professionals supports healthcare responsiveness.

Finally, we note from the onset that the timing, com-plexity, uncertainty and the ensuing consequences of sudden-impact disasters demand a logistical system that fits those contingencies. We identify responsive-ness (Bernardes & Hanna, 2009), more specifically healthcare disaster responsiveness, as the critical op-erational capability to address those contingencies. Our study advances propositions about characteris-tics of a self-sufficient emergency health system that fits the sudden-impact disaster environment, such as to deliver healthcare disaster responsiveness. These characteristics confer the logistics system the abil-ity to vary its state to accommodate changes in the demand levels for services (e.g., number of casual-ties), service mix (e.g., level of primary and second-ary care), demand for new services (e.g., from basic wards to infectious disease wards), and service deliv-ery schemes (e.g., to war zones, conflict areas, neutral areas, etc.). Table 1 summarizes the general insights emerging from our study that may inform practice.


5. The contribution of translators and local health employees is significant. 6. Integration of volunteer teams from other countries into field hospitals can fill

the lack of human resources and improve operations.7. The optimal operating period is 2-3 weeks. Substitutions and supplementary

airborne logistics are required for longer missions.8. Standardization of procedures is essential in order to optimize medical re-

sponse.9. After a few days, most medical activity becomes non-urgent treatment of the

population.10. Communication devices, information systems, and electronic medical records

improve efficiency of field hospitals. 11. Before departure back to the home country, the delegation need to coordi-

nate with local authorities the transfer of responsibility over the FFH facility, equipment, and supplies in order to ensure continuity of operations by local medical staff.

12. Ethical issues pertaining to treatment of patients and their families in disaster areas must be taken into consideration before mission deployment.

6. FUTURE RESEARCH

This study contributes a set of propositions that of-fer a fertile and hopefully fruitful avenue for future research. The propositions can be conceptually devel-oped and empirically tested to inform research and practice. For instance, underlying the set of proposi-tions is the notion that military healthcare logistics may offer greater responsiveness. Is that the actual case? Are there situations in which they may not be the most responsive or appropriate? While there have been studies focusing on civilian-military aid, there is a dearth of studies focusing on emergency healthcare, FFHs particularly, in the supply chain management literature. In general, underlying the propositions are structural and infrastructural factors that offer many possibilities for future research. For instance, creativ-ity and adaptability of human resources brought in to compose emergency healthcare logistical packages seem to be a key contributor to healthcare disaster emergency responsiveness. What specific traits form those characteristics and how can they be achieved through different structures (all-volunteer personnel, civilian personnel, etc.)?

Heaslip and Barber (2014) ascertain that the great-est contribution of the military to logistical aid is in the life-sustaining days after a natural disaster, while military expertise provided over time is best in a man-made complex emergency. Underlying this pro-posal is the notion that the contribution of the mili-

tary is contingent upon the type of disaster. Besides this consequential distinction, Heaslip and Barber (2014) contribute knowledge on the challenges faced by aid organizations during different phases of the disaster management cycle. However, whereas past research advances our knowledge by highlighting the potential role of types of disaster on the contribu-tion of the military to logistical aid, there is a paucity of research focusing on specific types of aid. In this study, we focus on emergency healthcare services, a type of contribution of the military to the logistical aid commonly encountered across various types of disasters, and on unique and little-known healthcare logistics systems, the FFHs. Although FFHs are de-ployed across a broad spectrum of disasters, and we examine factors that confer this logistical system re-sponsiveness in general, future studies may consider further uncovering specific factors that may be associ-ated to responsiveness in each distinctive phase of the disaster management cycle. Future research may also examine whether military FFHs are more effective in contributing emergency healthcare services in certain types of disaster than others.

Our analysis suggests that the ability to cooperate, co-ordinate, and incorporate or become part of another team may facilitate the fit between the military FFH structure and the high uncertainty, evolving needs, and complexity caused by sudden-impact disasters. One key action we identify in this process is the ability and willingness to hold open discussions to establish


clear lines of responsibility and relinquish control. Prior research suggests that different types of disaster also impact the challenge of achieving civil-military coordination (Cross, 2014; Heaslip, 2011; Heaslip et al., 2012; Kovács, Tatham, & Larson, 2012; Rietjns et al., 2007). Future research may examine whether the ability to hold open discussions have different im-pacts in different types of disaster and what other factors may contribute to the ability to cooperate or effectively become part of another team under differ-ent phases in emergency healthcare.

Some of the very factors that may confer respon-siveness to military FFHs, such as the availability of dedicated and specialized equipment, may also lead to unwanted outcomes, such as inhibiting the search for more effective plans of action. An illus-trative example related to our point seems to have been the availability and use of military aircraft to deliver food in Sudan in 1985. According to Cuny (1989), various specialists have argued that the use of military aircraft delayed key decisions on alter-native methods and concealed delivery issues with the route from the airports out to rural populations. Therefore, a question of critical importance is when military logistical systems to provide healthcare aid may be inappropriate or less effective. Are there trade-offs or contingencies involved that can inform planning and selection of resources?

We identify the capability to rapidly reconfigure modules of personnel and equipment as one po-tential contributor to healthcare services disaster responsiveness and the capability to integrate and coordinate efforts with multi-agencies as another. According to previous research (e.g., Peng, Schroed-er, & Shah, 2008), routines are a critical source of ca-pabilities. As healthcare logistics systems deployed in the aftermath of sudden-onset disasters operate in a politicized environment (Tomasini & Wassenhove, 2009b) and involve different types of organiza-tions, each with different structures, capacities, and goals (Balcik et al., 2010), understanding underlying bundles of routines that support coordination and integration is an important area for future research. The same potential is valid for underlying bundles of routines that support rapid reconfiguration of the system. Knowledge stemming from these areas can not only contribute to the humanitarian aid opera-tions but may also inform the supply chain manage-ment literature in general.

Another aspect that future researchers can further develop is the concept of healthcare services disas-

ter responsiveness proposed in this study. Future re-search can further advance the concept, propose the-oretically derived measurement items, and validate them empirically. Future research can also examine military foreign hospital deployments from other armed forces in different countries to compare, con-trast, and replicate the findings of our study. They can also formally compare the structure and opera-tions of all-volunteer forms of these self-sufficient healthcare systems with a military structure to un-cover additional factors and processes.

Finally, system dynamics has proven very useful in modeling complex systems (Besiou et al., 2011). Future researchers can use this body of knowledge to capture interdependency between the factors pro-posed in this study under different scenarios. Simu-lation models can assist researchers and managers alike to generate insights into the composition and deployment of healthcare logistics packages to dif-ferent areas under various circumstances.

7. ACKNOWLEDGMENT

We appreciate the assistance of Israeli Shlicha to Northern Virginia, Shiri Rachamim (of blessed memory), who inspired this work by her emissary in George Mason University, Hillel.

8. REFERENCES

Akhtar, P., Marr, N. E., & Garnevska, E. V. (2012). Coordination in humanitarian relief chains: Chain coordinators. Journal of Humanitarian Logistics and Supply Chain Management, 2(1), 85-103.

Amit, R., & Zott, C. (2001). Value creation in e-business. Strategic Management Journal, 22(6-7), 493-520.

Amital, H., Alkan, M.L., Adler, J., Kriess, I., & Levi, Y. (2003). Israeli Defense Forces medical corps humanitarian mission for Kosovo’s refugees. Prehospital and Disaster Medicine, 18(4), 301-305.

Apte, A. (2010). Humanitarian logistics: A new field of research and action (Vol. 7). Now Publishers Inc.

Balcik, B., Beamon, B. M., Krejci, C. C., Muramatsu, K. M., & Ramirez, M. (2010). Coordination in humanitarian relief chains: Practices, challenges and opportunities. International Journal of Production Economics, 126(1), 22-34.

Barber, E. (2012). Military involvement in humanitarian supply chains. In G. Kovacks & K. Spens. Relief supply chain man-agement for disasters: Humanitarian aid and emergency lo-gistics. Hershey, U.S.: IGI Global.

Bar-Dayan, Y., Leiba, A., Beard, P., Mankuta, D., Engelhart, D., Beer, Y., Lynn, M., Weiss, Y., Martonovits, G., Benedek, P.,


& Goldberg, A. (2005). A multidisciplinary field hospital as a substitute for medical hospital care in the aftermath of an earthquake: The experience of the Israeli Defense Forces field hospital in Duzce, Turkey, 1999. Prehospital and Disaster Medicine, 20(2), 103-106.

Bar-Dayan, Y., Mankuta, D., Wolf, Y., Levy, Y., VanRooyen, M., Beard, P., & Martonovits, G. (2000). An earthquake disas-ter in Turkey: An overview of the experience of the Israeli Defense Forces field hospital in Adapazari. Disasters, 24(3), 262-270.

Bar-On, E., Abargel, A., Peleg, K., & Kreiss, Y. (2013). Coping with the challenges of early disaster response: 24 years of field hospital experience after earthquakes. Disaster Medi-cine and Public Health Preparedness, 7(5), 491-498.

Bernardes, E. S., & Hanna, M. D. (2009). A theoretical review of flexibility, agility and responsiveness in the operations man-agement literature: Toward a conceptual definition of cus-tomer responsiveness. International Journal of Operations & Production Management, 29(1), 30-53.

Besiou, M., Stapleton, O., & Wassenhove, L. N. Van. (2011). Sys-tem dynamics for humanitarian operations. Journal of Hu-manitarian Logistics and Supply Chain Management, 1(1), 78-103.

Bjørnstad, A. L. (2011). Exploring network organization in mili-tary contexts: Effects of flatter structure and more decentral-ized processes. Military Psychology, 23(3), 315-331.

Boin, A., Kelle, P., & Whybark, D. C. (2010). Resilient supply chains for extreme situations: Outlining a new field of study. International Journal of Production Economics, 126(1), 1-6.

Brown, S. L., & Eisenhardt, K. M. (1997). The art of continuous change: Linking complexity theory and time-paced evolu-tion in relentlessly shifting organizations. Administrative Science Quarterly, 42(1), 1-34.

Byman, D., Lesser, I., Pirnie, B., Benard, C., & Waxman, M. (2000). Strengthening the partnership: Improving military coordination with relief agencies and allies in humanitarian operations, Santa Monica, CA: RAND Corporation.

Central Intelligence Agency. (2016). Country comparisons – Is-rael. The World Factbook. Retrieved from https://www.cia.gov/library/publications/resources/the-world-factbook/geos/is.html

Church, G. J. (1990). NATO logistics handbook. SNLC Secretariat International Staff, Defence Policy and Planning Division, Logistics NATO HQ.

Creswell, J. W. (2012). Qualitative inquiry and research design: Choosing among five approaches. Thousand Oaks, Califor-nia: SAGE Publications, Incorporated.

Cross, T. (2014). Disaster agencies and military forces, In Tatham, P. & Christopher, M. , Humanitarian logistics: Meeting the challenge of preparing for and responding to disasters (p. 257). Kogan Page Publishers.

Cuny, F. (1989). Use of the military humanitarian relief. Re-trieved from http://www.pbs.org/wgbh/pages/frontline/shows/cuny/laptop/humanrelief.html

Day, J. M., Melnyk, S. A., Larson, P. D., Davis, E. W., & Whybark, D. C. (2012). Humanitarian and disaster relief supply chains: A matter of life and death. Journal of Supply Chain Manage-ment, 48(2), 21-36.

DiAquot, R. (2011). There is a lot that I want to do: Reflections on the relief efforts in Haiti. Harvard Educational Review, 81(2), 331.

Diaz, A., & Tachizawa, E. M. (2015). What can business learn from humanitarian supply chains? The case of the Spanish Red Cross in Haiti. Journal of Operations and Supply Chain Management, 8(1), 1-15.

Drazin, R., & Ven, A. H. Van de. (1985). Alternative forms of fit in contingency theory. Administrative Science Quarterly, 30(4), 514.

Edmondson, A. C., & McManus, S. E. (2007). Methodological fit in management field research. The Academy of Management Review, 32(4), 1155-1179.

Eisenhardt, K. M. (1989). Building theories from case study re-search. The Academy of Management Review, 14(4), 532-550.

Elsharkawi, H., Jaeger, T., Christensen, L., Rose, E., Giroux, K., & Ystgaard, B. (2010). Mobile field hospitals in the Haiti earthquake response: A Red Cross model. Humanitarian Ex-change Magazine, 48, 1-6.

Farfel, A., Assa, A., Amir, I., Bader, T., Bartal, C., Kreiss, Y., & Sagi, R. (2011). Haiti earthquake 2010: A field hospital pe-diatric perspective. European Journal of Pediatrics, 170(4), 519-525.

Finestone, A. S., Bar-Dayan, Y., Wolf, Y., Stein, M., Tearosh, J., Zaide, Y., Mankuta, D., Eldad, A., & Benedek, P. (2001). Di-agnostic medical auxiliary equipment in a field hospital: Ex-perience from the Israeli delegation to the site of the Turkish earthquake at Adapazari. Military Medicine, 166(7), 637-640.

Gatignon, A., Wassenhove, L. N. Van, & Charles, A. (2010). The Yogyakarta earthquake: Humanitarian relief through IFRC’s decentralized supply chain. International Journal of Produc-tion Economics, 126(1), 102-110.

Ginzburg, E., O’Neill, W. W., Goldschmidt-Clermont, P. J., Marchena, E. de, Pust, D., & Green, B. A. (2010). Rapid medi-cal relief – Project Medishare and the Haitian earthquake. The New England Journal of Medicine, 362(10), e31.

Guha-Sapir, D., & Panhuis, W. G. van. (2009). Health impact of the 2004 Andaman Nicobar earthquake and tsunami in In-donesia. Prehospital and Disaster Medicine, 24(06), 493-499.

Halpern, P., Rosen, B., Carasso, S., Sorkine, P., Wolf, Y., Benedek, P., & Martinovich, G. (2003). Intensive care in a field hospi-tal in an urban disaster area: Lessons from the August 1999 earthquake in Turkey. Critical Care Medicine, 31(5), 1410-1414.

Heaslip, G. (2011). Challenges of civil military cooperation/co-ordination in humanitarian relief (Chapter 9). Relief sup-ply chain management for disasters: Humanitarian aid and emergency logistics, 147-172, IGI Global, Hershey, US.

Heaslip, G., & Barber, E. (2014). Using the military in disaster relief: Systemizing challenges and opportunities. Journal of


Humanitarian Logistics and Supply Chain Management, 4(1), 60-81.

Heaslip, G., Mangan, J., & Lalwani, C. (2007). Integrating mili-tary and Non-Governmental Organization (NGO) objectives in the humanitarian supply chain: A proposed framework. Hull, U.K.: Logistics Research Network.

Heaslip, G., Sharif, A. M., & Althonayan, A. (2012). Employing a systems-based perspective to the identification of inter-relationships within humanitarian logistics. International Journal of Production Economics, 139(2), 377-392.

Heyman, S. N., Eldad, A., & Wiener, M. (1998). Airborne field hos-pital in disaster area: Lessons from Armenia (1988) and Rwan-da (1994). Prehospital and Disaster Medicine, 13(1), 14-21.

Heyman, S. N., Ginosar, Y., Shapiro, M., Kluger, Y., Marx, N., & Maayan, S. (1997). Diarrheal epidemics among Rwandan refugees in 1994: Management and outcome in a field hospi-tal. Journal of Clinical Gastroenterology, 25(4), 595-601.

Heyman, S.N., Horovitz, J., Nehama, H., Sofer, S., Orbach, J., Amir, Y., Adar, A. (1994). Sudden death during fluid resusci-tation: a lesson from Rwanda. Lancet, 344, 1509-1510.

Herron, J., & Smith, M. W. (2011). The disaster of Hurricane Ka-trina: Malfeasance, official deviance and the failure to serve and protect a community. International Journal of Interdisci-plinary Social Sciences, 6(5), 127-140.

Johnsen, T., Howard, M., & Miemczyk, J. (2009). UK defense change and the impact on supply relationships. Supply Chain Management: An International Journal, 14(4), 270-279.

Kasis, I., Lak, L., Adler, J., Choni, R., Shazberg, G., Fekede, T. D., & Heyman, S. (2001). Medical relief operation in rural northern Ethiopia: Addressing an ongoing disaster. The Is-rael Medical Association Journal, 3(10), 772.

Kovács, G., & Spens, K. M. (2007). Humanitarian logistics in di-saster relief operations. International Journal of Physical Dis-tribution & Logistics Management, 37(2), 99-114.

Kovács, G., & Spens, K. (2009). Identifying challenges in humani-tarian logistics. International Journal of Physical Distribution & Logistics Management, 39(6), 506-528.

Kovács, G., & Spens, K. M. (2011). Trends and developments in humanitarian logistics: A gap analysis. International Journal of Physical Distribution & Logistics Management, 41(1), 32-45.

Kovács, G., Tatham, P., & Larson, P. D. (2012). What skills are needed to be a humanitarian logistician? Journal of Business Logistics, 33(3), 245-258.

Kreiss, Y., Merin, O., Peleg, K., Levy, G., Vinker, S., Sagi, R., & Ash, N. (2010). Early disaster response in Haiti: The Israe-li field hospital experience. Annals of Internal Medicine, 153(1), 45-48.

Lawrence, P. R., & Lorsch, J. W. (1967). Differentiation and in-tegration in complex organizations. Administrative Science Quarterly, 12(1), 1-47.

Levy, G., Blumberg, N., Kreiss, Y., Ash, N., Merin, O. (2010). Ap-plication of information technology within a field hospital deployment following the January 2010 Haiti earthquake di-

saster, Journal of American Medical Association, 17(6), 626-630.

Maayan, S., Marks, N., Viterbro, A., Zeide, Y., Morag, A., Neil, L., & Shapiro, M. (1997). HIV infection and susceptibility to epidemic bacterial infections among Rwandan refugees. In-ternational Journal of Infectious Diseases, 1(4), 199-201.

Margalit, G., Rosen, Y., Tekes-Manova, D., Golan, M., Benedek, P., Levy, Y., Martonovits, G., Bar-Dayan, Y. (2002). Recom-mendations for nursing requirements at a field hospital, based on Israel Defense Forces field hospital at the earth-quake disaster in Turkey- August 1999. Accident and Emer-gency Nursing, 10(4), 217-220.

Marom, T., Merin, O., Segal, D., Tsumi, E., Lin, G., Erlich, T. (2014). Ethical and clinical dilemmas in patients with head and neck tumors visiting a field hospital in the Philippines, American Journal of Disaster Medicine, 9(3), 1-8.

Martinez, A. J. P., Stapleton, O., & Wassenhove, L. N. Van. (2011). Field vehicle fleet management in humanitarian operations: A case-based approach. Journal of Operations Management, 29(5), 404-421.

McClintock, A. (2009). The logistics of humanitarian emergen-cies: Notes from the field. Journal of Contingencies and Cri-sis Management, 17(4), 295-302.

Merin, O., Blumberg, N., Raveh, D., Bar, A., Nishizawa, M., & Cohen-Marom, O. (2012). Global responsibility in mass casu-alty events: The Israeli experience in Japan. American Jour-nal of Disaster Medicine, 7(1), 61-64.

Merin, O., Kreiss, Y., Lin, G., Pras, E., & Dagan, D. (2014). Col-laboration in response to disaster-typhoon Yolanda and an integrative model. The New England Journal of Medicine, 370(13), 1183-1184.

Merin, O., Miskin, I. N., Lin, G., Wiser, I., & Kreiss, Y. (2011). Triage in mass-casualty events: The Haitian experience. Pre-hospital and Disaster Medicine, 26(5), 386-390.

Merin, O., Blumberg, N., Raveh, D., Bar, A., Nishizawa, M., Co-hen-Marom, O. (2012). Global responsibility in mass casualty events: The Israeli experience in Japan, American Journal of disaster Medicine, 7(1), 61-64.

Merin, O., Yitzhak, A., & Bader, T. (2015). Medicine in a disaster area: Lessons from the 2015 earthquake in Nepal. JAMA In-ternal Medicine, 175(9), 1437.

Miles, M. B., Huberman, A. M., & Saldana, J. (2013). Qualitative data analysis: A methods sourcebook. Thousand Oaks, Cali-fornia: SAGE Publications, Incorporated.

Peleg, K., Kreiss, Y., Ash, N., Lipsky, A.M. (2010). Optimizing medical response to large-scale disasters: The ad hoc collab-orative health care system, Annals of Surgery, 253(2), 421-423.

Peng, D. X., Schroeder, R. G., & Shah, R. (2008). Linking routines to operations capabilities: A new perspective. Journal of Op-erations Management, 26(6), 730-748.

Pettit, S. J., & Beresford, A. K. (2005). Emergency relief logistics: An evaluation of military, non-military and composite re-sponse models. International Journal of Logistics: Research and Applications, 8(4), 313-331.


Pettit, S., & Beresford, A. (2009). Critical success factors in the context of humanitarian aid supply chains. International Journal of Physical Distribution & Logistics Management, 39(6), 450-468.

Pinkert, M., Dar, S., Goldberg, D., Abargel, A., Cohen-Marom, O., Kreiss, Y. (2013). Lessons learned from an obstetrics and gynecology field hospital response to natural disasters. Ob-stetrics and Gynecology, 122(3): 532-536.

Ravenswood, K. (2011). Eisenhardt’s impact on theory in case study research. Journal of Business Research, 64(7), 680-686.

Redwood-Campbell, L. (2011). Primary care in the first 72 hours post disaster: A crazy idea or a sensible inclusion for foreign medical teams? Prehospital and Disaster Medicine, 26, 102-103.

Rietjens, S. J. H., Voordijk, H., & Boer, S. J. De. (2007). Coordinat-ing humanitarian operations in peace support missions. Di-saster Prevention and Management: An International Jour-nal, 16(1), 56-69.

Stephenson, M., Jr., & Schnitzer, M. H. (2006). Interorganizational trust, boundary spanning, and humanitarian relief coordina-tion. Nonprofit Management and Leadership, 17(2), 211-233.

Strasser, S. (1983). The effective application of contingency theo-ry in health settings: Problems and recommended solutions. Healthcare Management Review, 8(1), 15-23.

Tatham, P. & Kovacs, G. (2010). The application of “swift trust” to humanitarian logistics. International Journal of Produc-tion Economics, 126(1), 35-45.

Tomasini, R., & Wassenhove, L. Van. (2009a). Humanitarian lo-gistics. Palgrave Macmillan.

Tomasini, R. M., & Wassenhove, L. N. Van. (2009b). From pre-paredness to partnerships: Case study research on humani-tarian logistics. International Transactions in Operational Research, 16(5), 549-559.

Wassenhove, L. N. Van. (2006). Blackett Memorial Lecture Humani-tarian aid logistics: Supply chain management in high gear. Journal of the Operational Research Society, 57(5), 475-489.

Weiser, G., Mendlovic, J., Dagan, D., Albukrek, D., Shpriz, M., Merin, O. (2015). Case report: A brain abscess in a disaster zone- beyond the call of duty, Disaster and Military Medi-cine, 1(13), 1-3.

Wolf, Y., Bar Dayan, Y., Mankuta, D., Finestone, A., Onn, E., Mor-genstern, D., Rand, N., Halpern, P., Gruzman, C., Benedek, P., Martinovitz, G., Eldad, A. (2001). An earthquake disaster in Turkey: Assessment of the need for plastic surgery servic-es in a crisis intervention field hospital. Plastic & Reconstruc-tive Surgery, 107(1), 163-170.

World Health Organization/Pan-American Health Organization. (2003). Guidelines for the use of foreign field hospitals in the aftermath of sudden-impact disaster. Department of Emer-gency and Humanitarian Action, the World Health Organi-zation; Area of Emergency Preparedness and Disaster Relief, the Pan-American Health Organization.

Yin, R. K. (2013). Case study research: Design and methods. Thou-sand Oaks, California: SAGE Publications, Incorporated.

Yitzhak, A., Sagi, R., Bader, T., Assa, A., Farfel, A., Merin, O., & Kreiss, Y. (2012). Pediatric ventilation in a disaster: Clinical and ethical decision making. Critical Care Medicine, 40(2), 603-607.


Appendix 1. Brief description of IDF disaster relief missions

Country Brief description of FFH

Armenia In December 1988, a 7.1 magnitude earthquake occurred in Kirovakan, Soviet Armenia. The IDF Medical Corps deployed a field hospital to Kirovakan. The FFH team included general and or-thopedic surgeons, anesthesiologists, experts in rehabilitation, internal and emergency medicine, nephrology, and pediatrics. The medical relief operation was originally designed to serve as a pe-diatric rehabilitation center combined with dialysis facilities, as requested by the Soviet authorities but eventually provided primary care. The majority of patients received ambulatory treatment, but there were additional trauma cases, gynecology-obstetrics, and a few acute general surgical cases.

Sources: [#4, appendix 3], References: Heyman, Eldad, & Wiener, 1998; Maayan et al., 1997.Rwanda In July 1994, the IDF deployed 3 teams sequentially for 6 weeks to Goma, Zaire, following trib-

al strife in Rwanda with a consequently displaced population subjected to large-scale epidemics (principally cholera and dysentery) and famine. The length of the operation requiring team substi-tution every 2 weeks, and with replacements and supplies arriving by subsequent cargo airplanes, enabled a continuous prolonged operation. In each team there were experts in internal medicine and pediatrics with subspecialties, clinical microbiology/tropical medicine, critical care, anesthe-siology and neonatology, general and orthopedic surgeons, and gynecologists. The FFH compre-hensive multidisciplinary facilities provided primary and secondary care. The FFH composed of a triage unit, pediatric, medical and surgical wards, and diagnostic facilities.

Sources: [#4, appendix 3], References: Heyman et al., 1994; 1997; 1998.Kosovo The conflict in Kosovo in the 90s escalated in 1999, causing more than 1 million people from Kosovo

to flee from their country to the neighboring countries of Macedonia and Albania. In April 1999, the IDF provided medical services to the refugees. The structure of the hospital was composed of several wards: emergency room, internal medicine, obstetrics and gynecology, pediatric and neonatology, delivery, pharmacy, laboratory X-ray, and security. Twenty hours after arriving in Macedonia, the FFH became functional in the Brazda camp. The IDF field hospital became the referral center for all other primary medical teams. Most of the patients were treated for infections (because of poor sanitary conditions in the refugee camps), exhaustion, and chronic illness (heart disease, diabetes, etc.).

Sources: [#5, appendix 3], References: Amital, Alkan, Adler, Kriess, & Levi., 2003.Adapazari, Turkey

On August 17, 1999, a major earthquake (7.4 Richter) occurred in western Turkey. The city of Ada-pazari was severely hit. The Israeli field hospital was sent by the Israel Defense Forces (IDF) com-mand. The IDF field hospital located in Adapazari provided advanced surgical and medical ser-vices; it included trauma care and life-saving surgeries and was ready to accept patients in 24 hours after arrival on site. The site included 5 beds for intensive care treatment and 80 beds for general hospital admission, including internal medicine, obstetrics and gynecology, and surgery. The hos-pital staff was overall composed of 102 personnel acting as a secondary referral center.

Sources: [#1, appendix 3], References: Bar-Dayan et al., 2000; Finestone et al., 2001; Finestone et al., 2014; Halpern et al., 2003; Margalit et al., 2002; Wolf et al., 2001.

Duzce,

Turkey

In Nov 1999, an earthquake of 7.2 magnitude struck Turkey, this time in the region of Duzce. The IDF Medical Corps field hospital was sent 3 days after the disaster. It functioned for 9 days, aiming to substitute for a part of the damaged medical facilities. It acted as a secondary referral center pro-viding specialized and surgical care The hospital structure included 7 clinical branches: emergency room (triage), operation room (OR), surgical intensive care unit, internal medicine, orthopedics, pediatrics, obstetrics, and gynecology. The Israeli field hospital managed to fill the gap in the lo-cal medical system and, during its peak operation, its capacity was 300 patients per day. The field hospital focus was on secondary medical care rather than primary and urgent care.

Sources: [#1, appendix 3], References: Bar-Dayan et al., 2005.


Bhuj,

India

On January 26, 2001, a 7.7 Richter earthquake occurred in India, with the epicenter located in the city of Bhuj. The IDF-led relief activity in India departed within 84 hours after recruiting personnel from both regular army and reserve units and initiated hospital activity at site on day 6. The field hospital had a fully self-sufficient tent enactment with 30 beds and included auxiliary services units such as radiology, laboratory and medical supplies, and a logistical support unit. The total number of personnel deployed for the India operations was 97.

Sources: [#2, appendix 3], References: Bar-On, Abargel, Peleg, & Kreiss, 2013.Port au Prince, Haiti

A 7.2 Richter magnitude earthquake struck Haiti in January 2010. The Israel Defense Forces Medical Corps field hospital was on site and operational 89 hours after the earthquake and provided medi-cal care to many patients during its 10 days of operation. The hospital brought all required supplies in order to stay independent and provide fast deployment, including medical requirements such as antibiotics, imaging machines and laboratory facilities, and energy sources and accommodations. The field hospital consisted of 121 hospital staff members, divided in different units, including medical, surgical, pediatric, orthopedic, gynecologic, ambulatory and auxiliary. The capacity of the field hospital was 60 inpatient beds, which could be expanded to 72.

Sources: [#1, #2, and #3, #7, appendix 3], References: Farfel et al., 2011; Finestone et al., 2001; Kreiss et al., 2010; Levy et al., 2010; Merin et al., 2011; Peleg et al., 2010; Pinkert et al., 2013; Yitzhak et al., 2012.

Japan An earthquake of 9.0 on the Richter scale struck Japan on March 11, 2011. It caused a tsumani 250 miles at northeast Honshu. The IDF sent a delegation to build a small scale FFH in the format of a clinic. Its clinic was located on the east coast in the town of Minami-Sanriku. It served mainly as a referral unit for diagnostic and medical treatment. It was staffed with 55 personnel. The structure of the FFH consisted of several wards: registration-triage and discharge, gynecology, internal medi-cine, laboratory, surgery, pediatrics, pharmacy, laboratory and imaging, and a logistics command center. Also, a team of 8 translators helped the FFH crew. In addition, there were an imaging crew equipped with ultrasound and X-ray, a hematology-microbiology-chemistry laboratory, and wire-less services.

Sources: [#6, appendix 3], References: Finestone et al., 2001; Merin et al., 2012; Pinkert et al., 2013.The Philip-pines

The typhoon Haiyan struck the Philippines on November 8, 2013. Five days after the event, an IDF team from Israel was assigned by the Philippines government to provide medical assistance to the city of Bogo, where a local hospital serving more than 250,000 people was operating at partial ca-pacity. The FFH team in the Philippines decided to combine its physical setup with the local struc-ture and support the local medical staff with its experienced medical group, to provide maximum benefit and thereby create one integrated medical infrastructure. Although the IDF team had 25 physicians representing most medical subspecialties and first-class logistics support, they decided to relinquish sole decision-making authority and improvised to establish a model of cooperation with the local healthcare administrators.

Sources: [#6, appendix 3], References: Marom et al., 2014; Merin et al., 2014; Weiser et al., 2015.Nepal A 7.8 Richter magnitude earthquake struck Nepal on April 25, 2015. The IDF mission that estab-

lished a field hospital in Kathmandu on April 29 consisted of 126 personnel including 45 physi-cians. They arrived with 100 tons of equipment and supplies, and capacity to treat 200 patients per day. It was the largest IDF mission deployed overseas. Its wards included 2 operating rooms, an 8-bed intensive care unit, trauma, obstetrics, gynecology, surgical, orthopedic, and imaging facility.

Sources: [#6, appendix 3], References: Merin, Yitzhak, & Bader, 2015.


Appendix 2. Data on relief missions

Country Armenia Rwanda Kosovo Turkey (Adapaz-ari)

Turkey (Duzce)

India Haiti Japan Philippines Nepal

Date (month, year) Dec-88 Jul-94 Apr-99 Aug-99 Nov-99 Jan-01 Jan-10 Mar-11 Nov-13 Apr-15Type of disaster 6.8 Richter

Earth-quake

Rwandan Refugees

Albanian Refugees

7.6 Richter Earth-quake



7 Richter Earth-quake


Typhoon 7.8 Richter Earth-quake

Time until initiation of FFH

12 days 4 days 24-36 hours

63 hours 6 days 89 hours 2 weeks 5 days 82 hours

Duration of deployment 13 days 6 weeks 16 days 1 week 9 days 10 days 10 days 2 weeks 10 days 11daysNumber of casualties 25,000 Hundreds

of thou-sands

2,627 705 20,005 230,000 28,000 6,300 9000

Number of injured 19000 Hundreds of thou-sands

5,084 3,500 166812 250,000 2,800 28,000 23,000

Number of beds in FFH 25 50 35 80 30 72 80 60Total number of patients 2,400 6,000 1,560 1,205 2,230 1,223 1,111 400 2686 1668

Total personnel 34 110 76 102 100 97 100 55 147 126Physicians 20 18 15 21 21 45 14 25 45Nurses 3 21 7 10 13 27 7 29Paramedics and Medics 7 4 2 18 19 21 Pharmacists 1 2 1 1 2 1 1 1Radiology Technicians 1 1 1 1 1 2 1 1 1Laboratory Technicians 1 1 1 1 1 3 2 1 1


Appendix 3. Case study interviewees

Title1 Position as participant in missions2 Time period

#1 Ph.D. in Policy, Strategy and Administration of Large-scale Emergency Situations, in the field of Response to Large-scale Sudden Di-sasters.

Team member, United Nations Disaster As-sessment and Coordination [UNDAC].

Head of the organizations F.I.R.S.T./IsraAid/medical mission to Haiti.

(2011-2015)

#2 M.D., Director of Pediatric Orthopedic Unit, Schneider Children’s Medical Center in Is-rael.

Head of orthopedic sector in delegation to Haiti. Also, participated in mission to Tur-key.

(2012)

#3 M.D., Department of Medicine, Coney Is-land Hospital, New York City.

Volunteered as physician inside tent hospi-tals built by F.I.R.S.T. in Haiti.

(2011-2015)

#4 M.D., Head of Department of Medicine, Mt. Scopus Hospital, Jerusalem.

Chief Medical Officer of IDF missions both to Armenia and Rwanda.

(2013-2015)

#5 M.D., Orthopedic surgeon and emergency physician. Has served as Adviser on Disas-ter and Emergency Medicine to the Ministry of Health in Israel and has served as Director General of Magen David Adom (Israel’s na-tional Red Cross).

Medical Adviser for the Department of Peacekeeping Operations at the United Na-tions Headquarters in New York. Active in organizing and leading humanitarian medi-cal rescue and assistance teams both to Ethi-opia and Kosovo.

(2014)

#6 M.D., Head, Trauma Unit, Shaare Zedek Medical Center, and Head, emergency hos-pital preparedness for mass causality, Jeru-salem, Israel.

Chief Medical Officer of 3 recent missions to the Philippines, Japan, and Haiti.

(2013-2015)

#7 M.D., Resident in Critical Care Division, Tel Ha Shomer and Rambam Hospitals, Israel.

Volunteered as medical resident in Haiti. (2012-2014)

1. All informants were directly involved in disaster relief missions and some occupied senior positions in key areas with medical responsibilities, which allowed them to gain evidence-based knowledge about humanitarian aid.

2. Several informants were highly ranked in United Nations Disaster Assessment and Coordination [UNDAC] and the Department of Peacekeeping Operations at the United Nations Headquarters in New York. Therefore, they have expert knowledge about the administrative aspects of collaboration between countries during relief missions.


Appendix 4. Interview protocol

Personal background:

1. Please tell us about yourself (education, working experience in healthcare, etc.)

2. What is your expertise (pediatrics, surgery, ortho-pedics, mental, obstetrics, etc.)?

3. Do you have specific training in emergency medi-cine? How many years? What types of injuries are you trained to treat?

4. How long have you been working in the area of disaster relief and in what capacities?

5. Have you been personally deployed in a past mis-sion to provide medical aid as part of a foreign field hospital?

6. What is your current rank (technician, nurse, resi-dent, M.D.)?

Israel Defense Forces (IDF)-related questions:

1. In which missions of IDF did you participate (date, country, etc.) and what type of disaster (earth-quake, flood, civil war, etc.)?

2. What was your specific role in the missions?

3. Did you get paid for mission, insurance, etc.?

4. What type of care did the FFH provide during your mission (first aid, secondary care, etc.)?

5. What type of training did you undergo before the mission?

6. Did you see differences/progress between IDF mis-sions that you have participated in over the years (preparation, equipment, training, chain of com-mand)?

7. How does IDF retain knowledge and diffuse it to new staff recruited for future missions?

8. Does IDF conduct post-mission investigations af-ter missions to learn lessons for improvement?

9. What are the advantages and drawbacks of FFHs affiliated with Israel’s military over civilian orga-nizations deploying FFHs?

10. Does IDF use specific methods developed by the military in Israel for its FFHs? Are they effective?

11. What is the impact of Israeli national culture on the behavior of staff in the disaster area?

12. What differences have you seen between FFHs built by IDF and other countries?

Mission-related questions:

1. How were the processes of assessment and prepa-ration for the mission conducted?

2. What was the deployment time from disaster oc-currence until arrival to the area?

3. How did you arrive (flight, ship, etc.)?

4. When did you personally join the mission? When did you leave?

5. How long did the FFH built by IDF stay in the field? What was the overall length of its mission?

6. What type of equipment did you use? What was missing and should be included in future missions?

7. What type of remedies did you use? What was missing (vaccines, blood, etc.)?

8. How was IDF cooperation with FFHs from other countries?

9. How was the cooperation with the local govern-ment/agencies/population? Did you use language translators? What could be improved?

10. Did you use Information Systems during the mis-sion (what type)?

11. What was the structure of the foreign field hospi-tal? What wards did it include? Can you please draw a chart? What wards do you think were miss-ing and should be in future missions?

12. What type of injuries did you treat?

13. Did you treat non-disaster-related patients with chronic diseases, etc.?

14. What was the admission process into the hospital? How was the treatment and referral process be-tween wards inside the hospital conducted? Can you please draw a chart? Did you accept referral patients?

15. How was the discharge process of patients? Did you refer to other FFHs or local hospitals?

16. What recommendations do you have for future missions (equipment, staff, resource utilization, co-operation with other organizations and countries)?


Capturing Real-Time Data in Disaster Response Logistics

Kezban Yagci Sokat PhD Candidate at Northwestern University,

Department of Industrial Engineering and Management Sciences – Evanston – IL, USA [email protected]

Rui Zhou Undergraduate Student at Northwestern University,


Irina Dolinskaya Professor at Northwestern University,


Karen Smilowitz Professor at Northwestern University,


Jennifer Chan Professor at Northwestern University,

Department of Emergency Medicine – Chicago – IL, USA [email protected]

ABSTRACT: The volume, accuracy, accessibility and level of detail of near real-time data emerging from disaster-affected regions continue to significantly improve. Integration of dynamically evolving in-field data is an important, yet often overlooked, component of the humanitarian logistics models. In this paper, we present a framework for real-time humanitarian logistics data focused on use in mathematical modeling along with modeling implications of this framework. We also discuss how one might measure the attributes of the framework and describe the application of the presented frame-work to a case study of near real-time data collection in the days following the landfall of Typhoon Haiyan. We detail our first-hand experience of capturing data as the post-disaster response unfolds starting on November 10, 2013 until March 31, 2014 and assess the characteristics and evolution of data pertaining to humanitarian logistics modeling using the proposed framework. The presented logisti-cal content analysis examines the availability of data and informs modelers about the current state of near real-time data. This analysis illustrates what data is available, how early it is available, and how data changes after the disaster. The study describes how our humanitarian logistics team approached the emergence of dynamic online data after the disaster and the challenges faced during the collection process, as well as recommendations to address these challenges in the future (when possible) from an academic humanitarian logistics perspective.

Keywords: Humanitarian logistics, real-time data, classification, logistical modeling, Typhoon Haiyan.


23

Yagci Sokat, K., Zhou, R., Dolinskaya, I., Smilowitz, K., Chan, J.: Capturing Real-Time Data in Disaster Response LogisticsISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 1 p 23 – 5424

1. INTRODUCTION

Over the past two decades, the field of humanitar-ian logistics has progressed significantly, with a growing number of researchers and practitioners studying problems, such as relief distribution, post-disaster debris removal, and evacuation of affected populations. Much work within the academic com-munity has focused on the development and appli-cation of operations research tools for humanitarian logistics (e.g., see recent surveys: Altay & Green, 2006; Ergun, Karakus, Keskinocak, Swann, & Villar-real, 2010; Kovács & Spens, 2007; De la Torre, Do-linskaya, & Smilowitz, 2010). As the ultimate goals and benefits of these efforts are to improve real-world applications, integration of in-field data is an important, yet often overlooked, component of such humanitarian logistics models. For example, in their recent review, Sangiamkul & Hillegersberg (2011) identify only two papers (Sheu, 2010; Yi & Özdamar, 2007) out of 30 surveyed that use real-time data in logistical modeling. In another survey, Ortuño et al. (2013) describe only two papers among 87 that integrate dynamically up-dated data. Finally, Özdamar & Ertem (2015) acknowledge three papers (Sheu, 2010; Yi & Özdamar, 2007; Huang et al., 2013) out of 110 studies mentioned in their review of hu-manitarian logistic models, solutions and technolo-gies that capture such data. Outside these academic disciplines, extensive efforts have been made in in-formation communication technology, especially in regard to the use of social media and crowdsourc-ing in disaster management At the same time, the volume, accuracy, accessibility and level of detail of near real-time data emerging from disaster-affected regions continue to significantly improve Consider-able efforts are currently focused on the collection, aggregation and dissemination of field data, which, together with the help of the humanitarian logis-tics decision tools, have the potential to consider-ably impact relief efforts In this paper, we present a structure for analyzing humanitarian logistics data, explore the process of retrieving real post-disaster relief data from sources available online, and ex-amine the data for the purpose of integrating data streams into response logistics models to facilitate future modeling.

We present a framework for evaluating real-time humanitarian logistics data focused on use in math-ematical modeling. The framework reflects the in-tegration of our recent experience of near real-time data collection, a survey of different communities

producing data and disciplines using data, and a development of measures to evaluate the quality of data and applicability to other disasters for logistical modeling We also discuss how to measure the attri-butes of the framework and describe the application of this framework to a case study of near real-time data collection in the days following the landfall of Typhoon Haiyan. We detail our first-hand experi-ence of capturing data as the post-disaster response unfolded, starting November 10, 2013 until March 31, 2014 and assess the characteristics and evolution of data pertaining to humanitarian logistics model-ing. The case study, illustrating our information re-trieval process, presents an example of the classifi-cation of data and data sources using the proposed framework. The logistical content analysis, using the available information following Typhoon Haiyan, examines the availability of data and informs mod-elers about the current state of near real-time data. This analysis illustrates what data is available, how early it is available, and how the data changes after the disaster. The study describes how our humani-tarian logistics team approached the emergence of dynamic online data after the disaster and the chal-lenges faced during the collection process, as well as recommendations to address these challenges in the future (when possible) from an academic humani-tarian logistics perspective.

This study signifies the importance of an interdis-ciplinary team approach when exploring real-time humanitarian logistics data, its value and chal-lenges. The retrieval of information needed for hu-manitarian logistics models and knowledge of in-field data collection and dissemination come from a unique collaboration between logistical research-ers and humanitarian practitioners. This research shows that well-formed and growing relationships allow for parties to gain insights into each other’s respective use of terminology and broader domains. Such insights may enable each party to alert the other about potential opportunities for exploration, such as the uniqueness of Typhoon Haiyan with re-gards to public data, while the modelers can inform the practitioners and the broader humanitarian re-sponse community about the needs for accessible field-appropriate data for the on-ground-personnel or agencies to aid in their operations.

The rest of the paper is organized as follows. The fol-lowing section provides some background informa-tion, which includes a literature review of different communities involved in data generation, processing


and dissemination, and a description of disciplines using humanitarian logistics data. Next, section a proposed framework for humanitarian logistics data with respect to humanitarian logistics modeling is presented with the focus on data quality and applica-bility measures, and the framework implications for mathematical modeling. An application of this frame-work to the recent Typhoon Haiyan is illustrated with a logistical content analysis and describes the lessons learned. This paper concludes with final remarks on scarcity of data and points up the need for humanitar-ian logistics models that integrates multidisciplinary work to validate the limited data.

2. DISASTER RESPONSE DATA STAKEHOLDERS

Multiple entities play a role in the evolution of post-disaster data via collection, processing or dis-semination. Furthermore, various communities are the intended users and beneficiaries of this data. Understanding the roles and motivation of the key stakeholders is essential to analyzing the emergence of near real-time data following a disaster. In this section we describe the data-gathering communities and the disciplines using the data.

2.1 Data-gathering Communities

Altay and Labonte (2014) discuss the challenges of information management and coordination amongst intergovernmental organizations, such as OCHA, government and non-governmental organizations in the case of the Haiti response in 2010. The United Nations Foundation Disaster (2011) examines the fu-ture of information-sharing in humanitarian emer-gencies with a focus on volunteer and technical com-munities, such as OSM and Crisis Mappers (2014). The response to the Haiti earthquake highlights the disaster response operations where thousands of volunteers around the world collaborated around various information communication technologies to inform the public about the affected population. Grünewald and Binder (2010) analyze the humani-tarian response following the Haiti earthquake and discuss the inter-agency real-time evaluation. They also provide the list of people consulted from dif-ferent organizations from government, donor repre-sentatives, international NGOs, national NGOs and UN agencies. These studies provide a good starting point for the main stakeholders in data gathering.

We also survey a number of practitioners and re-sponders involved with Typhoon Haiyan and oth-

er major responses (e.g., the earthquake in Haiti) through the pre-existing relationships of our team members with other humanitarian practitioners and responders. These surveys and relationships brought to our attention considerable amount of disaster relief operations relevant data and their sources. In addi-tion, the on-ground personnel involved with major responses, including Typhoon Haiyan, assisted the team with assessing reliability and understanding the nature of the datasets and the data sources.

We first present the key players we observed in the data collection, processing and dissemination, mo-tivated by the literature, survey of practitioners and our observations of publicly available online data and information sources between November 10, 2013 and March 31, 2014 following Typhoon Haiyan.

Large International Humanitarian Response Organizations

The United Nations Office of Coordination for Hu-manitarian Affairs (OCHA) aims to plays a critical role in “mobiliz[ing] and coordinat[ing] effective and principled humanitarian action in partnership with national and international actors in order to al-leviate human suffering in disasters and emergen-cies” (OCHA, 2014e). In the immediate aftermath of a disaster, when OCHA receives an international call for providing assistance, it often sends United Nations Disaster Assessment and Coordination teams to provide an initial assessment of the situa-tion (OCHA, 2014d). The information management activities within OCHA aim to support information collection and sharing needs of humanitarian actors to support coordination (OCHA, 2014c). In large di-sasters, responding organizations often coordinate in “clusters” based upon major sectoral activities, such as logistics, health and shelter. In such cases, the information collection, management and sharing are often targeted to the specific cluster’s activities. For example, in Typhoon Haiyan, specifically, OCHA facilitated the activities of 11 clusters (CCCM, 2014).

Information management activities at OCHA take advantage of different types of information in differ-ent phases of the response. According to OCHA’s de-scription of its services, they aim to create and share information in media that are simple to understand and easily accessible. Datasets including common operational datasets, contact lists, and “who, what, where” data are also maintained and shared by this group. Shared documents in the form of portable data formats (PDF) reports and maps are frequently used .


Geographic information system (GIS) data plays a key role in OCHA’s information management (IM) services. Recent collaborations with external or-ganizations, including MapAction, Humanitarian OpenStreetMap Team (HOT) and GISCorps, have advanced the timely processing of map generation. These organizations sometimes leverage microtask-ing and crowdsourcing methods to process large amounts of geographic information from imagery datasets and non-traditional geographic sources (e.g., satellite imagery, photos).

National Government

After a disaster, the host government coordinates governmental departments and agencies, such as the department of health, department of defense and emergency management authority, for its disaster response. For example, in the case of Typhoon Hai-yan, The National Disaster Risk Reduction & Man-agement Council (NDRRMC), which functions un-der the Department of National Defense, managed gathering and reporting data (NDRRMC, 2014). In addition to NDRRMC, the Department of Social Welfare and Development (DSWD) played a crucial role in providing information about affected citizens (Presidential Management Staff, Presidential Com-munications Development, & Strategic Planning Office, 2014). A detailed description of these two government offices’ role in Typhoon Haiyan with respect to data efforts is provided inthe Appendix

Digital Humanitarians

The Digital Humanitarian Network (DHN) is a con-sortium of volunteer and technical communities that “provide an interface between formal, profes-sional humanitarian organizations and informal yet skilled-and-agile volunteer and technical networks” with the purpose “to leverage digital networks in support of 21st century humanitarian response” (DHN, “About”, 2014). In the case of Typhoon Hai-yan, OCHA activated DHN immediately after the disaster. According to media reference, this was the first time officials were appointed to coordinate the crowdsourced mapping efforts with volunteer groups (Butler, 2013) during the early stage of the response. Some of these volunteer groups include HOT, Standby Task Force and MapAction. The gen-eral role of HOT is to serve as a bridge between the OSM community and the traditional humanitarian relief organizations In the Philippines, there were more than 1000 OSM volunteers from 82 countries

who provided maps to nongovernmental organiza-tions, including Doctors without Borders (Butler, 2013) and the American Red Cross (OSM, “Ty-phoon Haiyan”). The Standby Task Force analyzed more than one million texts, tweets and other social media posts with the help of MicroMappers soft-ware, which uses machine-learning techniques to filter potentially relevant messages (Butler, 2013). MapAction, a longtime partner of OCHA, worked in the Philippines to generate more than a hundred files per day to be shared with the disaster relief community. With all these efforts, the data from Ty-phoon Haiyan is a notable example of the evolution of collaboration between digital humanitarian and response agencies, where access to information, col-laboration and the next steps of information-sharing were pushed forward.

Operationally-focused Humanitarian Practitioners and Responders

Humanitarian practitioners and responders (both local and international) in affected regions are often among the most knowledgeable people about the changing post-disaster environment. They are fre-quently aware of information sources and datasets, sometimes generating data themselves, which may not only reflect the current context but also repre-sent information and data used by organizations for planning and executing response activities. In our experience, the pre-existing relationship of our prac-titioner team member with other practitioners and responders has brought tremendous value to identi-fying and better understanding various information outlets and how they can be integrated in future lo-gistical models using real-time data.

As information communication technology im-proves, connecting with responding humanitarian organizations, theoretically, is more feasible. How-ever, developing trusting relationships and personal networks still requires years of engagement in work-ing with people from various backgrounds, often having different short-term goals but with common overarching missions.

2.2 Disciplines Using Data

Current humanitarian logistics models aim to cap-ture real-time data in order to improve their deci-sion support tools. Here, we discuss how the data is used in these models and the assumptions the researchers make, especially in relation to the data


availability. Various disciplines utilize humanitarian logistics data and impact the changes in data collec-tion, processing and dissemination. Therefore, the role and purpose of each discipline as it relates to real-time data should be taken into account by the logistics modelers in order to better understand the data characteristics.

As modelers studying the real-time humanitarian lo-gistics data focused on use in mathematical model-ing ourselves, we naturally investigate the academic humanitarian logistics as one of the disciplines ben-efiting from data. The efforts on understanding data gathering communities (such as literature review and surveys) enlighten us about the other disciplines ben-efiting from humanitarian logistics data. Similarly, the work on digital humanitarians enables us to recog-nize the importance of information and communica-tion technology for understanding the current status of the real-time data, its implications and challenges. Thus, in addition to the academic humanitarian logis-tics discipline, we also discuss the role humanitarian data plays for practitioners, the intended primary us-ers of this data, and information and communication technology (ICT), the data collection, processing and communication facilitators.

Academic Humanitarian Logistics

We first describe the current efforts of humanitarian logistics models with real-time data. As mentioned previously, although the number of models related to humanitarian logistics is growing, models with real-time data are limited. We review those papers below.

Liu and Ye (2014) present a decision model for the al-location of relief resources in natural disasters using information updates. These updates predominantly contain information on disaster states (population transfer rates) and traffic conditions (road affected level). Authors suggest that this information can be obtained from the disaster database of governmen-tal agencies, such as the National Oceanic and At-mospheric Administration (2013), the National Cli-matic Data Center, and the National Geo-physical Data Center, among others. Liu and Ye apply their model to the Wenchuan earthquake in China with data provided by China’s National Committee on Disaster Reduction.

Sheu (2010) develops a dynamic relief-demand management model that forecasts the demand in real-time and dynamically allocates supplies based on those forecasts, as well as urgency and popula-

tion vulnerability measures. The main components of the information used in the model are 1) time-varying ratio of the estimated number of trapped survivors relative to the local population; 2) popula-tion density associated with a given affected region; 3) proportion of frail population (e.g., children and the elderly) relative to the total number of popula-tion trapped; 4) time elapsed since the most recent relief arrival; and 5) level of building damage. Sheu uses the official statistics from the 921 earthquake (also known as the Jiji earthquake) special report from Taiwan to demonstrate the application of the developed model. The model contains the most de-tailed amount of data in comparison to other aca-demic studies we have surveyed and is valuable for estimating regional level demand under dynamic information updates. The author also generates sim-ulation data to replace the missing data points in an effort to tackle incomplete information.

Yi and Özdamar (2007) study a dynamic coordina-tion problem of supply distribution and transfer of injured people. They apply their model to an earthquake scenario for Istanbul. The demand dis-tribution (number of wounded people) and supply distribution (people, fleet composition, and total ca-pacity transport) are provided for each time period. Researchers employ the widely used data from the Earthquake Engineering Department of Bogazici University (2002) for attrition numbers and possible structural damage to Istanbul, which are used to cal-culate the number of affected people. Information about permanent emergency units is gathered from local municipalities and the Turkish Medical Doc-tors Association. However, the information about the number and capacity of vehicles, the capacity of temporary emergency units, as well as how this information is updated are not explicitly provided.

Huang et al. (2013) study the impact of incorporating real-time data into disaster relief routing for search and rescue operations in the aftermath of the 2010 Haiti earthquake. They use OpenStreetMap (OSM) to obtain road and building data. They also extract demand information on collapsed structures and trapped persons using Mission 4636, a text-message communication initiative. This research provides insights into incorporating crowdsourced data into humanitarian logistics models.

In addition to the integration of data into logistical models, some researchers also study classification frameworks for humanitarian data. This work is discussed in more detail later (see Measures of Data


Quality and Applicability section) as it closely re-lates to our developed measures of data quality and applicability.

Humanitarian Practitioners

Humanitarian practitioners often rely on situational awareness to make critical decisions in difficult situ-ations with limited resources and time. Mica Ends-ley (1988) defines situational awareness as “the per-ception of the elements in the environment within a volume of time and space, the comprehension of their meaning and the projection of their status in the near future” The availability of timely and accu-rate data is critical to personnel making operational decisions. Therefore, there have been numerous and ongoing efforts to improve the collection, manage-ment and sharing of humanitarian data for humani-tarian practitioners such as Humanitarian Data Ex-change (HDX) (See Information and communication technology section below for more details).

There are also ongoing efforts among practitio-ners to build vocabulary standards for crisis man-agement (Limbu, Wang, Kauppinen, & Ortmann, 2012). This is of particular interest to researchers, as we observe not only differences between research-ers and practitioners in the terminology used, but even among various handbooks and guidelines in-tended for practitioners (World Food Programme [WFP], ““About””; Logistics Cluster, ““About””; WFP, ““Food Aid Information System””; Federal Emergency Management Agency [FEMA], 2010; FEMA, ““Data Feeds””; FEMA, ““FEMA’’s Inter-national Programs””; Inter-Agency Standing Com-mittee [IASC], 2012; IASC, 2010a; IASC, 2010b). For example, while humanitarian logistics researchers extensively use the term “supply”, practitioners use “supply”, “resources”, “capacity”, “stockpile” and “availability”. Until these vocabulary standards are developed and implemented in the field, researchers should be aware of the various terms different data sources might use in the same context.

The role that data plays in humanitarian operations continues to change as data gathering, processing and sharing technologies evolve. Many humanitar-ian agencies actively acknowledge, assess and fore-cast the effects of the corresponding changes. The annual World Disaster Report 2013 from the Inter-national Federation of Red Cross and Red Crescent Societies (2013) “examines the profound impact of technological innovations on humanitarian action,

how humanitarians employ technology in new and creative ways, and what risks and opportunities may emerge as a result of technological innovations” (2013). These and other similar reports can provide logistics models with insights into how data is per-ceived by the humanitarian practitioners discipline.

Information and Communication Technology (ICT)

Information and communication technology plays a critical role in facilitating data collection, processing and communication. From the academic perspec-tive, with the ongoing evolution of these technolo-gies, the studies that analyze their application to crisis and emergency management have also signifi-cantly expanded (Faria Cordeiro, Campos, & Silva Borges, 2014; Howden, 2009; Scott & Batchelor, 2013; Li, Li, Liu, Khan & Ghani, 2014; Dorasamy, Ramen, & Kaliannan, 2013; Palen et al., 2010). The majority of this research focuses on crowdsourcing and so-cial media applications in disaster responses (Ashk-torab, Brown, Nandi, & Culotta, 2014; Hester, Shaw, & Biewald, 2010; Imran, Elbassouni, Castillo, Diaz, & Meier, 2013; Manso & Manso, 2012; Munro, 2013; Ortmann, Limbu, Wang, & Kauppinen, 2011; Puro-hit, Castillo, Diaz, Sheth, & Meier, 2013; Sarcevic et al., 2012; Velev & Zlateva, 2012), with particular in-terest in Twitter (Munro, 2013). For example, Ash-ktorab et al. (2014) present a Twitter-mining tool to classify, cluster and extract tweets. The authors in-clude the keywords processed in their study, such as “bridge”, “intersection”, “evacuation”, “impact”, “injured” and “damage”, among others. The au-thors implement their algorithm to tweets collected from 12 different crises in the United States. Puro-hit et al. (2013) present machine-learning methods developed for social media specifically to identify needs (demands) and offers (supplies) to facilitate relief coordination, by matching the needs with of-fers, encompassing shelter, money, clothing, volun-teer work, etc.

DeLone and McLean (1992) develop an informa-tion system success model with six interdependent success variables: system quality, information qual-ity, use, user satisfaction, individual impact, and organizational impact. Over the years, this model has been extensively studied and improved (Pet-ter, Delon, & McLean, 2008). DeLone and McLean (2003) later update their model with the following success variables: system quality, information qual-ity, service quality, system use, user satisfaction, and net benefits. Each variable also has numerous


dimensions. For example, relevance, understand-ability, accuracy, conciseness, completeness, curren-cy, timeliness, and usability are provided for infor-mation quality, which are defined as the desirable characteristics of the system outputs such as reports and web pages. System quality is defined as the de-sirable characteristics of an information system and ease of use system flexibility, system reliability, and ease of learning, as well as system features of intu-itiveness, sophistication, flexibility, and response times. Bharosa, Appelman, Zanten, and Zuurmond, (2009) examine information and system quality as requirements for information system success dur-ing disaster management. The researchers state that, although information quality requirements are very relevant for information system success during di-saster management, they are very hard to measure. In the case of systems quality measurements, much of the effort is focused on the inter-operability and ease of use.

From the practitioner’s perspective, ICT has improved data collection, processing and communication in recent decades. UN OCHA’s (2002) Symposium on Best Practices in Humanitarian Information Exchange resulted in humanitarian information management principles as: accessibility, inclusiveness, inter-opera-bility, accountability, verifiability, relevance, impartial-ity, humanity, timeliness and sustainability. In a later symposium, reliability, reciprocity, and confidentiality were added to the list (Haggarty & Naidoo, 2008). The ongoing Humanitarian Data Exchange (HDX) proj-ect, led by OCHA, aims to “make humanitarian data easily available and useful for decision-making,” by bringing together multi-country, multi-sourced, cu-rated data for analytical use through a single platform (OCHA, 2013c). As part of HDX project, Humanitar-ian Exchange Language (HXL) is intended to offer the standardization of humanitarian data (OCHA, 2013a). In order to further facilitate standardization, the HDX Quality Assurance Framework identifies five dimen-sions of quality as accuracy, timeliness, accessibility, interpretability and comparability (OCHA, 2014b). Lo-gistics modelers can benefit and often directly utilize the data collection and processing tools developed by the ICT discipline.

3. FRAMEWORK FOR ANALYZING REAL-TIME LOGISTICS DATA FOR MATHEMATICAL MODELING

This section presents the framework for analyzing real-time post-disaster data, specifically focusing on

the measures that describe the quality of data and data sources, as well as their applicability to differ-ent disasters for logistics modeling to learn from past disasters. Information during the aftermath of a disaster can more frequently be found on websites and is often shared via listservs and emails. Each di-saster context will vary in the degree of online infor-mation access for several reasons, such as: the type of disaster (e.g., disasters with predictable timing and location, such as hurricanes, versus disasters with unpredictable timing, such as earthquakes), availability of information technology, and level of involvement of host nations and their national and local governments. In order to better assess the quality of numerous information sources that emerge after a given disaster and their applicability to other disasters, we classify the outlets and data provided from these outlets based on a number of measures relevant to the focus of this study. We first determine broad areas of quality and applicability measures, which help us understand humanitarian logistics focused real-time data and their indication for modeling. We then introduce attributes of data and data sources to explain each measure in detail and discuss the implications of the presented attri-butes and measures for disaster response logistics models. Finally, we address how to measure the at-tributes of the proposed framework.

3.1 Measures of Data Quality and Applicability

In order to develop the framework, we first review the data standards and literature from the disci-plines described earlier in the disciplines using data, as well as survey our practitioner contacts for their input. Based on our previous modeling experiences and observations from the data and data sources over time, we identify four fundamental questions about the data and data sources, which lead us to the mea-sures in the framework. These questions are: what data is available (relevance), when data is available (timeliness), where data is available and to what de-gree data represents the surrounding environment (generalizability), and the degree to which the data reflects the true environment (accuracy). We believe these four categories emphasize the critical charac-teristics that describe the quality of data and data sources and their applicability to different disasters for logistical modeling. As a result, we propose the following four measures: 1) relevance, 2) timeliness, 3) generalizability, and 4) accuracy. Similar to mea-sures, we survey the literature and practitioners to identify attributes that sufficiently represent each


measure and their implications for logistical mod-eling. The attributes are described in this section in detail. Figure 1 outlines our developed framework for analyzing real-time logistics data for the purpose of use in mathematical modeling. Following a disas-ter, as the data starts to become available, the data user evaluates it based on his or her purpose (e.g., research, situational awareness and decision- mak-ing on the ground). As a result, the importance of overall quality measures and the level of their ap-plicability depend on the purpose of the data user; thus we span these measures around the user’s purpose. The data format is highly correlated with the purpose of the research team and refers to the format of the files that data or the information rel-evant to our humanitarian logistics modeling focus is represented. Day, Junglas, and Silva, (2009) and Altay and Labonte (2014) stress inconsistent infor-mation and data formats as one of the information flow impediments that impact decision-making and coordination in the humanitarian response. The data is available in many formats from portable data for-mats (PDF) to keyhole markup language (KML) and Microsoft Word Documents (.doc).

The data format plays a critical role in the acces-sibility of data and smooth integration of data into models. Many of the available files identified in our case study (see sectionFramework Application:

Typhoon Haiyan Case Study) depict humanitar-ian logistics information, such as roads and hospi-tals, yet frequently only in static formats. Optimal data formats for integration with logistics models are editable documents and dataset file types. For example, PDF maps often contain a great amount of information about the severity of damage in an area; however, due to their format limitations, it is hard to access information about road structure. Many released reports do not contain easily or quickly transferable numerical data. KML files and shape (SHP) files might contain the relevant data for testing models, however not in an immediately accessible manner, often requiring file conversions and format manipulations. On the other hand, even after these files are converted, discrepan-cies may exist between the content. Two different sources converted through the same online SHP to comma separated value (CSV) formatter (Geodata Converter, 2014) can return different marking sys-tems, e.g., XY coordinates vs. “osm_id,” as location indicators. This variation may be problematic for analysis, since it might require multiple infrastruc-ture information sources. On the other hand, some sources contain Microsoft Excel data spreadsheets, and classification in this data simplifies the identi-fication process of sources with promising data for the purpose of logistics models.

Figure 1. Data Analysis Framework: Applicability Measures and Related Attributes


Next, we describe the measures and attributes used in our framework based on their utility for logistical modeling and potential challenges. We should note that, while some attributes might refer to multiple measures (e.g., the classification category primary purpose might infer about the relevance and gener-alizability), some attributes can be related (e.g., local factors and disaster properties).

Relevance

Relevance is determined by whether the data meets the needs of its users The relevance of the data ob-tained from the data streams refer to the degree to which the data meets the current and future needs of the data required for logistical modeling. For our framework, we identify the following measures that represent relevance.

Logistical Content

Humanitarian and relief organizations constantly collect, process and disseminate immense amounts of information in a broad range of settings and ap-plications. Our work and this specific paper focuses on humanitarian logistic operations in post-disaster settings, such as on-the-ground operations immedi-ately following natural or man-made disasters (e.g., immediate medical assistance or search and rescue operations). Thus, our first step consists of identify-ing specific types of information relevant to humani-tarian logistics. The data is divided into the follow-ing categories: demand, supply and infrastructure. These categories are similar to those used in the lit-erature (Ergun et al., 2010a; Tatham & Spens, 2011).

– Demand: Effective and efficient relief efforts require the identification of the location, quan-tity and types of supplies needed within the affected region. Demand in these settings can correspond to needed physical goods, such as food, medication or shelter, as well as needed services, such as medical assistance, rescue, and telecommunication.

– Supply: Information about relief supplies that are pre-existing or gathered after a disaster, trans-portation vehicles and expert or key personnel (e.g., see (OCHA, 2013b) for examples specific to Typhoon Haiyan) is another important compo-nent for the relief efforts.

– Infrastructure: In order to facilitate distribution of supplies to demand, we need to have knowl-

edge of the infrastructure (e.g., roads, airports, seaports and their post-disaster conditions). While this critical component can be highly un-certain, there is great potential benefit from ac-curate and timely data from the field.

Primary Purpose

Primary purpose indicates the focus of the infor-mation posted in the outlet and/or role of the or-ganization providing the information. Some of the examples are initial assessment, evacuation and providing maps. The primary purpose of the data outlet or organization might help researchers search for relevant information for their models. For exam-ple, if a researcher is working on search and rescue operations, it might be easier for him or her to start from an outlet that is focused on initial damage as-sessments. More specifically, in the case of Typhoon Haiyan, a researcher can begin his or her analysis from CEMS, NDRRMC and OpenStreetMap (see Table 2for the primary purpose of surveyed infor-mation outlets in this study).

Outlet Type

Outlet type identifies the ownership of information, as observed in our study. We distinguish between two types of information outlets: original informa-tion outlets and aggregator information outlets. Original information outlets refer to organizational websites that primarily provide information and data, either collected by that organization or trans-form the data for their specific response purposes. We use the term aggregator for outlets that predomi-nantly disseminate information collected by various other sources.

Outlet type classification can be helpful for practitio-ners and researchers for the following reasons. Orig-inal information outlets might be a good choice to look at when a researcher or practitioner is search-ing for a certain type and/or format of information, such as maps or reports about damaged areas. In this case, researchers or practitioners might need to search for several original information outlets to find particular data or accumulate a series of dif-ferent information pieces. Aggregator information outlets generally compile information about supply, demand and/or infrastructure from assorted outlets. Thus, a researcher might want to start his or her ini-tial search from these sources.


Timeliness

The timeliness of data refers to time dimensions of the collected, analyzed and disseminated data. Timeliness is deemed by many users as generally the most important characteristic of data (Teran, 2014). Humanitarian data should be collected, an-alyzed and disseminated efficiently, and must be kept up to date (OCHA, 2002). Timeliness is also defined as the delay between when the data is col-lected and when data becomes available and acces-sible . Timeliness is a crucial measure for modelers as it highly impacts the types of the models they can feasibly implement.

Data Update Type (new update/incremental vs. overwrite)

Data update type represents the method by which the status updates are provided after the initial file upload. Incremental updates suggest that the new information is described in a new file or new field. On the other hand, overwrite updates indicate that the additional information is appended to the exist-ing file containing the original information.

Practitioners and researchers may have different needs with respect to data updates. While practitio-ners may seek the most current data with cumula-tive statistics for operational decision-making dur-ing a certain disaster, logistics modelers often prefer to see the evolution of the post-disaster data. As a result, while an overwrite update may be preferable for practitioners, it is not desirable for humanitarian logistics researchers who focus on adaptive model-ing. Modelers generally find the piecewise informa-tion about additional available roads or estimated needs (e.g., refugee camp population requiring food assistance from WFP) at a location more useful. Up-date type also provides important implications for the prioritization of the data collection process for research purposes. Sources such as OpenStreetMap may have openly available data that archives the prospective near real-time updates of roads, which can be retrieved later on, while other map sources that have overwrite updates should be monitored frequently to capture the evolution of data over time.

Update Frequency

Update frequency represents the frequency with which data is updated. Update frequency can be by minute, hour, day or other. Due to the nature of humanitarian operations and impact of time in the

output, the humanitarian community benefits from data being updated timely and frequently.

Update frequency may have a large influence on modeling decisions. In our case study (see Table 2), many organizations that share data sources appear to update and upload their datasets daily and fre-quently post new content data related to logistics. This may influence the model type and the inclusion of the dynamic information into the models. For ex-ample, as the frequency of the information increas-es, a researcher might prefer dynamic programming to multi-stage optimization for modeling.

Data Update Timeline and Retention Time

While both data update timeline and retention time are associated with the time perspective of data, up-date timeline refers to the timeline from the initial time data starts to be uploaded until the last time data is uploaded. On the other hand, retention time captures the last time when the data will be avail-able for public use. In other words, update timeline describes when files are updated on the website, e.g., from November 1, 2014 to December 15, 2014. Retention time is for how long that data will stay up on the website, e.g., the data can be accessed for an-other year after it has been uploaded. Retention time is also often associated with the establishment type of the information outlet, which is discussed later.

As stated before, timeliness is generally one of the most important measures of data and it has sev-eral implications for modelers. Different update timelines might express different values to vari-ous types of modeling purposes. The first available post-disaster data is crucial, especially when one wants to find as much information as possible to understand the immediate context and link infor-mation with high-priority humanitarian logistics activities within a certain period after the disaster. For example, the data available during the golden time (first few days after the disaster) is vital for models focused on search and rescue operations (Huang et al., 2013). As the initial few days pass after the disaster, the information about the sup-plies (which team is where with how much medical or other supplies) becomes widely available. This information provides a basis for the relief-distri-bution modeling. Additionally, longer timelines imply more data for the modelers and this allows them to conduct more comprehensive analysis for test case generations of past disasters.


Similar to update type, retention time might also impact and aid in enhancing the data collection pro-cess. Longer retention times enable researchers to access time-sensitive critical information. Postpon-ing the collection of data that remains well after its posting date may allow for greater time spent on more volatile sources.

Generalizability

The generalizability measure in this framework in-dicates the applicability of the information obtained from the data resources of a particular disaster to other disasters for preparedness, analysis, lessons learned and evaluation. We determine an estab-lishment’s local versus global designation, disaster properties and local factors as key indicators of gen-eralizability. Generalizability facilitates modelers to learn from previous disasters to improve the pre-paredness and response to future disasters.

Local/ National vs. Global/International

This classification addresses whether the informa-tion source is administered by an international organization or a local government/organization where the disaster occurs. It signifies the level of involvement from local government in the disaster response operations.

Local/national versus international data ownership might inform about the generalizability of this data and analysis for future disasters. For example, the level of involvement from the local government for the post-disaster response can be included in the dis-cussion of different disaster comparisons. Similarly, depending on the disaster type, pre-disaster evacu-ation efforts of the local/national government can be a critical factor when comparing different disasters and making inference from them.

Disaster Properties

As the name suggests, this attribute describes the main characteristics of a disaster. Tatham et al. (2013) develop a 13-parameter framework that cap-tures the factors impacting logistical preparedness and response. A significant part of the classifica-tion categories from Tatham L’Hermitte, Spens, and Kovács’s framework, such as the time available for action (disaster onset), disaster size, magnitude of impact, duration of time and environmental factors (such as the topography or weather conditions of the

affected area) are related to disaster characteristics used in our framework.

Disaster characteristics can educate modelers about decision-making in different stages of the disaster cycle. For example, a sudden-onset disaster with predictable timing, such as Typhoon Haiyan, can help modelers and practitioners on the ground to improve the prepositioning strategy to save as many lives as possible. In addition, disasters with predict-able timing impact the level of information available in the response phase by advance notice, which can impact the specific characteristics of the model and model validation.

Local Factors

The World Bank Logistics Performance Index (LPI) measures the “friendliness” of a country based on six factors: customs, infrastructure, services quality, timeliness, international shipments and tracking/tracing (Arvis, Saslavsky, Ojala, & Shepherd, 2014). Tatham et al. (2013) suggest the Logistics Perfor-mance Index as one of the four factors impacting the logistical preparation and response. While LPI focuses on factors that impact logistical performance directly such as infrastructure, local factors refer to metrics for local environment. L’Hermitte, Bowles, and Tatham (2013) present a classification model of disasters from a humanitarian logistics perspective. Their model composes the time and space compo-nents of the disasters and five external situational factors of the disaster environment. The external factors stated in the paper are the government situa-tional factors, the socioeconomic situational factors, the infrastructure situational factors, the environ-mental situational factors, and the conflict environ-ment. Five external situational factors of the disaster environment of L’Hermitte et al.’s work (2013) and some of the categories from Tatham et al’s 13-pa-rameter framework such as the geographic context, population density, per capital GDP, and potential for the reoccurence of the disaster in the same area are examples of local factors. Another local factor is the language of the local environment, as in the case of the 2010 Haiti earthquake (United Nations Foun-dation, 2011) .

According to existing studies (e.g., (Vaillancourt, 2013)), as the value of LPI increases, the expected number of affected people generally decreases. Lo-gistics modelers can benefit from this information when estimating service demand for a given region.


Using a country’s LPI value, modelers can evaluate applicability of the available data from one country to another. Furthermore, higher LPI scores usually correspond to less restriction from the government (Haavisto, 2014) on relief response operations, cor-responding to another generalizibility measure of the data.

Geographical context of the local area where a disas-ter takes place might also provide multiple insights for modeling. For example, the Philippines being a combination of islands implies routing decisions us-ing different modes of transportation, as well as the coordination of relief items among islands. Further-more, whether or not a given island is the hub for re-lief operations can also impact the routing decisions. Another local factor, disaster reoccurrence probabil-ity, can provide useful information to logistics mod-elers at various stages of the disaster management cycle. For example, Ergun, Stamm, Keskinocak, and Swann (2010) describe numerous efforts of Waffle House Restaurants to effectively respond to hurri-canes in southeast US. These efforts include equip-ment prepositioning, special menus and advanced personnel scheduling. Similar strategies can im-prove disaster management in areas that are prone to storms such as the Philippines.

Accuracy

Multiple researchers discuss data accuracy directly or using related terms, such as reliability, verifiability and accountability (OCHA, 2002; Haggarty & Nai-doo, 2008; OCHA, 2013a; OCHA, 2013b; Galton & Worboys, 2011; Day, Junglas, & Silvas, 2009; Altay & Labonte, 2014; DeLone & McLean, 2003). According to the Humanitarian Data Exchange Quality Assur-ance Framework, the accuracy of the data is defined as “the degree to which the information correctly de-scribes the phenomenon it was designed to measure” Synthesizing the definitions from these resources, we define accuracy as the measure that represents the reliability and the credibility of the information ob-tained from the data streams and data sources.

DeLone and McLean (2003) emphasize accuracy in their information success model. For example, ac-curacy-related terms appear in systems quality as “system reliability”, in information quality as “ac-curacy”, “conciseness” and “completeness”, and in service quality as “accuracy” and “reliability”. Day et al. (2009) state that self-reported informa-tion like shelter registration is generally unreliable.

Altay and Labonte (2014) assess unreliability as an information impediment for decision-making and provide several sources of unreliable information examples from the case of the earthquake in Haiti in 2010, such as crowdsourced data. They also note that, rather than waiting for the perfect information, practitioners should utilize the available informa-tion and make sure coordination is stressed in the information-sharing. From a modeler’s perspective, accuracy implies various assumptions about the available information, as well as the unknown data.

Establishment Type

Establishment type denotes whether the placement of the data (e.g., website or repository) was established specifically and solely for the purpose of a specific disaster or maintains data across multiple disasters. We use establishment type to separate data sources into two categories: multiple disaster and disaster specific. Multiple disaster sources correspond to or-ganizations and websites involved with data on di-sasters prior to a given disaster, often retaining data from such disasters. Such sources or repositories usu-ally retain data for multiple disasters after the relief operations are completed, e.g., ReliefWeb. Disaster-specific sources generally provide information dur-ing relief operations of a specific disaster and may become unavailable shortly afterwards.

Establishment type can often inform researchers and practitioners about reliability, completeness and accuracy of information since it often relates to the structure of the organization that maintains the information outlet. Multiple disaster data sourc-es may possess additional verification processes, which may increase their reliability. However, these data sources (e.g., PDF maps) rarely contain the raw pre-processed data. In contrast, based upon our spe-cific case study experience, we observe that disaster-specific sources may directly post datasets without progressing through a verification process, or may not fully share the verification process with the pub-lic. They may also lack the level of reliability and trust in comparison to sources established for previ-ous disasters. Regardless of the situation, connect-ing with practitioners at appropriate times might assist in further understanding how and whether data is verified. It may also open up opportunities to explore datasets in pre-filtered formats, reliable short-term sources, or data sources more relevant to practitioners. However, datasets may not necessar-ily be complete or entirely accurate, requiring data


fusion or synthesis of various datasets together to achieve logistics modeling requirements. The resul-tant fused datasets will need to be reassessed for ap-plicability for effective on-the-ground operations.

Coordination Level

This attribute represents the level of coordination among different communities during a disaster re-sponse. It might indicate various types of collabo-ration, such as coordination between different relief agencies and coordination between local and inter-national governing bodies. The concept of coordi-nation through subgroups introduced by Jahre and Jensen (2010) aims to organize humanitarian help in a number of different areas by predefined man-agement. Jahre and Jensen (2010) discuss the impor-tance of the balance between horizontal and vertical coordination in any period of disaster management. Additionally, the authors mention the role of logis-tics cluster, one of the 11 OCHA formed clusters, on information management and the challenges of co-ordinating the information.

One key component to a successful coordination is the exchange of information, which in return results in additional information generation (e.g., cluster reports to be shared with participants). In addition, the involvement of various parties in the combined mission improves data accuracy as information is validated by the distinct participants. Moreover, similar to the establishment’s international versus local designation, the level of cooperation of the lo-cal government with international communities in the disaster response efforts impacts the evolution of available data.

Completeness

Completeness represents whether there is missing information or not. Examples of incomplete infor-mation might be in regards to the status of certain roads or damage level of buildings.

Missing data raises questions regarding the accuracy of information to be used in the logistical models and forces modelers to account for the incomplete infor-mation. For example, when road status information is not provided, the modeler needs to make assump-tions about that information. In order to account for the inaccurate and incomplete information, uncer-tainty factors should be included in the models.

Assessment of Framework Attributes

Table 1 provides an example of a metric that might be used to assess and categorize each framework at-tribute developed above, as well as sample metric categories for illustration purposes. Some of these attributes and the metrics have been previously in-troduced in existing literature, in which case we in-clude the appropriate references in the last column of Table 1. As discussed by Bharosa et al. (2009), some parameters, such as relevance, accuracy, and timeliness that signify information quality, are of-ten hard to measure during disaster management. Thus, what we provide in Table 1 is just one set of examples to measure the attributes, and there might be many other ways to measure each of them. For example, in addition to the sample metric provided in Table 1, it is possible to measure the coordination level using the number of agencies sharing resourc-es or presence and the role of a single agency during a disaster such as local government.

Table 1. Assessment of Framework Attributes

Framework Measure Attribute Metric Example Sample Metric

Categories Reference

Relevance Logistical Con-tent

number of logistical content categories that the data source cover

a) infrastructure b) de-mand c) supply d) all

Ergun et al. (2010a)

Primary Purpose main focus as described in mission statement

a ) (initial) post disaster assessment, damaged in-frastructure b) relief aid c) coordination d) all

Outlet Type percentage of the con-tent that is originally prepared by the data source

a) < 20% (aggregator) b) 20-50% (mixed) c) >50% (original)


Timeliness Update Type number of previous ver-sions of the file stored

a) 1 (overwrite) b) >1 (incremental)

Update Fre-quency

average time between two consecutive updates

a) hourly b) daily c) weekly d) monthly e) one time

OCHA (2002)

Update Timeline average time between the first and last update

a) > 1 year b > 3 months c) >1 month d) > 1 week

Retention Time average time the infor-mation is kept by the data source

a) > 5 years b) > 3 years c) > 1 year d) < 1 year

Generalizability International vs. Local Establish-ment

percentage of funding available from local government

a) < 50 (local) b) > 50 (international)

Disaster Proper-ties

number of people im-pacted

a) > 1,000,000 b) > 10,000 c) > 1000 d) > 100

Tatham et al. (2013)

Local Factors LPI score a) > 4 b) >3.5 c) >3 d) <3

Tatham et al. (2013)

Accuracy Establishment Type

total number of disasters involved

a) 1 (disaster-specific) b) >1 (multiple - disaster)

Coordination Level

average number of agencies listed in the reports

a) 1 b) >5 c) >10 d) > 20

Jahre and Jensen (2010)

Completeness percentage of the at-tributes tagged or have information about

a) > 20% b) > 10% c) > 5% d) < 5%

DeLone and McLean (2003)

4. FRAMEWORK APPLICATION: TYPHOON HAIYAN CASE STUDY

To illustrate the application of the framework pre-sented above, we focus on a specific disaster re-sponse, Typhoon Haiyan, as the case study for inves-tigating the role, value and limitations of integrating new information streams for logistical modeling during the aftermath of a disaster. On November 8, 2013, Typhoon Haiyan, named as Typhoon Yolanda, the strongest storm recorded at landfall (Open Street Map [OSM}, “Typhoon Haiyan”, 2013) and one of the strongest tropical cyclones in recorded history (National Oceanic and Atmospheric Administration, 2013), hit the Philippines and resulted in catastroph-ic damage throughout the country. As of April 7, 2014, 6,300 individuals were reported dead, 28,689 injured, and 1,061 are still missing according to the National Disaster Risk Reduction and Management Council (NDRRMC) (Government of the Philip-pines, 2014). Both the Philippines government and

international humanitarian organizations started their preparedness activities as early as November 7, 2013 and began response activities immediately following the fall.

Typhoon Haiyan represents an evolution of disas-ter response in which the emergence and growth of data during relief operations brought new op-portunities for addressing humanitarian challenges. Multiple factors played a role in the generation of this outstanding level of information, including lo-cal factors, the nature of the disaster and efforts of OCHA and the availability of data from the Philip-pines government responding entities. More spe-cifically, the Philippines benefited from advanced information communications technology and wide-spread media and organizational coverage within the country in the disaster response efforts.

As this research is carried out by an English-speak-ing team, the synergy between information and data


predominantly shared in the English language pro-vides us an opportunity to pursue this case study. In addition, the Typhoon Haiyan post-disaster environ-ment was permissive with respect to information-sharing across stakeholders; unlike conflict-driven, complex humanitarian crises where repressive en-vironments often restrict information-sharing, es-pecially through public online sources. Typhoon Haiyan is a sudden-onset disaster with relatively predictable timing and location, which made it pos-sible for the advance staging of volunteers. OCHA’s call for digital volunteer support through the Digital Humanitarian Network prior to the typhoon activat-ed volunteers around the world to participate in col-lecting and processing information (OCHA, 2013d). In addition, the growth of digital humanitarians or “crisis mappers” has expanded nontraditional data streams during recent crises (Haiti earthquake,

Pakistan, Chile, Christchurch, Bhopa, super-storm Sandy, etc.), often in online formats and frequently available to the public.

In this section, we first present the timeline of events following Typhoon Haiyan for the case study. Next, we examine the data sources and provide the clas-sification of the data and data sources based on the framework developed in Section 3. We provide a brief logistical content analysis to assess the avail-able information for logistical models. Finally, we present lessons learned from this case study.

4.1 Information and Data Retrieval

This section highlights the chain of events describ-ing our information retrieval process. Figure 2 il-lustrates the information and data retrieval epochs of this study.

Figure 2. Information and Data Retrieval Epochs

Initialization

Shortly after November 8, 2013, when Typhoon Hai-yan moved into the central Philippines region, one of our team members, who is an active researcher and practitioner in the humanitarian technologies community, began receiving numerous email com-munications from the crisis mappers network These emails contained publicly available website links to information sources pertaining to Typhoon Haiyan. This network is also linked to other digital humani-tarian groups that were being activated by the Digi-tal Humanitarian Network, including the Standby Task Force, Humanitarian OpenStreetMap Team, GISCorps, HumanityRoad, Info4Disaster, MapAc-tion, Translators without Borders, Statistics without Borders and other members of the Digital Humani-tarian Network (DHN, “Super Typhoon Haiyan”) .

Collection and sorting

The team recruited an undergraduate research as-sistant to collect data beginning on November 12, 2013, starting with the resources identified in ini-tial emails. As a first step, we iteratively devised a sorting scheme for data intake. After teasing out the emails relevant to the typhoon, our initial approach in the first days, between November 12, 2013 and November 19, 2013 was to continuously download from the data sources identified on those websites, such as Logistics Cluster and Copernicus Emer-gency Management Service (CEMS), with the goal of capturing the temporal aspect of the available data. The potential relevance for each data source was assessed using the accompanying description of the data. However, since time constraints and our advancing knowledge of humanitarian information


sources did not allow for a complete understanding of each dataset at the time of retrieval, we continued to download from as many of these sources as we could find, in the hopes that each of those sources might contain desired information.

Prioritization

After one week of sorting data, around Novem-ber 19, 2013, we realized that the volume, both in number and size, of data was overwhelming. For example, the Humanitarian Response had over a hundred new files (Humanitarian Response, 2014c) and OpenStreetMap had several gigabits of data (OSM, “Index of Haiyan”. The magnitude of the data created a need to balance the trade-off between processing the known data sources and searching for new sources. After the initial few days of collect-ing a breadth of data, we attempted to focus on un-derstanding the content of the resources, especially with respect to their logistical content. For almost a week, we focused on differentiating between logisti-cal content, storing and archiving current data ac-cording to its content type.

Searching for new sources

As time passed, around November 25, 2013, we con-tinued to search for new data sources. The sources from the email lists served as a starting point, and allowed for retrieval from some important sources earlier than otherwise possible. However, finding new sources required other methods, such as sub-scriptions to appropriate newsletters and mailing lists, and manual internet searches. Yet, even with these methods as aids to supplement the manual search for sources, we were not able to identify all additional sources, particularly those not openly shared on the internet. This illustrates the challenge to discover the relevant sources far enough to reduce lost data and the limits of remote research activities that explore field operations. Within this particular case study, many relevant sources, e.g., the OSM re-pository specific to Typhoon Haiyan, were further researched by our team over one month later, de-spite HOT and the American Red Cross activities commencing very early in the response, around No-vember 8, 2013.

Reorganization

With the addition of new resources, the team ac-quired significant information and continued con-tent differentiation, storing and archiving. On De-cember 4, 2013, the team noticed the duplication of resources and decided to reorganize the list of them. At this point, the team discontinued downloading from repeating outlets.

Analysis

The team started the analysis of downloaded data on November 21, 2013, which consisted of the initial assessment of the data content, especially the logis-tical content discussed in measures of data quality and applicability section While the team worked on analysis and data collection in parallel after this point, the detailed analysis of data sources classifica-tions began on December 5, 2013.

4.2 Data and Data Sources Classification

In this section, we implement the framework pro-posed in the study to the data obtained from Typhoon Haiyan. We begin with the detailed description of the studied data outlets (see Appendix for details). The information posted at the outlets, generally in their “about us” and data pages are analyzed based on their relevance to humanitarian logistics models dur-ing the timeline of the study, between November 10, 2013 and March 31, 2014. In developing measures of data quality and applicability we identify two types of information outlets: information aggregation sites and organization or sector specific data repositories (original, in other words, primary outlets). However, this dichotomy may not necessarily be entirely dis-tinct, as some organization-focused sources will also include some data collected from other groups; for example, MapAction takes advantage of data from NDRRMC to generate some of the maps. Thus, one might expect to categorize MapAction as an aggre-gator. Also, there might be more information outlets providing information after Typhoon Haiyan. This classification includes analysis of a subset of avail-able resources. Table 2 comprises a subset of the proposed classification categories such as outlet type, primary purpose and logistical content as they apply to the studied data sources.

Yagci Sokat, K., Zhou, R., Dolinskaya, I., Smilowitz, K., Chan, J.: Capturing Real-Time Data in Disaster Response LogisticsISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 1 p23 – 5439

Table 2. Classification of Information Data Files Collected by Our Team(see Section 3 for details on classification categories and Appendix for description of sources)



4.2 Logistical Content Data Availability Analysis

In this section, we examine the availability of logisti-cal content information following the days after Ty-phoon Haiyan. As discussed in disciplines using data section there are differences in the terminology used by researchers and practitioners. In order to find the relevant information (e.g., demand and infrastructure) to integrate into models, academic researchers should first learn about these differences. For example, in the case of Typhoon Haiyan, while searching for relevant information, we establish a set of key search terms that are used to identify the potential data sources within our compiled database that contain information about service demand and infrastructure. We use sources from different disciplines described in Section to help us build the list of these search terms. Then, using this list, we examine the data gathered from sources such as OpenStreetMap, Logistics Cluster, NDRRMC, and Copernicus Emergency Management Service (CEMS) for availability of the logistical content (see Table 2 for logistical contents and the Appendix for description of these data sources). The filtered data is then analyzed to identify the specific content they contain in relation to the kind of information needed for logistical models. For example, we want to know the level of damage each road link sustained, its post-disaster status and location of potential beneficiaries of medical and rescue services.

In the analysis of data availability for road damage, we look specifically at the data for Tacloban City, which is expected to have more data in comparison to more rural areas (DHN, 2014b). The number of

roads labeled with some level of damage and the to-tal number of roads recorded are compiled for each day between November 7, 2013 and November 28, 2013 using OSM and CEMS. However, even by No-vember 28, 2013, only approximately 5% of the road links among these data sources are labeled to con-tain at least some level of damage. This result dem-onstrates how limited logistics data is for modeling following a disaster.

As an example, Table 3 shows the details about the information provided by NDRRMC regarding the road and port status between November 7, 2013 and November 13, 2013 in its situation reports. Regard-ing road status, the reports include information about the name of the road, status (not passable or passable) and additional comments (why it is not passable or status of ongoing clearance operations). The reports before November 8, 2013 12:00 pm do not include road status information. The information provided in the reports is cumulative; in other words, a road that was previously identified as impassable is kept in the following reports until November 13, 2013. As of November 13, 2013 6:00 pm all roads that were previously affected are stated to be passable and road status information is not included in the following situational reports. In the entire Philippines area, the reports include at most only 18 roads. The airport information consists of the name of the suspended airports as well as cancelled flight information. Data related to sea ports include the name of the port and number of strandees by type (passengers, vessels, rolling cargoes and motor banca boats).

Table 3. Road and Port Status Information Update after Typhoon Haiyan between 11/8/2013 and 11/13/2013 in NDRMMC Situational Reports

Update Date Update Time Number of Roads with Status Information

Number of Roads Reported Not

Passable

Number of Suspended Airports

Number of Sea Ports with

Strandees11/8/2013 12:00 PM 5 5 5 1211/8/2013 6:00 PM 5 5 13 1511/9/2013 6:00 AM 5 5 13 2011/9/2013 6:00 PM 13 13 4 411/10/2013 6:00 AM 18 18 4 311/10/2013 7:00 PM 18 3 4 011/11/2013 6:00 AM 18 3 4 011/12/2013 10:00 AM 18 3 4 011/12/2013 10:00 PM 18 3 4 011/13/2013 7:00 AM 18 2 0 011/13/2013 10:00 PM 18 0 0 0


In order to obtain service demand information for logistical models, we also examine the available building data, which might provide information about the individuals either trapped under col-lapsed structures or displaced persons due to loss of property. The building damage reports vary by different data sources. Table 4 contains a sample

of information from the NDRRMC reports on the number of damaged buildings (totally or partially damaged), deaths, injured, and missing individu-als, affected families and persons, and stranded in-dividuals between November 8, 2013 and Novem-ber 13, 2013. These numbers are aggregated for the entire Philippines.

Table 4. Demand Information Update after Typhoon Haiyan between 11/8/2013 and 11/13/2013 from NDRMMC Situational Reports

Update DateNumber of Damaged Houses

Number of Total Damage

Number of Partial Damage

Number of Deaths

Number of Injured

Number of Missing

Number ofAffected/

Pre-Emptively Evacuated Families

Number ofAffected/

Pre-emptively Evacuated

Persons11/8/2013 26675 12560411/8/2013 3 7 151910 74857211/9/2013 4 7 4 161973 79201811/9/2013 3438 2055 1383 138 14 4 944597 428263611/10/2013 3480 2071 1409 151 23 5 982252 445946811/10/2013 19651 13191 6360 229 48 28 2055630 949784711/11/2013 23190 13473 9717 255 71 38 2095262 967905911/12/2013 41176 21230 19946 1774 2487 82 1387446 693722911/12/2013 149015 79726 69289 1798 2582 82 1387446 693722911/13/2013 149756 80047 69709 1833 2623 84 1387446 693722911/13/2013 188225 95359 92886 2344 3804 79 1730005 8012671

For more detailed building data analysis, we next focus on five cities: Tacloban City, Guiuan, Palo, Or-moc and Cebu. Notably within three of these cities (Tacloban City, Guiuan and Palo), the percentage of buildings with “collapse” or “damage” indicators range between 40% and 60% by November 20, 2014. This analysis is conducted by using OpenStreetMap and CEMS. Table 5 shows the total number of build-ings and number of damaged or collapsed build-

ings for each city between November 8, 2013 and November 20, 2013. The information about building damage appears to be delayed in these sources in comparison to NDRRMC reports. Even in Tacloban City, the damage information starts on November 14, 2013. This is another example of limited informa-tion immediately after disaster, especially for search and rescue purposes.


Table 5. Demand Information Update after Typhoon Haiyan between 11/8/2013 and 11/20/2013 in OpenStreetMap and CEMS

We next examine the available data for supply infor-mation. For illustration purposes, we examine two pieces of supply information. Table 6 depicts the number of vehicles available each day throughout November, beginning on November 11, 2013 from

UN OCHA’s Logistics Information About In-Kind Relief Aid (LogIK) records. The table includes the number of vehicles decided by certain dates and their status categories (i.e., dispatched, committed, delivered) as of December 8, 2013.

Table 6. Vehicle Information Update after Typhoon Haiyan between 11/8/2013 and 11/13/2013 from LogIK Entries

Date Number of Vehicles Dispatched

Number of Vehicles Committed

Number of Vehicles Delivered by

Total Number of Vehicles

11/11/2013 1 4 13 1811/12/2013 2 6 18 2611/13/2013 3 9 33 4511/14/2013 4 10 48 6211/15/2013 4 11 54 6911/16/2013 4 13 55 7211/17/2013 4 15 60 7911/18/2013 4 15 63 8211/19/2013 5 15 68 8811/20/2013 5 15 80 10011/21/2013 5 16 83 10411/22/2013 5 20 85 11011/23/2013 5 20 85 110


11/24/2013 5 20 86 11111/25/2013 5 20 86 11111/26/2013 5 20 88 11311/27/2013 5 20 90 11511/28/2013 5 20 90 11511/29/2013 5 20 90 11511/30/2013 5 20 90 115

In addition to vehicle information, we also explore data about the medical teams from WHO Health Cluster Reports. The PDF maps show the total num-ber of foreign medical teams divided by city and ori-gin of the medical team, starting November 15, 2013.

Table 7 shows the number of foreign medical teams and their operational status. In this context, opera-tional teams refer to medical teams that are actually seeing patients. Similar to infrastructure informa-tion, updates on the medical teams are very scarce.

Table 7. Medical Team Information Update after Typhoon Haiyan between 11/15/2013 and 11/30/2013 from WHO Health Cluster Reports

Date Standby in country / without destination (out-of country)

At Destination (not registered)

Operational teams (not registered)

Left Deployment (not registered)

11/15/13 6(0) 12(0) 10(0) 011/17/13 1(4) 14(0) 9 (0) 011/20/13 5(3) 10(0) 22 (0) 011/22/13 1(6) 2(0) 42 (0) 011/26/13 2(6) 0(2) 41(10) 1(1)11/30/13 0(5) 0(2) 39(11) 6(3)

4.3 Lessons Learned: Challenges Faced During the Col-lection Process and Potential Solutions

This case study enables us to better understand the current situation of real-time data, how data evolves, and to what extent real-time data is available. We be-lieve that this valuable experience will inform and aid modelers in building improved models. This sec-tion describes the challenges faced during our study, and recommendations for how these challenges can be addressed in the future (when possible) from an academic humanitarian logistics perspective.

Time-sensitive information

As information evolves after a disaster, some sourc-es that commonly recur during separate disaster relief efforts do not retain their data for long peri-ods of time. This generally depends on the estab-lishment type and update type of the information outlet. Investigating retention time and update type

of information outlets before the onset of a disaster can aid in gathering time-sensitive information. For instance, collection and analysis of data from re-sources that have the tendency to retain their files longer can be postponed to later times depending on the ultimate goals of the data. If a researcher focuses on modeling the initial few days of the disaster, this approach might not work. However, if a researcher wants to model a later period, postponing collection of retained files can be beneficial, thus avoiding the trade-off between collection of time-sensitive infor-mation and the prioritization of analysis. Addition-ally, the format of the time-sensitive information provided by an outlet tends to be similar for each disaster. Familiarity with the data format can ease the data collection process. Moreover, some out-lets, such as OSM, benefit from specifically search-ing for a separate repository that might be linked to their wiki webpage. Accessing those repositories in the early days of the disaster response supports the smooth collection of time-sensitive data.


Delays

The data on collapsed and damaged buildings did not begin to appear in Palo and Tacloban City until November 14, 2013, and in Guiuan until November 19, 2013. Depending on the main objectives of the humanitarian agencies using the data, those dates might be too late. For example, from the perspective of search and rescue operations, receiving informa-tion three days after a disaster may seem too late to assist most of the people trapped under buildings. However, people who were stranded but not direct-ly impacted by the collapsed structures would likely still be alive during that particular time frame and could benefit from relief supplies.

Data explosion and information overload

With the emergence of technology and increasing number of humanitarian organizations, the amount of information available after a disaster is accelerat-ing From the perspective of a humanitarian logistics research team, it is challenging to account for this information load in a timely manner. One major fac-tor is a limited research workforce. The limited hu-man resource capacity for information processing is a shared challenge across the humanitarian ecosystem and acutely experienced by field practitioners. The evolution and success of many digital humanitarian efforts is the harnessing of remote workforces, often through crowdsourcing and microtasking efforts. Based on this case study and our team’s experience, a possible solution for this issue is to recruit additional help in the data collection process whenever possible. Exploring collaborative opportunities with research teams and potentially practitioners to build a feasible and appropriate workforce to identify, filter, assign and prioritize humanitarian logistics datasets for modeling purposes may be a long-term goal. In the short term, future efforts might consist of two groups working in tandem on the data retrieval: one search-ing for new sources and initializing their retrieval, while the other investigates whether to continue re-trieving data from the identified sources or not.

Duplication

Further complications arose when we observed that several data sources were reposting data from other sources; for example, the NDRRMC situation reports were placed on both ReliefWeb.int and the NDRRMC site, in the case of Typhoon Haiyan. Ad-ditionally, many updating files with recent time-

stamps appeared to be identical to older versions of the files. Recognizing the overlapping data segments between primary information outlets and aggrega-tor information outlets early is a key point for re-searchers. Furthermore, prior information about the expected update timeline and primary purpose of the information outlets can help resolve these issues. For example, if an information outlet is only focused on initial damage assessment, a researcher might stop downloading from this outlet a few weeks after a disaster to prevent any possible duplication.

Relevance

Some of the data retrieved was not as relevant to humanitarian logistics models as originally hoped. Identifying logistical content of an information outlet and prioritizing these outlets might be helpful in or-ganizing the data collection process in the future. For example, seeking out resources, such as OSM and the Logistics Cluster, earlier in future disasters would be valuable, since the coordinates used in OSM provide information about the damaged structures. Addition-ally, collaborating with and supporting these organi-zations before disasters strike would help researchers to understand data relevance more clearly.

Compatibility

Even assuming file compatibility, problems might exist between perceptions of the sources and how the sources are developed. For example, when one data source is developed by people on the ground, and another source is developed by digital humani-tarian mappers, conflicting information would have to have a system for prioritization. Such a system would also require the differentiation of information obtained from mappers versus on field personnel. Furthermore, the particular mapping techniques of various sources may differ. One information source may mark a singular section as damaged or impass-able, whereas another source might tag the entire street. This discrepancy can cause major differences in routing decisions and make it challenging to com-bine multiple resources to build a larger database.

Availability

Our data analysis shows that there is only informa-tion for 5% of the roads that indicate some sort of damage to the road structure. This low level of in-formation in the case of a high-impact disaster, with a high level of media coverage and a large amount


of data tracking efforts within the first few weeks of the typhoon, shows that the available information is not enough to integrate real-time data into models without putting efforts into adding accuracy mea-sures and finding missing data. On the other hand, 60% of the available building damage information also requires validity checks for source data.

The availability of data across regions change. Some locations receive more attention than others. In par-ticular with the building data, Palo, Tacloban City, and Guiuan appear to receive more mapping than Ormoc or Cebu City. While some of this can be at-tributed to proximity to the storm at the peak inten-sity, looking at displacements suggests that more people were affected in Cebu and Iloilo than Gui-uan, yet these regions were less mapped (Protection Cluster, 2013). Some of these variations in coverage may be due to the focus and collaboration of map-ping activities with the online communities such as OSM, and need to be further explored. We also observe that most of the damage indicated by the data was for roads along the coast. A possible ex-planation of this phenomena might be the presence of multiple medical teams close to the coast (World Health Organization, 2013), as well as UN On-Site Operations Coordination Centers having predomi-nant locations along the coast (MapAction, 2013a). In addition, digital humanitarians assisting with up-dating maps might more easily distinguish damage to a larger coastal road than to more crowded neigh-borhood streets.

We recognize that numerous pieces of data from ag-gregator outlets have citations to their primary in-formation sources. However, availability of the un-derlying raw data is frequently limited for a number of reasons. In addition, multiple primary informa-tion outlets share PDF maps, yet the detailed infor-mation about the infrastructure damage is challeng-ing to obtain since the original core datasets from the primary source are infrequently cited or made available. This results in information loss.

Technological Status of Disaster-Affected Regions

The pre- and post-disaster states of the communica-tion system play a significant role in the opportuni-ties, limitations and gaps of the available data. No matter how technologically advanced a particular geographic area may be, gaps in telecommunication coverage in the post-disaster setting are often pres-ent. Significant communication problems arose due

to the destruction of power and communication lines in the Philippines soon after Typhoon Haiyan (Pala-tino, 2013). Over a month after the onset, connecting with field teams within specific regions on a daily basis presented significant challenges, as exempli-fied by “a survey undertaken in the affected com-munity in Guiuan, which reconfirmed the need for clearer and more frequent communication between aid partners and affected communities” (OCHA, 2014). Recognizing the damage sustained by re-gional communication systems can help researchers understand the information flow and better explain missing data (completeness) for specific geographic regions and time periods. Absence of information flow from an area can also serve as a signal of sig-nificant damage and imply increased needs for hu-manitarian relief (i.e., demand).

5. CONCLUSION

This study introduces a framework for real-time humanitarian logistics data focused on use in math-ematical models. We define a set of measures to assess the quality of the data and their applicabil-ity to different disasters. Additionally, we provide modeling implications of data based on the pro-posed framework and discuss how to measure the attributes listed in the framework. We then apply this framework to the data collected from Typhoon Haiyan and present an example of data sources clas-sification based on proposed measures. We also pro-vide an analysis of the data focused on the logistical content to inform modelers about the availability of logistical data, at least in the case of Typhoon Hai-yan. The study describes how our humanitarian lo-gistics team approached the emergence of data after the disaster and the challenges faced during the col-lection process, as well as our observations.

The study shows that, even with accumulating in-formation from different resources, real-time logisti-cal information is very scarce. The analysis demon-strates that only 5% of the infrastructure in Tacloban City has damage information. The number would be much less for other cities that did not receive as much attention as Tacloban City. We encourage researchers to design appropriate models that con-sider this issue. The framework and its application illustrate what data is available to the team, when data is available, and how data changes after the di-saster. It also provides direction about which data sources to search for a particular purpose after a di-saster which would be beneficial in future disasters.


The information and observations included in this study are based only on one disaster, Typhoon Hai-yan. Future experiences might differ based on mul-tiple factors, such as the disaster type (e.g., complex emergency, man-made disaster), ICT environment, and involvement of organizations and affected populations. The information outlets described and analyzed in this work constitute only a subset of the available resources and focus on those with an Eng-lish content and online availability. The description of organizations and digital humanitarian groups involved in information management and data shar-ing is based upon a growing knowledge of our re-search team and one that is a work in progress. Fur-thermore, the classification provided in this paper is only one of the many possible ways, where other re-searchers might approach the same data differently. The development of parameters to measure the at-tributes of the framework is in its early stages. More work needs to be done to improve the measurement structure and customize it for specific purposes.

To the best of our knowledge, this is the first study conducted by humanitarian logistics researchers fo-cusing on the real-time data collection process in a post-disaster setting. It also presents a unique team approach that combines the expertise of both hu-manitarian logistics researchers and a researcher with humanitarian practitioner experience. The data retrieval and aggregation process described in this paper would not have been possible to carry out in a timely fashion without the pre-existing relationships between researchers and humanitarian practitioners. Through comprehensive mathematical models built specifically for the emerging data sources, researchers can identify the most valuable and promising data for the purpose of more efficient humanitarian logistics operations, and ways to integrate this data into a de-cision-making process. Ideally, validated humanitar-ian logistic models developed based on near real-time data shared by humanitarian agencies should under-go a series of iterative processes with practitioners to translate logistic models into relevant tools for field logisticians and agencies to assist in their operational activities. The study enlightens researchers about the availability of real-time data and its challenges. Addi-tionally, it provides a ground work for the integration of real-time data into logistical models.

6. ACKNOWLEDGEMENTS

This work has been in part funded by the National Science Foundation, Grant CMMI-1265786: “Ad-

vancing Dynamic Relief Response: Integration of New Data Streams and Routing Models” and ac-companying REU supplement, CMMI-1265786/001.

7. REFERENCES

All Partners Access Network. (2013). Typhoon Haiyan response 2013. Retrieved from https://community.apan.org/typhoon-haiyan/p/map.aspx

All Partners Access Network. (2014). Typhoon Haiyan commu-nity. Retrieved from https://community.apan.org/typhoon-haiyan/b/updates/archive/2013/11/09/typhoon-haiyan-com-munity.aspx

Altay, N., & Labonte, M. (2014). Challenges in humanitarian in-formation management and exchange: Evidence from Haiti. Disasters, 38(s1), S50–S72.

Arvis, J. F., Saslavsky, D., Ojala, L., Shepherd, B., & Busch, C. (2014). Connecting to compete 2014, trade logistics in the global economy: The logistics performance index and its in-dicators. Retrieved from http://lpi.worldbank.org/

Ashktorab, Z., Brown, C., Nandi, M., & Culotta, A. (2014). Tweedr: Mining twitter to inform disaster response. Proceed-ings of ISCRAM 2014, Pennsylvania, USA.

Bharosa, N., Appelman, J. A., Zanten, B. Van, & Zuurmond, A. (2009, May 10-13). Identifying and confirming information and system quality requirements for multi-agency disaster management. Proceedings of the 6th International Confer-ence on Information Systems for Crisis Response and Man-agement, Gothenborg, Sweden, ISCRAM.

Bogazici University, Earthquake Eng. Dept. (2002). Earthquake risk analysis of Istanbul metropolitan area (in Turkish). Bo-gazici Universitesi Kandilli Rasathanesi ve Deprem Arastir-ma Enstitusu, Istanbul, Turkey.

Butler, D. (2013). Crowdsourcing goes mainstream in typhoon response. Nature, 20. Retrieved from http://www.nature.com/index.html

Camp Coordination and Camp Management Cluster. (2014). Typhoon Haiyan (Yolanda) consolidated cluster briefs, 10 March 2014. Retrieved from http://reliefweb.int/report/philippines/typhoon-haiyan-yolanda-consolidated-clus-ter-br

Copernicus Emergency Management System. (2014a). EMSR058: Typhoon in Philippines. Retrieved from http://emergency.copernicus.eu/mapping/list-of-components/EMSR058

Copernicus Emergency Management System. (2014b). What is Copernicus. Retrieved from http://emergency.copernicus.eu/mapping/ems/what-copernicus

Cordeiro, K. de F., Campos, M. L. M., & Borges, M. R. da S. (2014). Adaptive integration of information supporting deci-sion making: A case on humanitarian logistic. Proceedings of ISCRAM 2014., Pennsylvania, USA.

Crisis Mappers. (2014). Home. Retrieved from http://crisismap-pers.net/


Day, J. M., Junglas, I., & Silva, L. (2009). Information flow im-pediments in disaster relief supply chains. Journal of the As-sociation for Information Systems, 10(8), 637-660.

DeLone, W. H., & McLean, E. R. (1992). Information systems suc-cess: The quest for the dependent variable. Information Sys-tems Research, 3(1), 60-95.

DeLone, W. H., & McLean, E. R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30.

Department of Social Welfare and Development. (2014). Reports and updates. Retrieved from http://disaster.dswd.gov.ph/reports-and-updates/

Digital Humanitarian Network (2014). DHNetwork about. Re-trieved from http://digitalhumanitarians.com/about

Digital Humanitarian Network. (2014a). Esri disaster response program. Retrieved from http://digitalhumanitarians.com/content/esri-disaster-response-program

Digital Humanitarian Network. (2014b). Super Typhoon Haiyan. Retrieved from http://digitalhumanitarians.com/content/super-typhoon-yolanda

Dorasamy, M., Ramen, M., & Kaliannan, M. (2013). Knowledge management systems in support of disasters management: A two decade review. Technological Forecasting and Social Change, 80(9), 1834-1853.

Endsley, M. R. (1988, October). Design and evaluation for situa-tion awareness enhancement. Proceedings of the human fac-tors and ergonomics society annual meeting (Vol. 32, No. 2, pp. 97-101). SAGE Publications Santa Monica, CA

Ergun, O., Karakus, G., Keskinocak, P., Swann, J., & Villarreal, M. (2010). Operations research to improve disaster supply chain management. Wiley Encyclopedia of Operations Research and Management Science, John Wiley & Sons, Hoboken, NJ.

Ergun, Ö., Stamm, J. L. H., Keskinocak, P., & Swann, J. L. (2010). Waffle House Restaurants hurricane response: A case study. International Journal of Production Econom-ics, 126(1), 111-120.

Environmental Systems Research Institute. (2014a). Cyclone Hai-yan impact (MapServer). Retrieved from http://fema-dev.esri.com/arcgis/rest/services/DisasterResponse/Cyclone_Haiyan_Impact/MapServer

Environmental Systems Research Institute. (2014b). Typhoon Haiyan/Yolanda maps. Retrieved from http://www.esri.com/services/disaster-response

Federal Emergency Management Agency. (2010). The Federal Emergency Management Agency. Retrieved from https://www.fema.gov/pdf/about/pub1.pdf

Federal Emergency Management Agency. (2014a). Data feeds. Retrieved from: http://www.fema.gov/data-feeds on 12 Janu-ary 2014.

Federal Emergency Management Agency. (2014b). FEMA’s In-ternational Programs & Activities. Retrieved from http://www.fema.gov/femas-international-programs-activities

Galton, A., & Worboys, M. (2011, May). An ontology of informa-tion for emergency management. Proceedings of 8th Inter-national Conference on Information Systems for Crisis Re-sponse and Management, Lisbon, Portugal.

Geodata Converter. (2014). Vector. Retrieved from http://con-verter.mygeodata.eu/vector

Google Crisis Maps. (2014). Typhoon Yolanda relief map. Re-trieved from http://google.org/crisismap/2013-yolanda

Google Crisis Response. (2014). Tools for responders. Retrieved from https://www.google.org/crisisresponse/

Government of the Philippines. (2014). NDRRMC updates re effects of TY YOLANDA (HAIYAN) (as of 17 Apr 2014). National disaster risk reduction and management coun-cil. Retrieved from http://reliefweb.int/report/philippines/ndrrmc-updates-re-effects-ty-yolanda-haiyan-17-apr-2014

Grünewald, F., & Binder, A. (2010) Inter-agency real time eval-uation in Haiti: 3 Months after the earthquake. Plaisians, France: Groupe Urgence Réhabilitation Développement and Global Public Policy Institute.

Haavisto, I. (2014). Performance in humanitarian supply chains. Ekonomi och Samhälle/Economics and Society, No. 275, Hanken School of Economics, Helsinki, Finland.

Haggarty, A., & Naidoo, S. (2008). Global symposium+5 final re-port, information for humanitarian action. UNOCHA Global Symposium Secretariat. Palais des Nations 1211, Geneva, Switzerland.

Hester, V., Shaw, A., & Biewald, L. (2010, December). Scalable crisis relief: Crowdsourced SMS translation and categoriza-tion with Mission 4636. Proceedings of the first ACM Sympo-sium on Computing for Development (p. 15), ACM, London, United Kingdom.

Howden, M. (2009), How humanitarian logistics information systems can improve humanitarian supply chains: A view from the field. Presented at 6th International ISCRAM Con-ference, Gothenburg, Sweden.

Huang, A., Ma, A., Schmidt, S., Xu, N., Zhang, B., Meineke, L., Shi, Z. E., Chan, J. & Dolinskaya, I. (2013). Integration of real time data in urban search and rescue center for the commer-cialization of innovative transportation technology. Trans-portation Center, Northwestern University. Retrieved from http://www.ccitt.northwestern.edu/documents/Integration_of_real_time_data_in_urban _search_ and_rescue.pdf

Humanitarian Response. (2014a). About Humanitarian Re-sponse. Retrieved from https://philippines.humanitarianre-sponse.info/about

Humanitarian Response. (2014b). Humanitarian Response COD-FOD Registry Philippines. Retrieved from https://cod.humanitarianresponse.info/search/field_country_region/164?search_api_vi ews_fulltext=

Humanitarian Response. (2014c). Humanitarian Response Phil-ippines. Retrieved from https://philippines.humanitarianre-sponse.info/search/

Imran, M., Elbassuoni, S. M., Castillo, C., Diaz, F., & Meier, P. (2013). Extracting information nuggets from disaster-related


messages in social media. Proceedings of ISCRAM 2013, Baden-Baden, Germany.

Inter-Agency Standing Committee. (2010a). IASC guidelines Common Operational Datasets (CODs) in disaster pre-paredness and response. Retrieved from https://www.humanitarianresponse.info/system/files/documents/files/iasc_guidelines_on_common_operational_datasets_in_di-saster_preparedness_and_response_2010-11-01.pdf

Inter-Agency Standing Committee. (2010b). IASC strategy, meet-ing humanitarian challenges in urban areas. Retrieved from https://interagencystandingcommittee.org/meeting-humani-tarian-challenges-urban-areas/documents-public/iasc-strate-gy-meeting-humanitarian

Inter-Agency Standing Committee. (2012). Reference module for cluster coordination at the country level. Retrieved from https://www.humanitarianresponse.info/system/files/documents/files/iasc-coordination-reference%20module-en_0.pdf.

International Federation of Red Cross and Red Crescent Soci-eties. (2013). World disasters report. Retrieved from http://www.ifrc.org/en/publications-and-reports/world-disasters-report/world-disasters-report-2013/

Jahre, M., & Jensen, L. M. (2010). Coordination in humanitarian logistics through clusters. International Journal of Physical Distribution & Logistics Management, 40(8/9), 657-674.

Kovács, G., & Spens, K. M. (2007). Humanitarian logistics in di-saster relief operations. International Journal of Physical Dis-tribution & Logistics Management, 37(2), 99-114.

L’Hermitte, C., Bowles, M., & Tatham, P. (2013). A new classifica-tion model of disasters based on their logistics implications. 11th ANZAM Operations, Supply Chain and Services Man-agement Symposium (pp. 1-19), Brisbane, QLD, Australia.

Li, J., Li, Q., Liu, C., Khan, S. U., & Ghani, N. (2014). Community-based collaborative information system for emergency man-agement. Computers & Operations Research, 42, 116-124.

Limbu, M., Wang, D., Kauppinen, T., & Ortmann, J. (2012). Man-agement of a crisis (MOAC) vocabulary specification. Re-trieved from http://observedchange.com/moac/ns

Liu, N., & Ye, Y. (2014). Humanitarian logistics planning for natural disaster response with Bayesian information up-dates. Journal of Industrial and Management Optimiza-tion, 10(3), 665-689.

Logistics Cluster. About the Logistics Cluster. Retrieved from http://www.logcluster.org/logistics-cluster

Logistics Information about In-Kind. (2014a). About LogIK. Re-trieved from http://logik.unocha.org/SitePages/about.aspx

Logistics Information about In-Kind. (2014b). Logistics Infor-mation about In-Kind relief detailed report. Retrieved from http://logik.unocha.org/SitePages/DetailedReportAdmin.aspx?soid=2,3

De la Torre, L.E., Dolinskaya, I. S., & Smilowitz, K. R. (2012). Di-saster relief routing: Integrating research and practice. Socio-economic Planning Sciences, 46(1), 88-97.

Manso, M., & Manso, B. (2012). The role of social media in cri-sis: A European holistic approach to the adoption of online and mobile communications in crisis response and search and rescue efforts. Proceedings of the 17th International Command & Control Research & Technology Symposium, Virgina, USA. Retrieved from http://www.dodccrp.org/events/17th_iccrts_2012/papers/007.pdf

MapAction. (2013a). MA029 Philippines Typhoon Haiyan (Yolanda) 3W Overview (21-Nov-2013). Retrieved from http://www.humanitarianresponse.info/operations/phil-ippines/infographic/ma029-philippines-typhoon-haiyan-yolanda-3w-oveview-21-nov-2013

MapAction. (2013b). Typhoon Haiyan update. Retrieved from http://www.mapaction.org/more-news/385-typhoon-hai-yan-update.html

MapAction. (2014). MapAction home page. Retrieved from http://www.mapaction.org/

Munro, R. (2013). Crowdsourcing and the crisis-affected com-munity. Info Retrieval, 16(2), 210-266.

National Disaster Risk Reduction and Management Council. (2014). NDRRMC home page. Retrieved from http://www.ndrrmc.gov.ph

Open Street Map. Humanitarian OSM Team. Retrieved from http://wiki.openstreetmap.org/wiki/Humanitarian_ OSM_Team

Open Street Map. (2014a). Index of Haiyan. Retrieved from http://labs.geofabrik.de/haiyan/

Open Street Map. (2014b). Some editing stats from the Typhoon Haiyan response. Retrieved from http://hot.openstreetmap.org/updates/2014-01-14-some-editing-stats-from-the-ty-phoon-haiyan-response

Open Street Map (2014c). Typhoon Haiyan. Retrieved from http://wiki.osm.org/wiki/Typhoon_Haiyan

Ortmann, J., Limbu, M., Wang, D., & Kauppinen, T. (2011, Octo-ber). Crowdsourcing linked open data for disaster manage-ment. Proceedings of the Terra Cognita Workshop on Founda-tions, Technologies and Applications of the Geospatial Web in conjunction with the ISWC (pp. 11-22), Bonn, Germany.

Ortuño, M. T., Cristóbal, P., Ferrer, J. M., Martín-Campo, F. J., Muñoz, S., Tirado, G., & Vitoriano, B. (2013). Decision aid models and systems for humanitarian logistics. A survey. In: Vitoriano B, Montero J, Ruan D (eds) Decision aid models for disaster management and emergencies (pp. 17-44). Atlantis Press, Springer Business + Science Media, New York, NY

Özdamar, L., & Ertem, M. A. (2015). Models, solutions and en-abling technologies in humanitarian logistics. European Journal of Operational Research, 244(1), 55-65.

Palatino, M. (2013, November 13). Lessons from the Haiyan Ty-phoon Tragedy. The Diplomat. Retrieved from http://thediplo-mat.com/2013/11/lessons-from-the-haiyan-typhoon-tragedy/

Palen, L., Anderson, K. M., Mark, G., Martin, J., Sicker, D., Palm-er, M., & Grunwald, D. (2010). A vision for technology-medi-ated support for public participation and assistance in mass emergencies and disasters. Proceedings of ACM-BCS Visions of Computer Science 2010, Swinton, UK.


In: Proceedings of the 2010 ACM-BCS Visions of Computer Sci-ence Conference (Edinburgh, United Kingdom, April 14 – 16, 2010). ACM-BCS Visions of Computer Science, British Com-puter Society, Swinton, UK, 1-12.

Petter, S., DeLone, W., & McLean, E. (2008). Measuring informa-tion systems success: Models, dimensions, measures, and interrelationships. European Journal of Information Sys-tems, 17(3), 236-263.

Presidential Management Staff, Presidential Communications Development & Strategic Planning Office. (2014, April 24). Infographic: Timeline of government actions in response to Typhoon Haiyan. Official Gazette. Retrieved from http://www.gov.ph/2014/04/24/infographic-timeline-of-govern-ment-actions-in-response-to-typhoon-haiyan/

Protection Cluster (2013). Protection Cluster displacement and 3W map. Retrieved from http://www.humanitarianresponse.info/operatio ns/philippines/infographic/protection-cluster-displacement-and-3w-map

Purohit, H., Castillo, C., Diaz, F., Sheth, A., & Meier, P. (2013). Emergency-relief coordination on social media: Automatically matching resource requests and offers. First Monday, 19(1).

Red Cross. (2014). Typhoon Haiyan mapfolio. Retrieved from http://americanredcross.github.io/haiyan_mapfolio/

ReliefWeb. (2014). ReliefWeb about page. Retrieved from http://reliefweb.int/about

Sangiamkul, E., & Hillegersberg, J. van. (2011, May 8-11). Re-search directions in information systems for humanitarian logistics. 8th International Conference on Information Sys-tems for Crisis Response and Management, ISCRAM, Lis-bon, Portugal.

Sarcevic, A., Palen, L., White, J., Starbird, K., Bagdouri, M., & An-derson, K. (2012, February). Beacons of hope in decentralized coordination: Learning from on-the-ground medical twitter-ers during the 2010 Haiti earthquake. Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work (pp. 47-56), ACM, New York, USA.

Scott, N., & Batchelor, S. (2013). Real time monitoring in disas-ters. IDS Bulletin, 44(2), 122-134.

Sheu, J. B. (2010). Dynamic relief-demand management for emer-gency logistics operations under large-scale disasters. Trans-portation Research Part E: Logistics and Transportation Re-view, 46(1), 1-17.

Tatham, P., L’Hermitte, C., Spens, K., & Kovács, G. (2013). Hu-manitarian logistics: Development of an improved disaster classification framework. The 11th ANZAM Operations, Supply Chain and Services Management Symposium (pp. 1-10), Brisbane, QLD, Australia..

Tatham, P., & Spens, K. (2011). Towards a humanitarian logistics knowledge management system. Disaster Prevention and Management, 20(1), 6-26.

Teran, J. (2014). Measuring the quality of humanitarian data: An emerging framework. Retrieved from http://docs.hdx.rwlabs.org/measuring-the-quality-of-humanitarian-data-an-emerging-framework/#comments

The National Oceanic and Atmospheric Administration. (2013, November 21). NWS Director Uccellini speaks on CNN about Typhoon Haiyan and climate change. Retrieved from http://www.nws.noaa.gov/com/weatherreadynation/news/131121_louis.html

United Nations Foundation. (2011). Disaster relief 2.0: The fu-ture of information sharing in humanitarian emergency. Retrieved from http://www.unfoundation.org/what-we-do/legacy-of-impact/technology/disaster-report.html

United Nations Institute for Training and Research. (2013). UN-OSAT. Retrieved from http://www.unitar.org/unosat/ United Nations Office for Coordination of Humanitarian Affairs. (2002). Symposium on best practices in humanitarian infor-mation exchange. Palais des Nations, Geneva, Switzerland.

United Nations Office of Coordination of Humanitarian Affairs. (2013a). Developing humanitarian data standards: An intro-duction and plan for 2014. Retrieved from http://docs.hdx.rwlabs.org/wp-content/uploads/HXL_Paper-forsite.pdf

United Nations Office for the Coordination of Humanitarian Affairs. (2013b). Governmental contact list: Region VI (Western Visayas). Retrieved from https://philippines.humanitarianresponse.info/document/government-contact-list-region-vi-western-visayas

United Nations Office of Coordination of Humanitarian Affairs. (2013c). Humanitarian data exchange. Retrievde from http://docs.hdx.rwlabs.org/wp-content/uploads/HDX-Project-Doc-ument-abridged.pdf

United Nations Office of Coordination of Humanitarian Affairs. (2013d, November 8). Philippines: Super Typhoon Haiyan makes landfall, UN and humanitarian community on high-alert. Retrieved from http://www.unocha.org/roap/top-sto-ries/philippines-super-typhoon-haiyan-makes-landfall-un-and-humanitarian-community-high-alerton

United Nations Office of Coordination of Humanitarian Affairs. (2014b). HDX quality assurance framework. Retrieved from http://docs.hdx.rwlabs.org/wp-content/uploads/HDX_Qual-ity_Assurance_Framework_ Draft.pdf

United Nations Office of Coordination of Humanitarian Affairs. (2014c). Information management. Retrieved from http://www.unocha.org/what-we-do/information-management/overview

United Nations Office of Coordination of Humanitarian Af-fairs. (2014d). What is UNDAC? Retrieved from http://www.unocha.org/what-we-do/coordination-tools/undac/overview

United Nations Office of Coordination of Humanitarian Affairs. (2014e). Who we are. Retrieved from http://www.unocha.org/about-us/who-we-are

Vaillancourt, A. (2013). Government decentralization and disas-ter impact, an exploratory study. Retrieved from http://www.buildresilience.org/2013/proceedings/files/papers/352.pdf

Velev, D., & Zlateva, P (2012). Use of social media in natural di-saster management. International Proceedings of Economic Development and Research, 39, 41-45.

Volontaires Internationaux en Soutien aux Opérations Virtuelles. (2014a). About us. Retrieved from https://haiyan.crowdmap.com/page/index/1


Volontaires Internationaux en Soutien aux Opérations Virtu-elles. (2014c). Retrieved from http://visov.org

World Food Programme. (2014a). About. Retrieved from http://www.wfp.org/about

World Food Programme. (2014b). Food aid information system. Retrieved from http://www.wfp.org/fais/

World Food Programme Office of Evaluation, United Nations Children’s Fund Evaluation Office, & the Ministry of For-eign Affairs, Policy and Operations Evaluation Department, Netherlands. (2012). Joint evaluation of the global logistics cluster. Retrieved from http://documents.wfp.org/stellent/groups/public/documents/reports/wfp251775.pdf

World Health Organization. (2013). Foreign medical teams in Tacloban City. Retrieved from http://www.humanitarian-response.info/operatio ns/philippines/infographic/foreign-medical-teams-tacloban-city

Yi, W., & Özdamar, L. (2007). A dynamic logistics coordination model for evacuation and support in disaster response ac-tivities. European Journal of Operational Research, 179(3), 1177-1193.

Appendix

A.1 Original Information Outlets

Logistics Cluster

Logistics Cluster, created by OCHA, aids the coop-eration of groups of humanitarian organizations. World Food Programme (WFP, 2014a) is the lead agency as appointed by the IASC (WFP, UNICEF & Ministry of Foreign Affairs, Netherlands, 2012). Common types of datasets are maps, meeting min-utes, and situation updates. Logistics Cluster main-tains maps that detail infrastructure data such as op-erations access constraints and general operations. The meeting minutes from the Coordination – Roads Transport – Sea and Rivers Transport Group include information about the road conditions. These almost daily meeting notes start from November 11, 2013. The situation updates also provide information about various types of transportation channel avail-ability including the overland transport. These up-dates start on November 14, 2013, with limited data about the road conditions. Most of Logistics Clus-ter’s files found during the case study comprise of portable data formats (PDF).

LogIK

LogIK, or Logistics Information about In-Kind Relief, is a global online database maintained by OCHA (LogIK, 2014a). LogIK provides detailed re-

ports in PDF or XLS format of supply operations, with three categories of data: relief items, transports and contributions. The database reflects reported international/regional humanitarian contributions of relief items. Within these categories, LogIK offers information such as supplier information, destina-tion, and quantity (Logik, 2014b). Specifically, the relief items section includes data about donated item types (e.g., blankets, tents and emergency kits), quantity, senders and other. The contributions sec-tion provides the decision date and dollar value of the contributions. The transport section contains information about vehicles provided from different organizations by air, road and sea. This information source may be of higher reliability because its source data originates from donors affiliated with the Unit-ed Nations Office (UN). The information in LogIK is updated daily.

HOT

The Humanitarian OpenStreetMap Team (HOT) is a volunteer-based community formed within the larger OpenStreetMap community that has emerged as a pivotal provider and platform for data in hu-manitarian operations by providing open source data The data placed into OSM by volunteers con-tinues to increase its scope and accuracy with ris-ing numbers of users verifying information in more locations. OSM furthermore contributes to the large growth in information in post-disaster operations by frequently updating geographic data, sometimes ev-ery minute (OSM, “Typhoon Haiyan”). OpenStreet-Map globe data takes several gigabytes, so specific repositories exist for disasters such as Haiyan (OSM, 2014a). While the “history” feature of the OSM helps to see the previous actions, space limitations tend to prompt these repositories to update at longer inter-vals and not retain many previous updates. The ini-tial OSM updates for Typhoon Haiyan date back to November 7, 2013 since the HOT team was called by OCHA to start mapping the region a day before the typhoon touchdown The basic street maps of the cities of Port-Au-Prince and Carrefour provided by OpenStreetMap (2014b) in about 48 hours following the previous crises were claimed to be the best avail-able maps , “Some editing stats”.

MapAction

MapAction is a non-governmental organization that produces maps for the humanitarian crisis. From November 13, 2013 to January 17, 2014, they pro-


vided maps in JPEG and PDF formats of affected areas, along with information on the populations, road conditions, coordination (cluster activities by location), shelters and others. The main type of MapAction maps seem to be “Who, What, Where” maps that exhibit the locations of organizations. Ma-pAction compiles data from several sources such as OCHA and NDRRMC for different cities, and each map is accompanied by a summary. A lag appears to exist between the report date and the update time; however, MapAction directs its users to Humani-tarian Response (2014a) Philippines portal for pri-mary MapAction maps (MapAction, 2014). Similar to OSM, MapAction was also present in the Philip-pines before the typhoon hit (MapAction, 2013b).

Copernicus EMS

CEMS (Copernicus Emergency Management Sys-tem, 2014b), maintained by the European Com-mission, “monitors and forecasts the state of the environment on land, sea and in the atmosphere, in order to support climate change mitigation and adaptation strategies, the efficient management of emergency situations and the improvement of the security of every citizen” The website appears to present these maps, which seem to run between November 9, 2013 to November 18, 2013, in numer-ous formats and resolutions for over a year after the disaster (CEMS, 2014a). Moreover, while CEMS is claimed to have published some of the best pre- and post-event analysis images in the first 36 hours of the Haiyan’s landfall , the website appears to make only a few updates publicly once the initial assess-ment occurs.

ESRI

Environmental Systems Research Institute (ESRI) holds data from the US Government on infrastruc-tural damage. The ESRI Disaster Response Program supports organizations responding to disaster. They provide “software, data coordination, techni-cal support, and other GIS assistance to organiza-tions” (DHN, 2014a). In this case study, similar to CEMS, these files appear to consist of initial damage assessments. They supported the Typhoon Haiyan response by providing an ESRI platform for pub-licly licensed imagery after the event, and have sup-ported other disasters (ESRI, 2014a). ESRI was one of the organizations that responded to the OCHA call for volunteers as part of the DHN. After Typhoon Haiyan, ESRI collaborated with the digital volun-

teer mapping groups such as Standby Taskforce and GISCorps to process social media reports and provide interactive maps (ESRI, 2014b). The website also provides maps from other groups such as Ma-pAction and OSM. The Haiyan maps start from No-vember 8, 2013 and were last updated on November 25, 2013 (as of March 2014).

UNITAR - UNOSAT

United Nations Institute for Training and Research’s (UNITAR) Operational Satellite Applications Pro-gramme (UNOSAT) is a satellite program that pro-vides “solutions to relief and development organiza-tions within and outside the UN system to help make a difference in critical areas such as humanitarian relief” (UNITAR, 2013). The satellite images appear to allow digital mapping volunteers to contribute to changing sources, such as OSM, and often remain available for several years. Daily maps illustrating brief overviews of satellite-detected areas of de-stroyed and possibly damaged structures of different areas of Philippines are provided from November 11, 2013 to November 20, 2013. While the first few are presented only in PDF format, the rest are also of-fered in Shapefile and ESRI’s geodatabase format.

VISOV

The goal of Volontaires Internationaux en Soutien aux Opérations Virtuelles (2014a) or International Volunteers in Support of Virtual Operations (VISOV) “is to help coordinate disaster responses with those of emergency organizations (formal or humanitar-ian) via digital spaces on which they organize and communicate” (VISOV, 2014c). VISOV appears to openly share and maintain its datasets on the web-site, possibly due to its intention to “become a tool in the hands of local communities” These datasets contain relevant tweets and map tags to estimate the road damage and relief progression VISOV datasets in particular include information such as the type of damage, description of the damage, geographical lo-cation, and time of notification. The data is available in the comma separated value (CSV) and keyhole markup language (KML) format from November 11, 2013 to December 3, 2013.

NDRRMC

NDRRMC, a governmental agency of the Philip-pines, develops detailed situation reports used by many mapping efforts and other situational reports


(NDRRMC, 2014). These PDF reports include infor-mation about situation overviews, causalities, af-fected populations, damaged houses, status of roads and bridges, standees, prepositioned and deployed assets/resources, cost of assistance, cost of damages, status of lifelines (both power and network out-age), and emergency management. The status of the roads and bridges demonstrates the damaged areas, declares if the roads are passable and adds remarks such as closing reasons or efforts made to make the roads passable. The level of detail includes even missing persons’ names, as well as the coordination efforts (involvement of different governmental and international agencies and humanitarian groups). These reports were initiated immediately after Hai-yan on November 8, 2013. NDRRMC retains the sit-uational reports during the recovery operations and appears to archive a large number of files.

DSWD

The Department of Social Welfare and Development (DSWD) appears to play a similar role to NDRRMC. However, it seems to focus on breaking down the in-formation on citizens by geographic regions, as well as statuses within each region such as the number of families in each evacuation center (DSWD, 2014). For the Typhoon Haiyan, DSWD frequently publishes ef-fect, service and intervention reports from November 8, 2013 to December 12, 2013. Viewing previous di-sasters suggests that DSWD also retains its reports for several months after the onset of the disaster.

A.2 Information Aggregation Outlets

Humanitarian Response

Humanitarian Response, maintained by OCHA, “aims to be the central website for Information Man-agement tools and services” It appears to compile files from OCHA sectors (Logistics Cluster, Edu-cation Cluster, Protection Cluster, etc.), and other groups such as the Canadian Red Cross, Logistics Cluster, MapAction and OSM. The Humanitarian Response website possesses a large number of files, retaining information from several past operations. The outlet provides numerous file filters such as content and data source, and within each filter, mul-tiple items may be selected. Humanitarian Response also maintains a registry of common operational datasets and fundamental operational datasets that often contains files with numerical data, which it claims “should represent the best available datasets

for each theme” (Humanitarian Response, 2014b). The relevant data starts from as early as the moment Typhoon Haiyan hit, and new information is still be-ing uploaded months after the event.

ReliefWeb

As with Humanitarian Response, OCHA maintains the ReliefWeb website. ReliefWeb appears to differ from Humanitarian Response in that it provides files, from situation reports to maps, from a broad range of sources and topics, not focusing on infor-mation management to the extent that Humanitar-ian Response does. ReliefWeb may be effective for identifying primary sources, since it “collects, up-dates and analyzes from more than 4,000 global in-formation sources” (ReliefWeb, 2014). Alternatively, ReliefWeb may help narrow which sources’ files do not need to be captured right away since it appears to contain most of the files from each source and re-tains them long after relief operations. The OCHA-sourced information about Typhoon Haiyan is di-rectly linked to the ReliefWeb website on the OCHA website. The ReliefWeb page for Typhoon Haiyan was activated on November 8, 2013 and different updates are still being uploaded, as of March 2014.

APAN

All Partners Access Network (APAN) functions sim-ilarly to ReliefWeb and Humanitarian Response, but differs mainly in that users upload the files them-selves and that the specific page for Typhoon Hai-yan is reactionary (APAN, 2013). User uploaded files allow for the identification of reactionary sources that may be overlooked in the expansive collection of ReliefWeb and Humanitarian Response sources. However, user uploading tends to lack consistency in uploading files from any given primary source, so using APAN as a data retrieval site may be problem-atic. In contrast, users may sometimes upload files not on a given website but derived from nonpublic datasets, e.g., insurance industry datasets. APAN amalgamates maps, briefs, reports from a variety of different organizations, agencies and groups from November 10, 2013 to January 7, 2014. The commu-nity for Typhoon Haiyan provides a link to an ESRI map (APAN, 2014).

Red Crescent Societies (BRC, ARC)

The Red Crescent Societies do not appear to put out files as an overarching system of organizations; rath-


er some individual Red Cross societies may choose to do so on their own. In this case, a collaboration between the American Red Cross and British Red Cross (Red Cross, 2014) makes available numerous maps throughout the disaster recovery efforts us-ing various data sources. Since the map files spec-ify what data sources each map employs, they may be used to locate the primary sources that contain the desired raw data. Moreover, the maps seem to specify the exact file, e.g., report number, from the source, allowing for direct retrieval of specific data. The Red Cross also provides reports about damage assessments, affected people, shelter, etc. They col-lect information from a variety of resources, such as OSM, UNITAR-UNOSAT, and ReliefWeb.

Google Crisis Maps

Google Crisis Maps, one of the tools of Google Crises Response Group crowdsources data not

only within its self-produced facility locations files, but also provides options to access files from sources such as Waze, a traffic mapping ap-plication, and CNES/Astrium, which provides satellite imagery (Google Crisis Maps, 2014). In particular, the self-produced map from Google Crisis Maps plays a role as infrastructure data. However, the facilities that Google Crisis Maps display appear to remain relatively constant at each map, so frequent downloads may not be necessary depending on the goals. The map shows damaged areas, their severity, evacuation centers, and relief drop zone areas. When color coding the damaged areas, the map shows the data as aggregated chunks. However, it is not al-ways clear if this means that the roads to those areas or the roads within that area are closed or not; and more detailed explanation about classi-fication of damages might be useful.


An Empirical Analysis of Humanitarian Warehouse Locations

Sander de Leeuw Professor at Vrije Universiteit Amsterdam,

Faculteit der Economische Wetenschappen en Bedrijfskunde – Amsterdam, The Netherlands [email protected]

Wing Yan Mok MSc at Vrije Universiteit Amsterdam,

Faculteit der Economische Wetenschappen en Bedrijfskunde – Amsterdam, The Netherlands [email protected]

ABSTRACT: The purpose of this paper is to empirically verify characteristics of current warehouse locations of humanitarian organizations (based on public information) and to relate those to the model developed by Richardson, de Leeuw and Dullaert (2016). This paper is based on desk research. Public data such as (annual) reports and databases are used to determine the features of the location in em-pirical terms. We find that a significant proportion of our sample co-locates their products at UNHRD premises. This suggests that organizations prefer to cluster their warehouse activities, particularly when there is no fee involved for using the warehouse (as is the case in the UNHRD network). The geo-graphic map of the current warehouses, together with the quantified location factors, provides an over-view of the current warehouse locations. We found that the characteristics of the current warehouse locations are aligned with literature on location selection factors. Current location can be characterized by infrastructure characteristics (in particular closeness to airport and safety concerns) and by the low occurrence of disasters. Other factors that were considered by us but were not supported by empirical evidence were labor quality and availability as well as the political environment. In our study we were only able to use a limited sample of warehouses. We also focused our research on countries where two or more organizations have their warehouses located. We did not account for warehouse sizes or the kinds of products stored in our analysis.

Keywords: Humanitarian supply chain management, facility location, warehouse location, empirical study, humanitarian logistics.


55

Leeuw, S., Mok, W. Y.: An Empirical Analysis of Humanitarian Warehouse LocationsISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 1 p 55 – 7656

1. INTRODUCTION

Humanitarian logistics involves issues of great com-plexity due to the physical and geographical envi-ronment of the places where disasters occur. . As a result, the affected areas are often hard to reach. To achieve efficient and effective humanitarian relief it is essential for humanitarian organizations to have their warehouses in appropriate locations. The loca-tions of these warehouses have a direct influence on the response time of the humanitarian organizations (Balcik & Beamon, 2008). When a disaster strikes, ba-sic items such as water and food need to be distrib-uted as fast as possible to cover initial needs. In ad-dition, hygiene kits and sanitary supplies as well as medication are important in the early response phase, because of the risk of the outbreak of various diseas-es (MSF, 2011). In order to fulfill these needs some humanitarian organizations locate their supplies in a place where they can serve a region, for example, per continent such as the IFRC [International Federation of Red Cross] (Gatignon, Van Wassenhove, & Charles, 2010). Another option is for humanitarian organiza-tions to place their inventory in the country they want to assist (Richardson & Leeuw, 2012).

Facility location models and the associated factors that are relevant in determining warehouse loca-tions form a topic of frequent discussion in the com-mercial domain, see e.g. Farahani, Bajgan, Fahimnia, and Kaviani (2015), Melo, Nickel, and Saldanha-da-Gama (2009) and MacCarthy and Atthirawong (2003). Many factors influence the selection of the lo-cation of a facility, though often the overriding con-cern is costs (MacCarthy and Atthirawong, 2003). The research carried out by MacCarthy and Atthi-rawong (2003) also showed that site selection fac-tors are industry-specific because each industry has different characteristics and strategies. For example, in the case of humanitarian organizations, the deliv-ery time can be expected to be important because people’s lives are at stake. If the supplies are strate-gically placed, the delivery time of the goods to the affected area can be reduced (Duran, Guiterrez, & Keskinocak, 2007; Balcik & Beamon, 2008). Empiri-cal research into location factors of humanitarian organizations is scant, with most of the research be-ing anecdotal in nature. The only structured attempt to organize factors that impact facility locations of humanitarian organisations has been undertaken by Richardson et al. (2016), although their work focuses on the input from users rather than an analysis of the current locations.

In this paper, we build on the findings of Richardson et al. (2016). We base our theoretical starting point on their analysis of factors deemed to be relevant for warehouse facility location in humanitarian or-ganizations. We aim to empirically determine the features of current warehouse locations of humani-tarian organizations (based on public information) and to relate these to the model developed by Rich-ardson et al. (2016).

The remainder of this paper is organized as follows: section two consists of a literature review and sec-tion three sets out the methodology for this research. The results will be described and analyzed in section four. Finally, section five discusses the results and describes the conclusion, limitations and future op-portunities.

2. LITERATURE REVIEW

2.1 Facility location in humanitarian supply chains

The purpose of emergency aid or disaster relief is to mitigate the effects of disasters and reduce the suffering of the affected people (Kelly, 1995). It is therefore important to rapidly provide appropriate emergency supplies to the people affected so that human suffering can be minimized (Balcik, Beamon, & Smilowitz, 2008). Designing an efficient and effec-tive humanitarian supply chain is a key challenge for humanitarian organizations. Humanitarian sup-ply chains differ from regular supply chains because they focus at minimizing loss of life and suffering, whereas commercial supply chains are mainly con-cerned with quality and profitability (Campbell, Vandenbussche, & Hermann, 2008). In fact, a hu-manitarian supply chain is one of the most dynamic supply chains in the world (Hoffman, 2005). Every disaster is different and it is hard to tell what the im-pact will be on an area or country. The management of these humanitarian supply chains is complicated because the amount of experienced logistics experts available is limited and coordination between the in-volved parties is often minimal (Nahleh, Kumar, & Daver, 2013).

Timely distribution may be complicated because the infrastructure in the affected area is often damaged or difficult to reach (Balcik et al., 2008). Furthermore, special care in transportation is needed since strict attention must be paid to food safety (e.g. storage of perishable food and , temperature) as well as hy-giene (Gaboury, 2005). Several medicines and/or vaccines need to be transported in a refrigerated box


because they must be kept at the right temperature (UNICEF, 2012). These issues require humanitarian organizations to engage in preparatory activities such as inventory prepositioning in warehouses. Ukkusuri and Yushimito (2008) define preposition-ing as: ‘the storage of inventory at or near the disas-ter location for seamless delivery of critical goods’. Prepositioning will reduce the lead-times for reach-ing places that are affected by a disaster. Time is an important factor in the provision of relief ; this is especially critical in the first 72 hours (Nahleh et al., 2013). The survival rate in affected areas is en-hanced by the quick availability of critical supplies such as blood and water as well as other resources. Critical supplies and relief personnel must therefore be transported quickly and efficiently to minimize the cost of the operations and maximize the survival rate of the affected people (Nahleh et al., 2013). All these aspects lead to supply chain challenges when disaster strikes.

Facility location is a key problem that has a con-siderable effect on the success of relief operations (Nahleh et al., 2013). Facility location concerns the placement of facilities taking several characteris-tics into account such as demand size and location (Caunhye, Nie, & Pokharel, 2012). Simchi-Levi, Ka-minsky, and Simchi-Levi (2008) state that business literature indicates that facility location decisions involve the number, location, size and capacity of each facility. These considerations also apply to the humanitarian sector (Richardson, Leeuw, & Vis, 2010). Facility location decisions have a direct im-pact on the operating cost and on the timeliness of response to the demand (Haghani, 1996). In order to respond quickly to the onset of a disaster, facility location and stock pre-positioning are therefore key decisions in humanitarian relief (Balcik & Beamon, 2008). Distributing relief supplies from strategical-ly- located warehouses improves the efficiency of disaster relief in economic terms, but also in terms of transport ation efficiency, speed and demand sat-isfaction (Döyen, Aras, & Barbarosoğlu, 2012). In humanitarian supply chains, this may translate into minimizing transportation cost (Drezner, 1995) and delivery time (Akkihal, 2006). In fact, within relief operations, a faster delivery time will often be cho-sen over lower costs (Akkihal, 2006).

A popular modeling approach in facility location is the “covering problem”. In covering problems, cus-tomers receive service by facilities depending on the distance between customers and facilities (Farahani,

Asgari, Heidari, Hosseininia, & Goh, 2012). Custom-ers receive service from a facility when the distance is equal to, or lower than, a predefined number – the so-called coverage distance or radius. In the case of disaster relief, it is difficult to set such a requirement. Disaster relief supply chains have to deal with high levels of demand uncertainty and large-scale de-mands at short notice, such as damaged roads, dis-traught victims , fragile communication lines, short lead times, and uncertainty about what relief sup-plies are actually needed (Nahleh et al., 2013). Bal-cik and Beamon (2008) indicate that the dominating characteristics that bring complexity into disaster relief chains are the unpredictability of the event (in terms of timing, location, type and size), the quan-tity of the needs arising and the short lead times required for many different supplies. Generally in these circumstances stakes are high, and there is a lack of appropriate resources (supply, people, tech-nology, transportation capacity and money).

2.2 Factors influencing new warehouse locations

MacCarthy & Atthirawong (2003) investigated rel-evant factors affecting location decisions. Although their research was mainly focused on manufactur-ing organizations, these factors can also be applied to humanitarian organizations (Richardson et al., 2016). MacCarthy & Atthirawong (2003) identified thirteen major factors. Each major factor also has specific sub-factors that include quantitative and qualitative aspects that are relevant to making loca-tion decisions. These include operational, strategic, economic, political, social and cultural dimensions.

Richardson and Leeuw (2012) and Richardson et al. (2016) draw on the work of MacCarthy & Atthira-wong (2003) to identify 10 main factors that have an influence on humanitarian inventory preposition-ing locations. Their top five factors include: the cost of operating a facility, the speed of humanitarian response, the availability and quality of labor, the availability and quality of business and support ser-vices (which consist of standard business services (e.g., warehousing and handling of goods) and spe-cific business services (e.g., procurement), (cf. Rich-ardson et al., 2016)) and the availability and quality of the infrastructure. The other factors in their top 10 are as follows (cf. Richardson et al., 2016): availabili-ty of suppliers, characteristics unique to the location (i.e., what gives a location an advantage over other potential facility locations (Ulgado, 1996) such as the space to carry out specialized operations), gov-


ernment and political factors, economic factors and community environment (which includes the com-munity’s attitude to business, among other factors ), and social and cultural factors (which, for example, relates to the general level of acceptance of certain relief supplies ). These factors fit in the framework of MacCarthy and Atthirawong (2003), though some factors are specific to humanitarian supply chains.

In addition to the papers by MacCarthy and Atthi-rawong (2003) and Richardson et al. (2016), which summarize the academic research in the area of fac-tors affecting facility location, we have investigated four industrial reports that discuss location factors. We have selected these four industry reports in con-sultation with Dutch and Belgian facility location experts; these reports are considered key sources of information regarding facility location in Western

European industry. The VIL Flanders Institute for Logistics (2006) and the European Distribution re-port of Cushman & Wakefield (2008) identified the following factors: transport system (road, sea, rail, and air, as well as the problem of traffic congestions), accessibility of the markets, costs of storage space, land and labor (rent, land and labor costs), supply of buildings and land, labor supply and productivity, know-how of logistics and languages. According to Inbound Logistics (2012) the following factors are important when choosing a location:transportation infrastructure, business culture and IT competen-cy. The Holland International Distribution Council (HIDC, 2012) identified the following factors: in-frastructure, the business environment (quality of overall/port/railroad infrastructure) and taxes. An overview of all the factors and their sources is pro-vided in Table 1.

Table 1. Overview of factors that influence facilitylocation as outlined in the literature and used in our study

Facility location factors derived from the literature ReferenceCostLabor standards InfrastructureProximity to suppliersProximity to markets/customersProximity to facilities of the parent company Proximity to competitionQuality of lifeLegal and regulatory frameworkEconomic factorsGovernment and political factorsCharacteristics of a specific location

Maccarthy and Atthira-wong (2003)

Operational Costs of a facilitySpeed of humanitarian responseAvailability and labor standard Availability and quality of business and support servicesAvailability and quality of infrastructureAvailability of suppliers Unique features of the locationGovernment and political factorsEconomic factors and the local environmentSocial and cultural factors

Richardson and Leeuw (2012); Richardson et al. (2016)

Transport system (road, sea, rail, air, and traffic congestion )Accessibility to the marketsCosts of storage space, land and labor (rent, land and labor costs)Supply of buildings and landLabor supply and productivityKnowhow of logistics and languages

VIL Flanders Institute for Logistics (2006) andThe European Distribu-tion Report of Cushman & Wakefield (2008)


Transportation infrastructureBusiness cultureIT competency

Inbound Logistics (2012)

InfrastructureBusiness environment (quality of overall/port/railroad infrastructure)Taxes

HIDC (2012)

Factors used in this studyInfrastructureLabor quality and availabilityPolitical environment Unique features of the location

the infrastructure (logistics quality and competence of a location – see section 3).

3. METHODOLOGY

This section will describe the methodology that will be employed in this paper. Our research can be classified as desk-research based on public sec-ondary data. An advantage of using secondary data is that this type of data is easily accessible and can therefore be obtained relatively quickly (Malhotra & Birks, 2007). For example, information about hu-manitarian organizations can be obtained relatively easily via their websites and/or annual reports. The use of public data will also enhance the validity of the research findings since similar results may be obtained if this research is replicated (Malhotra & Birks, 2007). Where possible and necessary, we emailed organizations for additional (publicly avail-able) information.

A key constraint on our ability to establish a re-search sample was the question of whether public sources could be found about warehouse locations of humanitarian organizations. Furthermore, the or-ganizations had to have at least a regional or pref-erably a global scope. We used Reliefweb (www.reliefweb.int) and a list of non-governmental orga-nizations belonging to Global Corps, to compile a list of 32 humanitarian organizations. . Not all ma-jor organizations could be put on the list due to the lack of any publicly available relevant supply chain information. The list can be found in Appendix 1. Reliefweb is part of the United Nations Office for the Coordination of Humanitarian Affairs. They func-tion as a digital platform to provide reliable disaster and crisis updates to humanitarians. The next step was to determine the current warehouse locations of these organizations. These warehouse locations

We base our paper on the study by Richardson et al. (2016) which is the only empirically grounded study in this domain that has been carried out so far, and the study by MacCarthy and Atthirawong (2003), which is the key source for the paper by Richard-son et al. (2016). We aim to empirically verify these frameworks by analyzing the current location of warehouses using publicly available information. This restricts the factors that we can use since not all the data may be available. In our research we focused on four factors: infrastructure, labor qual-ity and availability, government and political fac-tors and the unique features of the location. We left out costs since these cannot be estimated based on public sources. The only aspect related to costs that we can measure is the number of organizations that make use of the United Nations Humanitarian Re-sponse Depot (UNHRD) network. Space is provided for free to the participating organizations (cf. Rich-ardson et al., 2016). The United Nations World Food Programme manages this network and its depots are located around the world: Brindisi (Italy), Ac-cra (Ghana), Dubai (United Arab Emirates), Subang (Malaysia) and Panama City (Panama).

The factors are in line with the most decisive factors mentioned by MacCarthy and Atthirawong (2003) though we could not measure all factors in the list of most decisive factors outlined by Richardson et al. (2016). We could not take speed directly into ac-count – a factor on the top of the list of Richardson et al. (2016) - since the actual speed of delivery is not documented. However, as discussed in section 3, the category ‘infrastructure’ contains distance to an airport or seaport. Quick access to ports contrib-utes to speed in the supply chain. The other factor in the list of most decisive factors of Richardson et al. (2016) - availability and quality of business and sup-port services – is also a part of what we measure in


were determined by analyzing the annual reports and websites of the humanitarian organizations listed in Appendix 1. After identifying the current warehouse locations, these locations were grouped per country. In this way, we were able to use coun-try-based information such as the Enabling Trade Index (by the World Economic Forum) or Logistics Performance Index (by the World Bank) to rate the locations. Below we discuss the operationalization of the location factors.

For the factor ‘infrastructure’ we used the Enabling Trade Index to rate countries on the quality of in-stitutions, policies, infrastructure and services that facilitate the free flow of goods over borders and to their destination. The four main categories of this index are: market access, border administra-tion, infrastructure and the operating environment. These four main categories are further divided into subcategories (pillars). The categories used for this research will be infrastructure and availability and quality of infrastructure (pillar 4). This pillar mea-sures the quality of the infrastructure of different kinds of transportation modes: road, air, rail and sea (WEF, 2014).

Information about the Logistics Performance Index (LPI) will be retrieved from the country scorecard of the World Bank. The World Bank is an institute that plays a vital role in providing financial and technical assistance to developing countries. In addition, the World Bank provides several reports such as reports that contain the LPI. For this research the Logistics Performance indexes of infrastructure and logistic performance will be used.

Other information that will be retrieved from the World Bank concerns the ‘labor quality and avail-ability’ of a country. Four factors will be used to measure this factor: the labor force (i.e., people aged 15 and over who conform to the definition of the International Labour Organization of an economi-

cally active population), the participation rate (i.e., the proportion of the population aged 15 and older that is economically active) and unemployment rate (the share of the labor force that is without work but available for and seeking employment). The fourth factor is ‘Labor Market Efficiency’, which indicates how efficient countries allocate their workers with regard to their most effective use and provides the incentives for them to give their best efforts in their jobs (WEF, 2013).

The ‘political environment’ factor is measured by the Global Peace Index. The Global Peace Index ranks countries according to their level of peace. This ranking is based on 22 qualitative and quanti-tative indicators, and covers three broad areas : the level of safety and security in society, the extent of domestic or international conflict and the degree of militarization (GPI, 2013). In 2013 this ranking con-sisted of 162 independent countries or States.

The unique features of the location are operational-ized by the number of incidents that affect the loca-tion (the number of hazardous incidents as well as number of affected people). We use data from The international Disaster Database EM-DAT, which is a part of the Centre for Research on the Epidemiology of Disasters (CRED). Data from the last ten years will be employed for this research. The disasters are divided into two types: natural and technological disasters. As well as the number of affected people, the number of disasters will be included , since this will show the proportion of the number of affected people to the number of disasters . The criteria for inclusion in the EM-DAT database are as follows: 10 or more people are reported as killed, 100 people are reported affected, a call for international assis-tance has been made and/or a declaration of a state of emergency is made. At least one of these criteria has to be fulfilled for a disaster to enter the database. Table 2 provides and presents an overview of how the selected factors will be measured


Table 2: Overview of the assessment of the factors

Main factor Source

InfrastructureInfrastructure Availability and quality of infrastruc-ture (pillar 4)

Enabling Trade Index (ETI)

Infrastructure Logistic performance

Logistics Performance Index (LPI)

Labor quality and availability

Labor forceDegree of Participation Unemployment rate

World Bank

Labor market efficiency World Development Report

Political environment Level of peace in the country Global Peace index (GPI)

Unique features of the location

Number of affected peopleNumber of hazards

EM-DAT: The international Disaster Database

4. RESULT AND ANALYSIS

Of the 32 humanitarian organizations incorporat-ed in our analysis we had to discard four organi-zations because they did not (actively) operate a warehouse (Partners in Health and the Emergen-cy Nutrition Network (ENN), among others). For seven other organizations, the warehouse locations could not be identified based on public information (e.g. Caritas, Food For The Hungry International

(FHI), Habitat for Humanity, and Hunger Plus Inc.). This was because of a lack of complete infor-mation on their websites and a failure to respond to the emails sent to the humanitarian organizations to obtain this information. This left us with 21 re-maining organizations, of which 11 are members of the United Nations Humanitarian Response Depot (UNHRD) network. Figure 1 shows the geographi-cal distribution of warehouse locations of 21 differ-ent organizations.

Figure 1. Geographical distribution of the warehouse locations


The colors of the pins in Figure 1 represent the num-ber of organizations that have a warehouse at that location. For example, 16 organizations have a ware-house at the red pins (Dubai). Figure 1 shows that the warehouses are spread all around the world.

Figure 2. Number of warehouses per country

Each continent has at least one warehouse location. A complete table including the exact number of warehouses per organization is presented in Appen-dix 2. Figure 2 shows the number of organizations that have a warehouse location by country.

The humanitarian organizations that we inves-tigated have warehouse locations in 27 different countries. The UNHRD network represents five countries: United Arab Emirates (Dubai), Pana-ma, Italy, Ghana and Malaysia. The presence of a UNHRD facility most likely explains why these five countries also have the highest number of warehouses in Figure 2. We incorporated in total 109 warehouses in our study and 69 of these ware-house locations (from 11 different organizations) are part of the UNHRD network. UNHRD offers free warehouse storage space and logistical sup-port to humanitarian organizations that are mem-ber of the UNHRD network (Duran et al., 2007). The fact that many organizations locate their relief supplies in the UNHRD network is an indication that they do take costs into account when making location decisions. This is in line with Richard-son et al. (2016) and MacCarthy and Atthirawong (2003), who both argued that costs represent a key factor in the choice of location .

In our analysis we will only focus on the countries that have locations of two or more organizations.

This leaves 11 countries, namely United Arab Emir-ates (Dubai), Panama, Italy, Ghana, Malaysia, the United States, the United Kingdom, Kenya, El Salva-dor, Indonesia and Vietnam. We analyzed the coun-tries using the factors described in Table 2.

The first factor is infrastructure. Our assessment of the infrastructure will be divided into two parts: the distance to airports and seaports, and an analysis of the Enabling Trade Index (ETI) and the Logistic Per-formance Indicators (LPI). With regard to the first part, we identified the main airports and seaports of all the countries in the sample. We then calculated the distance from the warehouse to the nearest air- and seaport. In some cases, the nearest airport was not the largest or most commonly used airport in the country, and thus the distance was estimated s from the warehouse to the largest airport were measured. Table 3 shows the distances from the warehouses to the nearest air- and seaports. A complete overview of these distances, transportation times, warehouse addresses and the names of the sea- and airports can be found in Appendix 3.


Table 3. Estimated distances in countries from the humanitarian warehouses to the nearest airports - and seaports

Country City Distance to nearest airport Distance to seaport

Italy Brindisi 1 km 402 km

Ghana Accra 1 km 30 km

Malaysia Subang 1 km 30 km

Kenya Nairobi 19 km 336 km

United Arab Emirates Dubai 21.6 km 23.3 km

United States Denver/Michigan 26/42 km 1670/998 km

Panama Panama City 29 km 1.4 km

Vietnam Hanoi 31 km 109 km

Indonesia Jakarta 40 km 30 km

El Salvador San Salvador 42 km 184 km

United Kingdom Oxford-Bicester/Milton Keynes/Salford-Blackburn

76/44/20 km 106/93/61 km

Table 3 shows that in almost every country the hu-manitarian warehouses are less than 50 kilometers away from a major airport. This shows that it is an important factor for a warehouse to be close to an airport, because when disaster strikes it can be cru-cial to get the supplies to the victims in the shortest possible time. Three warehouse locations are even located at an airport. Seaports, on the other hand, are often much further away from the main ware-houses. Only five of the eleven locations are closer than 50 kilometers to a seaport, which shows that the distance to a seaport is less important when choosing a warehouse location. One may conclude from this that when making decisions about access, humanitarian organizations give priority to fast de-livery rather than reducing costs.

The second part of assessment of infrastructure will be carried out by means of two indices. The areas that will be assessed through the Enabling Trade In-dex (ETI) are the entire infrastructure and the avail-ability and quality of the infrastructure (pillar 4 of the ETI). The ETI scores 138 countries through scores ranging from one and seven, where one is the low-

est possible score and seven is the highest. The areas that are assessed with the Logistics Performance In-dicator (LPI) are as follows: Infrastructure, and Lo-gistics quality and competence. The LPI uses a scale from one to five, where one is the lowest score and five the highest. Table 4 shows the ranks and scores of the countries in question.. The rank is based on the ETI’s overall ranking of infrastructure out of 138 countries (third column in Table 4). When looking at all the ETI scores the top five countries are: United Kingdom, United States, Dubai, Malaysia and Italy. When looking at the LPI scores of infrastructure in general and the competence and quality of logistics services (e.g. transport operators, custom brokers), we see the same countries in the top five. This shows that these five countries have the best infrastructure and logistics quality and competence of the ware-house locations in scope. This is not surprising , because these countries are more economically de-veloped than the remaining six countries and hence have a better infrastructural system.. A striking fea-ture in this Table is that one of the UNHRD loca-tions, Ghana, achieved some of the worst scores of all the countries.


Table 4. Enabling Trade Index and Logistics Performance Indicator by country

ETI 2014 LPI 2014

Infra-structure

Pillar 4: Availability &Quality

Infra-structure

LogisticsQuality and Competence

# WH

Rank (out of 138)

Score (1-7)

Rank (out of 138)

Score (1-7)

Score (1- 5) Score (1-5)

UK 4 4 6 10 5,9 4,16 4,03US 9 8 5,8 8 6 4,18 3,97Dubai 16 10 5,8 1 6,5 3,7 3,5Malaysia 13 23 5,1 14 5,3 3,56 3,47Italy 13 32 4,8 22 4,8 3,78 3,62Panama 14 45 4,3 31 4,4 3 2,87Vietnam 2 60 3,9 74 3,3 3,11 3,09Indonesia 3 64 3,9 60 3,6 2,92 3,21El Salvador 3 70 3,8 75 3,3 2,63 3,16Kenya 3 93 3,3 85 2,9 2,4 2,65Ghana 13 95 3,2 94 2,7 2,67 2,37

The second factor that we analyzed was the qual-ity and availability of labor. To measure this factor, information provided by the World Bank was used. Labor standards can be divided into three parts: the total labor force, the total participation rate and the unemployment rate. Everyone who is older than 15 and who complies with what is defined by the La-bour Organizations as an economically active group of people, belongs to the ‘total labor force’. The par-ticipation rate is the proportion of the population aged 15 and older that is economically active. The unemployment rate means the percentage of the to-tal labor force that is without work but available for and seeking employment. The labor market efficien-cy index (which indicates how efficiently countries allocate their workers with regard to their most ef-fective use and provides incentives for them to make the best effort they can in their jobs) is reflected by means of a score ranging from one to seven, where one is the lowest and seven the highest possible score. Table 5 provides an overview of the corresponding scores. The ranking is based on the participation

rate of the country. When looking at the total work-force in the countries in scope the United States was found to have the largest workforce and Panama the smallest (158.686.472 people in the USA compared with 1.777.005 people in Panama). We also observed that the participation rate varies from 79% in Dubai to 49% in Italy. This means that in Italy, more than half of the total work force is not economically ac-tive. Italy also has the highest unemployment rate and the lowest efficiency rate of the countries in the table, which may negatively influence the decisions about locating a warehouse in that country . Except for the smaller size of the work force available Dubai received high scores. In the remaining rankings they are in the top four. Vietnam is in the top three on all rankings, except for labor market efficiency (6th), which makes Vietnam a potentially suitable ware-house location. The United Kingdom has the highest labor market efficiency, but its unemployment rate is rather high compared to that of the other coun-tries. All the other countries are in the middle, and thus no conclusions can be drawn about them.


Table 5. Labor quality and availability of labor of the countries in question

Worldbank data /2013 WDR 2014

# WH Labor Quality Labor Market efficiency

Labor force Participation rate Unemployment. rate Score (1-7)Dubai 16 6.248.007 79 3,8 5,24Vietnam 2 52.859.471 77 2 4,51Ghana 13 10.779.112 69 3,6 4,08Indonesia 3 118.378.606 68 6,6 3,87Kenya 3 16.697.483 67 9,2 4,62Panama 14 1.777.005 66 4,5 4,17US 9 158.686.472 63 8,1 5,37UK 4 32.377.782 62 7,9 5,42El Salvador 3 2.708.794 62 6,9 3,86Malaysia 13 12.717.901 59 3,1 4,82Italy 13 25.658.144 49 10,7 3,72

The third factor is the political environment. This will be measured by means of the Global Peace In-dex, which ranks countries according to their level of peace. Table 6 shows the Global Peace Index of 2013 that lists countries in terms of the highest index for peace to the lowest. Eight out of the eleven coun-tries in scope are ranked within the first 60 (out of 162) countries with the highest peace index, which is quite a positive observation. If the peace index is high, it will be more unlikely to encounter problems when one needs to distribute supplies from a ware-

house. Moreover, if a country is stable it is safer for humanitarian organizations to ask help from the lo-cal people, which is necessary when a disaster oc-curs . All five UNHRD warehouse locations are in countries with a high peace index. However, the dif-ference between the first ranked and the last ranked of the 11 countries in scope is considerable: 29th (Malaysia) and 136th (Kenya). Although the coun-tries with the largest number of warehouses are very safe it does not seem to be a common practice to lo-cate warehouse in only the safest countries.

Table 6. Global Peace Index (2013) by country in question

Global Peace Index 2013# WH Rank (out of 162) Score

Malaysia 13 29 1574Italy 13 35 1663Dubai 16 36 1679Vietnam 2 41 1772UK 4 44 1787Indonesia 3 54 1879


Panama 14 56 1893Ghana 13 58 1899US 9 99 2126El Salvador 3 112 2240Kenya 3 136 2466

The last factor that we will analyze is a key feature in each country : the number of affected people and number of hazards. To provide a clearer overview of the affected number of people, a distinction will be made between natural and technological hazards. Only the numbers of the last ten years (2003-2013) will be presented. Table 7 provides an overview of the number of people affected per type of haz-ard as well as the total number of people affected. The rank order is from the country with the small-est number of people affected to the country with the largest number of affected people. We also list the number of disasters in Table 7 (last column). This shows for example that Kenya has almost as many affected people as the United States, but Ke-nya only had 93 disasters , while the United States had 248. The average number of affected people in

Kenya (209.651) is much higher than in the United States (84.113). This also indicates that even though the United States has the largest number of affected people, it does not mean that the United States has an unstable environment. One can see from this Ta-ble that the most used location (Dubai) is also the location that has experienced fewest disasters (four) with altogether 32 people affected. This Table shows that warehouses are often located far away from di-saster- prone locations. In addition, four out of five UNHRD warehouses (Dubai, Italy, Panama and Ma-laysia) are in the top five countries where there were the least number of people affected, which implies that they are not located in a disaster-prone loca-tion. The complete table, including the distinctions between (sub) types of hazards, can be found in Ap-pendix 4 and 5.

Table 7. Number of people affected by disasters in the countries analyzed (2004-2013)

#WHPeople affected

by Natural disasters

People affected by Technological

disasters

Total number of people affected

Number of

hazards

Number of inhabitants in country

(2014)

Dubai 16 0 32 32 4 9,086,139Italy 13 91,405 938 92,343 49 60,789,140Panama 14 112,217 1,153 113,370 25 3,867,535UK 4 394,721 153 394,874 32 64,559,135Malaysia 13 496,633 218 496,851 27 29,901,997El Salvador 3 569,691 114 569,805 24 6,107,706Ghana 13 704,714 316 705,030 30 26,786,598Indonesia 3 10,860,609 17,509 10,878,118 229 254,454,778Vietnam 2 18,281,545 5,253 18,286,798 103 90,728,900Kenya 3 19,448,077 49,454 19,497,531 93 44,863,583US 9 20,856,615 3,338 20,859,953 248 318,857,056

Source: EM-DAT the International Disaster Database; http://data.worldbank.org/indicator/SP.POP.TOTL (for population figures)


5. DISCUSSION, CONCLUSIONS, LIMITA-TIONS AND FUTURE RESEARCH

The goal of this paper was to empirically verify char-acteristics of warehouses locations of humanitarian organizations. The characteristics analyzed were de-rived from the studies undertaken by Richardson et al. (2016) and MacCarthy and Atthirawong (2003). We investigated 21 organizations where public in-formation was available. The locations of the ware-houses of these organizations are spread all around the world: each continent has at least one warehouse location and some countries host multiple organiza-tions with warehouse locations.

A first observation is that having a good infrastruc-ture was found to be an important characteristic of the warehouse locations of the humanitarian orga-nizations we investigated. All the locations in our sample have good access to airports. Since the first 72 hours after a disaster are critical for effective re-sponse and the affected areas are often difficult to reach, having an infrastructure that facilitates speedy response is of crucial importance.. Most of the loca-tions that were investigated were not very close to a seaport. Only 5 out of the 11 locations were within a distance of 50 km to a seaport. However, the first response generally takes place by means of aircraft, which means the distance to airports is more impor-tant . As argued by Richardson et al. (2016) the abil-ity to provide a quick response to disasters is a key consideration in facility location.

We also observed that the humanitarian warehouse locations we investigated are in the top 60 safest countries (out of 162) which is evidence that safety is a serious consideration. However, the organiza-tions have located their facilities in relatively safe areas. We also found that in many cases, the facility locations are far away from disaster-prone regions. A good example of this is the presence of many or-ganizations in Dubai, which is a place that is hard-ly ever affected by disasters but which has a very good infrastructure,and good access to resources (cf. Leeuw, Kopczak, & Blansjaar, 2010).

Although we cannot draw statistically supported conclusions, our results show that many (large) or-ganizations use UNHRD facilities as a warehouse lo-cation. This may be driven by the fact that UNHRD offers the location for free to UNHRD members. We therefore expect that costs are an important driver for warehouse location decisions, as also identified by Richardson et al. (2016). Our findings thus sup-

port those of Duran et al. (2007). They stated that the UNHRD network provides free warehousing, which makes the implementation of a pre-positioning net-work financially and logistically better feasible. This result is most likely related to the fact that a signifi-cant portion of our sample co-locates their products at UNHRD premises. This also indicates that orga-nizations prefer to cluster warehouse activities, par-ticularly when there is no fee involved for using the warehouse (such as in the case of the UNHRD net-work). As a result, the presence of humanitarian or-ganizations in a certain location will have a positive influence on other organizations and encourage them to locate their facility there as well. By doing this, they can create opportunities for collaboration and coor-dination with the other organizations (Richardson et al., 2010). Collaboration is not just important for com-mercial logistics but also for humanitarian logistics (Beamon, 2004; Wassenhove, 2006).

We unfortunately cannot draw conclusions with re-gard to the factors labor quality and availability and the political environment (as measured by the Glob-al Peace Index) since we could not observe large dif-ferences between locations.

In summary, , our results show that humanitarian warehouses are often located in areas with good quality and availability of infrastructure (all ware-house locations were within a distance of 50 km of an airport, which suggests that access to other loca-tions is good too), and in relatively safe areas that are not prone to disasters. We can thereby confirm that a number of key location factors identified by Richardson et al. (2016) indeed seem to represent ac-tual warehouse locations and therefore most likely affect location choice.

Our research comes with limitations. Unfortunately, not all warehouse locations of the major organiza-tions could be located due to lack of public informa-tion available. Future research should aim to expand the information provided here and include addi-tional organizations where possible, in order to pro-vide an as complete overview of factors as possible. Expansion will also allow for a statistical analysis of the data, something that was impossible in this study given the limited amount of data available for comparison purposes. We furthermore did not include locations with only 1 organization . Finally, we did not distinguish between large and small or-ganizations (e.g. in terms of the facilities required) nor did we differentiate between the foci of the or-ganization in terms of the product or type of activ-


ity that needed to be supported by facilities. Future research may aim to further detail this.

6. REFERENCES

Akkihal, A. R. (2006). Prepositioning for humanitarian opera-tions (MSc Thesis, Massachusetts Institute of Technology, Cambridge, USA).

Balcik, B., & Beamon, B. M. (2008). Facility location in humani-tarian relief. International Journal of Logistics Research and Applications, 11, 101-121.

Balcik, B., Beamon, B. M., & Smilowitz, K. (2008). Last mile dis-tribution in humanitarian relief. Journal of Intelligent Trans-portation Systems, 12(2), 51-63.

Beamon, B. M. (2004). Humanitarian relief chains: Issues and chal-lenges. Proceedings of the 34th International Conference on Computers & Industrial Engineering, San Francisco, CA, USA.

Campbell, A. M., Vandenbussche, D., & Hermann, W. (2008). Rout-ing for relief efforts. Transportation Science, 42(2), 127-145.

Caunhye, A. M., Nie, X., & Pokharel, S. (2012). Optimization models in emergency logistics: A literature review. Socio-Economic Planning Sciences, 46(1), 4-13.

Cushman & Wakefield (2008). European Distribution Report. London: Cushman and Wakefield.

Döyen, A., Aras, N., & Barbarosoğlu, G. (2012). A two-echelon stochastic facility location model for humanitarian relief lo-gistics. Optimization Letters, 6(6), 1123-1145.

Drezner, Z. (1995). Facility location: A survey of applications and methods (Springer Series in Operation Research). Hei-delberg, Germany: Springer Verlag.

Duran, S., Guiterrez, M., & Keskinocak, P. (2007). Pre-position-ing of emergency items worldwide for CARE International. Presentation at INFORMS annual meeting, Seattle, USA.

Farahani, R. Z., Asgari, N., Heidari, N., Hosseininia, M., & Goh, M. (2012). Covering problems in facility location: A review. Computers & Industrial Engineering, 62(1), 368-407.

Farahani, R. Z., Bajgan, H. R., Fahimnia, B., & Kaviani, M. (2015). Location-inventory problem in supply chains: A modelling review. International Journal of Production Research, 53(12), 3769-3788.

Gaboury, J. (2005). Hungry to serve. Industrial Engineer, 37(5), 28-29.

Gatignon, A., Wassenhove, L. N. Van, & Charles, A. (2010). The Yogyakarta earthquake: Humanitarian relief through IFRC’s decentralized supply chain. International Journal of Produc-tion Economics, 126(1), 102-110.

Global Peace Index (2013). Global Peace Index 2013; measuring the state of global peace. Geneva: Institute for Economics and Peace.

Haghani, A. (1996). Capacitated maximum covering location models: Formulations and solution procedures. Journal of Advanced Transportation, 30(3), 101-136.

Holland International Distribution Council (2012). Location Benchmark Study. Zoetermeer: Holland International Distri-bution Council.

Inbound Logistics (2012). “8th annual Global Logistics Guide”. Inbound Logistics. March. 41-49.

Kelly, C. (1995). Simplifying disasters: Developing a model for complex non-linear events. Australian Journal of Emergency Management, 14(1), 25-27.

Leeuw, S. De, Kopczak, L., & Blansjaar, M. (2010). What really matters in locating shared humanitarian stockpiles: Evidence from the WASH cluster. In L. M. Camarinha-Matos, X. Bouch-er, & H. Afsarmanesh (Eds.). Proceedings of the 11th Working Conference on Virtual Enterprises PRO-VE 2010 – IFIP AICT 336 (pp. 181-188). Heidelberg, Germany: Springer Verlag.

MacCarthy, B. L., & Atthirawong, W. (2003). Factors affecting lo-cation decisions in international operations: A Delphi study. International Journal of Operations & Production Manage-ment, 23(7), 794-818.

Malhotra, N. K., & Birks, D. F. (2007). Marketing research: An ap-plied approach (3rd European ed.). Harlow: FT Prentice Hall.

Medecin Sans Frontier. (2011). About MSF (Online). Retrieved from http://www.msf.org/msf/about-msf/about-msf_home.cfm

Melo, M. T., Nickel, S., & Saldanha-da-Gama, F. (2009). Facility location and supply chain management: A review. European Journal of Operational Research, 196, 401-412.

Nahleh, Y. A., Kumar, A., & Daver, F. (2013). Facility location problem in emergency logistic. International Journal of Me-chanical, Industrial Science and Engineering, 7(10), 833-838.

Richardson, D., Leeuw, S. de, & Vis, I. (2010). Conceptualising inventory prepositioning in the humanitarian sector. In L. M. Camarinha-Matos, X. Boucher, & H. Afsarmanesh (Eds.). Proceedings of the 11th Working Conference on Virtual En-terprises PRO-VE 2010 – IFIP AICT 336 (pp. 149-156). Heidel-berg, Germany: Springer Verlag.

Richardson, D., & Leeuw, S. de. (2012). Factors affecting global inventory prepositioning locations in humanitarian logistics: A Delphi study. Presentation at the 4th Production & Op-erations Management World Conference, Amsterdam, the Netherlands.

Richardson, D., Leeuw, S. de, & Dullaert, W. (2016). Factors af-fecting global inventory prepositioning locations in humani-tarian logistics. Journal of Business Logistics, 37(1), 59-74.

Simchi-Levi, D., Kaminsky, P., & Simchi-Levi, E. (2008). Design-ing and managing the supply chain concepts, strategies and case studies (3rd ed.). New York: McGraw Hill.

Ukkusuri, S. V., & Yushimito, W. F. (2008). Location routing ap-proach for the humanitarian prepositioning problem. Trans-portation Research Record: Journal of the Transportation Research Board, 2089, 18-25.

Ulgado, F. M. (1996). Location characteristics of manufacturing investments in the U.S.: A comparison of American and for-eign-based firms. Management International Review, 36(1), 7-24.


United Nations Children’s Fund. (2012), UNICEF Annual Sup-ply Report 2012. Copenhagen: UNICEF. Available at https://issuu.com/supplydivision/docs/unicef_supply_annual_re-port_2012_we.

Vlaams Instituut voor de Logistiek. (2006), Comparison of Top Locations for European Distribution and Logistics, Flanders Institute for Logistics.

Wassenhove, L. N. Van. (2006). Humanitarian aid logistics: Sup-ply chain management in high gear. The Journal of the Op-erational Research Society, 57, 475-489.

World Economic Forum (2013). Global competitiveness Report 2012-2013. Geneva: World Economic Forum.

World Economic Forum (2014). The Global Enabling Trade re-port 2014. Geneva: World Economic Forum.

Appendix 1: List of humanitarian organizations:

1. Action Against Hunger (AAH)

2. American Refugee Committee International

3. Care

4. Caritas Internationalis

5. Catholic Relief Services (CRS-USCC)

6. Emergency Nutrition Network (ENN)

7. Food For The Hungry International (FHI)

8. Habitat for Humanity

9. Humanitarian aid and civil protection depart-ment of the European Commission (ECHO)

10. Hunger Plus Inc.

11. International Federation of Red Cross and Red Crescent Societies (IFRC)

12. InterAction

13. International Organization for Migration (IOM)

14. International Rescue Committee (IRC)

15. Islamic Relief

16. Life for Relief and Development

17. Lutheran World Federation (LWF)

18. Médecins Sans Frontiers (MSF)

19. Mennonite Central Committee (MCC)

20. Mercy Corps

21. Norwegian Refugee Council (NRC)

22. Overseas Development Institute (ODI)

23. Oxfam

24. Partners in Health

25. Refugees International

26. Save the Children

27. The Office of U.S. Foreign Disaster Assistance (OFDA)

28. United Nations Children’s Fund (UNICEF)

29. United Nations High Commissioner for Refu-gees (UNHCR)

30. United Nations Office for the Coordination of Humanitarian Affairs (OCHA)

31. US Committee for Refugees (USCR)

32. World Vision International (WVI)


Appendix 2: List of humanitarian organizations and their warehouse locations (part 1)

Name Organization UNHRD WH yes/ no UAE Panama Italy Ghana Malay-

sia US UK KenyaEl

Salva-dor

Indone-sia

Viet-nam

1 Action Against Hun-ger (AAH) 1 1 1 1 1 1

2 American Refugee Committee Interna-tional

1

3 Care 1 1 1 1 1 1 4 Caritas Internatio-

nalis NA

5 Catholic Relief Ser-vices (CRS-USCC) 1 1 1 1 1 1

6 Emergency Nutrition Network (ENN) No WH

7 Food For The Hun-gry International (FHI)

NA

8 Habitat for Humanity NA 9 Humanitarian aid

and civil protection department of the European Commis-sion (ECHO)

1 1 1 1 1 1

10 Hunger Plus, Inc NA 11 Int. Fed. of Red

Cross and Red Cres-cent Societies (IFRC)

1 1 1 1 1 1 1 1

12 InterAction No WH 13 International Organi-

zation for Migration (IOM)

1 1 1 1 1 1

14 International Rescue Committee (IRC) 1 1 1 1 1 1


15 Islamic Relief 16 Life for Relief and

Development 1 1

17 Lutheran World Fed-eration (LWF) 1 1

18 Medecins Sans Fron-tiers (MSF) 1 1

19 Mennonite Central Committee (MCC) NA

20 Mercy Corps 1 1 1 1 1 1 2 21 Norwegian Refugee

Council (NRC) 1

22 Overseas Develop-ment Institute (ODI) No WH

23 Oxfam 2 1 24 Partners in Health No WH 25 Refugees Interna-

tional NA

26 Save The Children 3 227 The Office of U.S.

Foreign Disaster As-sistance (OFDA)

NA

28 United Nations Chil-dren’s Fund (UNI-CEF)

1 1 1 1 1 1

29 United Nations High Commissioner for Refugees (UNHCR)

1 1 1 1 1 1

30 United Nations Of-fice for the Coordina-tion of Humanitarian Affairs (OCHA)

1 1 1 1 1 1

31 US Committee for Refugees (USCR) 1 1 1 1 1 1


32 World Vision Interna-tional (WVI) 1 1 1 1 1 1 8

TOTAL: 11 16 14 13 13 13 9 4 3 3 3 2

*Although ECHO is not directly involved in humanitarian relief activities like the other organizations it has been included here since it funds stockpil-ing through the UNHRD network which it has actively supported

Appendix 2: List of humanitarian organizations and their warehouse locations (part 2)

Name Organization Libiya Cam-bodia

Nether-lands

Bel-gium France Spain Zam-

bia Nepal Philip-pines China Den-

markBo-livia

Dom. Rep. Iraq Ger-

manyAus-tralia

1 AAH 2 American Refugee

Committee Interna-tional

3 Care 1 4 Caritas Int. 5 CRS-USCC 6 ENN 7 FHI 8 Habitat for Humanity 9 ECHO 10 Hunger Plus, Inc. 11 IFRC 1 12 Inter-Action 13 IOM 14 IRC 15 Islamic Relief 16 Life for Relief and

Development 1

17 LWF 1 1


18 MSF 1 1 1 19 MCC 20 Mercy Corps 1 21 NRC 22 ODI 23 Oxfam 1 24 Partners in Health 25 Refugees Int. 26 Save The Children 1 1 27 OFDA 28 UNICEF 1 1 29 UNHCR 30 OCHA 31 USCR 32 WVI 1 1

TOTAL: 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Appendix 3: Distances and transportation times from the warehouse to (the main) airport and seaport

Country Name city Address WH Name airport Distance Time Name container port Distance Time

UAE Dubai Dubai Industrial City Al Maktoum Airport 21,6 km 0h27 Jebel Ali port 23,3 km 0h27Panama Panama city BLDG 200, Av. Omar

TorrijosTocumen International Airport

29 km 0h26 Balbao 1,4 km 0h04

Italy Brindisi Aeroporto Militare “Pierozzi, 72011 Casale

Leonardo Da Vinci International (Fiumicino)

581 km 5h20 Gioa Tauro 402 km 4h17

Brindisi – Salento Airport

1 km 0h02

Ghana Accra Kotoka International Airport

Kotoka International Airport

1 km 0h02 Tema 30 km 0h29


Malaysia Subang Jalan TUDM, Seksyen U7 40150 Shah Alam, Se-langor

Kuala Lumpur International Airport

52 km 0h41 Klang 30km 0h29

Sultan Abdul Aziz Airport (Subang)

1 km 0h02

US Denver 11000 East 40th Avenue, Denver International Airport

26 km 0h21 Houston 1670 km 15h50

Michigan 17300 W 10 Mile Rd. Southfield, MI 48075 (office)

Detroit Metropolitan Wayne County Airport

42 km 0h29 New York/ New Jersey

998 km 9h19

UK Oxford/ Bicester/ (near London)

Arkwright Road, Bicester

London Heathrow 87 km 0h56 Port of London 106 km 1h24

London Luton 76 km 1h02Milton Keynes Oxfam Southern Logis-

tics Centre, Milton Point, Garamonde Drive, Wym-bush

London Heathrow 79 km 1h03 Port of London 93 km 1h20

London Luton 44 km 0h33Salford/ Blackburn (near Manchester)

Bury Old Road , M7 4ZH Salford

London Heathrow 338 km 3h17 Liverpool 61 km 0h51

Manchester Airport 20 km 0h26Kenya Nairobi IFRC Offices Nairobi,

Woodlands Road, Jomo Kenyatta International Airport

19 km 0h25 Inland Container Depot Kisumu

336 km 4h44

El Salvador San Salvador IFRC Offices, 17 Calle Poniente y Avenida Henyi Dunant

El Salvador International Airport

42 km 0h33 Porto de la Union (former puerto Cutuco)

184 km 2h33

Indonesia Jakarta Jl. Pejaten Barat no. 8 Pasar Minggu, Jakarta Selatan, Jakarta 12550

Soekano- Hatta International Airport

40 km 0h45 Tanjung Priok 30 km 0h40


Vietnam Hanoi Trung Tu Diplomatic Compound, 6 Dang Van Ngu, Dong Da District

Noi Bai International Airport

31 km 0h47 Haiphong 109 km 2h04

Appendix 4: Number of people affected by disasters (2004-2013) (part 1)

Natural disasters SUMDrought Earthquake Epidemic Extr.Temp*. Flood Mass movement** Storm Volcano Wildfire

Dubai 0 0 0 0 0 0 0 0 0 0Panama 0 0 0 0 110.781 0 0 0 1.436 112.217Italy 0 81.400 0 0 9.840 160 5 0 0 91.405Ghana 0 0 18.351 0 686.363 0 0 0 0 704.714Malaysia 0 5.063 0 0 450.564 6 41.000 0 0 496.633US 0 6.262 0 31 11.221.201 8.893.846 735.275 20.856.615UK 0 4.501 47 382.793 7.380 394.721Kenya 17.650.000 0 16.700 0 1.781.115 262 0 0 0 19.448.077El Salvador 0 17.221 4.598 0 305.832 0 176.961 65.079 0 569.691Indonesia 0 7.560.370 93.862 0 2.856.294 20.573 14.265 315.045 200 10.860.609Vietnam 410.000 0 142 0 7.544.165 1 10.327.237 0 0 18.281.545

*Extreme temperatures ** Landslides (wet/dry)


Appendix 4: Number of people affected by disasters (2004-2013) (part 2)

Technological disasters SUM TOTAL (Natural + Technological)Industrial Miscellaneous Transport

Dubai 0 6 26 32 32Panama 0 1.125 28 1.153 113.370Italy 0 0 938 938 92.343Ghana 5 130 181 316 705.030Malaysia 0 0 218 218 496.851US 822 641 1.875 3.338 20.859.953UK 0 3 150 153 394.874Kenya 208 48.951 295 49.454 19.497.531El Salvador 0 50 64 114 569.805Indonesia 12.121 2.727 2.661 17.509 10.878.118Vietnam 5.013 0 240 5.253 18.286.798

Appendix 5: Number of disasters (2004-2013)

Natural disasters SUM

Techno-logical disasters

SUM TOTAL

Drought Earth-quake

Epi-demic

Extr.Temp.

Flood Mass move-ment

Storm Vol-cano

Wild-fire

Industrial Miscel-laneous

Trans-port

Dubai 0 0 0 0 0 0 0 0 0 0 1 1 2 4 4Panama 1 0 0 0 18 0 0 0 1 20 0 2 3 5 25Italy 1 3 0 6 10 2 3 0 2 27 0 1 21 22 49Ghana 0 0 6 0 9 0 0 0 0 15 5 2 8 15 30Malaysia 0 1 1 0 18 1 1 0 1 23 0 0 4 4 27US 0 0 0 9 50 0 130 0 24 213 8 7 20 35 248UK 0 1 0 5 13 0 9 0 0 28 0 1 3 4 32Kenya 5 1 12 0 30 3 0 0 0 51 3 14 25 42 93El Salvador 1 2 1 1 6 0 7 2 0 20 0 2 2 4 24Indonesia 0 36 6 0 68 21 4 15 2 152 8 11 58 77 229Vietnam 1 0 4 0 39 2 33 0 0 79 6 1 17 24 103


The Role of Private Stakeholders in Disaster and Humanitarian Operations

Tharcisio Cotta Fontainha Doctoral student at Pontifícia Universidade Católica do Rio de Janeiro – Rio de Janeiro – RJ, Brazil

[email protected]

Patricia de Oliveira Melo M.Sc. in Production Engineering from Universidade Federal do Rio de Janeiro – Rio de Janeiro – RJ, Brazil

[email protected]

Adriana Leiras Professor at Pontifícia Universidade Católica do Rio de Janeiro – Rio de Janeiro – RJ, Brazil

[email protected]

ABSTRACT: The role of private stakeholders in disaster operations goes far beyond the delivery of profits to its shareholders. Disasters and humanitarian operations literature acknowledges the impor-tance of private sector in disaster lifecycle; however, it lacks an analysis of the risks and benefits of each different form of their engagement in such operations (contractual relationships, one-off relationships and CSR - Corporate Social Responsibility partnerships). To address this research gap, a literature review was conducted on papers covering the perspective of private stakeholders when engaging in disaster and humanitarian operations with stakeholders from public and social groups. The results revealed that some challenges are specific from one approach and others are common for all of them. Moreover, despite the increasing of reputation capital and organizational learning being used to moti-vate CSR approach, they are mentioned as benefits in approaches with lower engagement - contractual and one-off relationship approaches. Thus, the benefits and risks of each approach need to be carefully addressed by scholars and field professionals in order to seek better results from the engagement of private organizations.

Keywords: Stakeholder theory, corporate social responsibility, humanitarian operations, disaster op-erations, private sector.


77

Fontainha, T. C., Melo, P. de O., Leiras, A.: The Role of Private Stakeholders in Disaster and Humanitarian OperationsISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 1 p 77 – 9378

1. INTRODUCTION

Disasters are events that cause a disruption that physically affects a system as a whole and threatens its priorities and goals (Van Wassenhove, 2006). Di-saster and humanitarian operations involve imme-diate search and rescue, medical treatment, provi-sion of shelter, basic supplies (i.e. water and food), special supplies (i.e. clothing), reestablishment of the infrastructure and of the manufacturing/commercial activities of essential services, and so forth (Blecken, 2010; Bastos, Campos, & Bandeira, 2014). Due to the amount and variety of activities in these operations, the responsibility is shared among different stake-holders (such as non-governmental organizations - NGOs, donors, aid agencies, government, military, logistics companies, and media) throughout the response and recovery periods, which are preced-ed from the plans developed in the mitigation and preparation stages (Altay & Green, 2006).

Despite the responsibility for action in disaster and humanitarian operations is traditionally attributed to the public sector - or the government in particular (Mankin & Perry, 2005) - the involvement of stake-holders from private sector has been increasingly recognized as fundamental, in accordance with the attention received by the academia and practice (Van Wassenhove, 2006; Kapucu, 2006; Inauen, Oli-vares, & Schenker-Wicki, 2010; Caruson & MacMa-nus, 2011; Abidi Leeuw, & Klumpp, 2015; Vega & Roussat, 2015). For instance, private stakeholders represented only 9.8% (140 national organizations) of all organizations that joined forces during the September 11th response in 2001 (Kapucu, 2006) and increased to 27% of all organizations that engaged in the Katrina Hurricane response in 2005 (Koliba, Mills, & Zia, 2011). While India has considered the importance of private sector since the Indian Ocean Tsunami in 2004 (Chatterjee and Shaw, 2015), only recently the involvement of private sector through their corporate social responsibility actions have begun in Africa countries (Van Niekerk, Ndlovu, & Chipangura, 2015). However, public and non-prof-it institutions report that maintaining an effective partnership with private sector is a challenging issue after a disaster, as investigated by Kapucu (2006). According to Kapucu’s study, only 14% of the suc-cessful partnerships that continued after the disaster have private organizations involved, versus 53% of non-profit and 33% of public organizations.

Koliba et al. (2011) point out that, while being “vic-tims of and responders to disasters, local business-

es and regional and national corporations had a key role to play in providing supplies and services needed for the response and recovery efforts”. Be-sides that, Tomasini and Van Wassenhove (2009a) reinforce that private stakeholders can be benefited: providing goods and services as a corporate citizen and learning and developing their business in order to act in critical situations, like those faced during disasters, exploring the purchasing needs of hu-manitarian organizations and other stakeholders. Moreover, it is also possible to attract some media attention to their participating in the disaster and humanitarian operations with a contractual rela-tionship involvement despite any further commit-ment or effort to coordination during the response to the event (Tomasini & Van Wassenhove, 2009b).

Despite all these benefits, Tomasini (2011) high-lights that the literature about the participation of private organizations in disaster and humanitarian operations focuses only on the knowledge which humanitarian organizations can get for working with private companies. Thus, despite the grow-ing number of papers reporting the importance of private sector engagement in disaster and humani-tarian operations, the different forms in which such organizations participate in disaster and humanitar-ian environments are isolated analyzed and not con-sistently explored in these papers. Considering also that stakeholders constantly play an intermediate role among other stakeholders (Friedman & Miles, 2006), it is even more relevant to analyze these dif-ferent forms of engagement by not only the dyadic relationship perspective (between two stakehold-ers), but also the intermediated and other complex relationships (involving not only private sector or-ganizations but other types of organizations).

Considering this research gap, a systematic litera-ture review is proposed in order to analyze risks, challenges, and benefits for private organizations engage in disaster and humanitarian environments. In this regard, systematical reviews increases the chance of finding much of the relevant literature on the subject, reduces the likelihood of a partial review, and increases the reliability of a research (Van Aken, Berends, & Van Der Bij, 2007). Moreover, according to Torraco (2005), this research method can deliver four different results: a taxonomy, a framework, a synthe-sis, and a research agenda; which the last two are im-portant results for understand the current situation of private organizations engagement in disaster and humanitarian operations and thinking forward.


In this context, the present paper aims firstly to iden-tify and categorize the different forms of engagement in disaster and humanitarian operations based on main concepts of purchasing and Corporate Social Responsibility in the disaster environment and hu-manitarian logistics (Guidry, Vaughn, Anderson, & Flores, 2015; Ingirige & Wedawatta, 2014; Sarmiento et al., 2015; Tomasini & Van Wassenhove, 2009b). Sec-ondly, though a systematic literature review, analyze the academic literature that reports the engagement of private organizations in such operations. Through this analysis, it is expected that practitioners and aca-demics could understand and address properly the issues that can prevent negative effects and attract a greater number of private organizations to disaster and humanitarian operations.

After this introduction, the theoretical foundation is presented in Section 2. The third section covers the research methodology adopted. Section 4 presents the results of the literature review. The fifth section presents a synthesis of the findings and the proposi-tion of a research agenda that need to be addressed by scholars and practitioners who are engaged in di-

saster and humanitarian operations initiatives. The paper ends summarizing the main findings and sug-gesting future studies.

2. THEORETICAL FOUNDATION ON THE ROLE OF PRIVATE STAKEHOLDERS IN DISASTER AND HUMANITARIAN OPERATIONS

Stakeholders are defined as “all groups or individu-als that affect or are affected by the business” (Free-man, 1984). Fontainha et al. (2015) developed the So-cial-Public-Private Partnership (or S3P) stakeholder model for disaster and humanitarian operations presented in the Figure 1. Based on generic models from business and specific models from disaster and humanitarian models (Cozzolino, 2012; Hellingrath, Link, & Widera, 2013; Thomas, 2003), Fontainha et al. (2015) attest that the S3P aims to highlight three important characteristics of stakeholders’ relation-ship: the central perspective of beneficiaries, the in-trinsic fragility of their relationships represented by dashed lines and that there are several connections among all stakeholders, both the dyadic and others complex relationships.

Figure 1. S3P stakeholder model of disaster and humanitarian operations

Source: Adapted from Fontainha et al., 2015


The private stakeholders are detailed as follow based on the definition provided by Fontainha et al. (2015):

• Private sector – this stakeholder contributes in di-saster operations through different methods, for example, when an organization donates goods or services from its own manufacturing operations or even when they join efforts with society in the preparation for disaster or to reestablish their own operation after the event (Cozzolino, 2012);

• Direct supplier – this stakeholder stands apart from the Private sector as a different stakeholder in the model due to the relevance in which suppli-ers of specific products and logistics services play in the disaster lifecycle. These suppliers’ efforts to provide aid to the beneficiaries directly lead to reduce the risks, alleviate the suffering, minimize impacts and even save lives (Cozzolino, 2012);

• Media – this stakeholder plays a very important role in humanitarian operations due the increas-ing speed in which the news is broadcasted by the mass media and online social networks (Fritz Institute 2012).

Caruson and MacManus (2011) and Nirupama and Etkin (2012) emphasize the urgency of formulating a collaborative partnership between the public and private sectors but stressed that not all partnerships are equally effective, since it is difficult to manage different cultures, laws, interests and organizational resources. In this sense, Nirupama and Etkin (2012) observed three different forms in which private sector engaged in emergencies with Canadian emergency management professionals: providing what is needed without ulterior purposes; providing what is needed with an aversion to major commitments; and being the organization responsible for the planning and the information sharing with the community, and with the best long-term care facilities preparations.

Based on this observation and from the companies’ main motivation perspective, the private sector en-gagement in disaster operations rely on three ap-proaches respectively: (a) contractual relationships, (b) one-off relationships (c) CSR - Corporate Social Responsibility partnerships (based on Guidry et al., 2015; Ingirige & Wedawatta, 2014; Sarmiento et al., 2015; Tomasini & Van Wassenhove, 2009b).

The first approach relies on the supply of prod-ucts and services to disaster and humanitarian op-erations in a traditional commercial agreement. In

this approach, the private organization complies with contractual obligations to provide goods and services as part of their contractual daily activities. For example, the contract of additional trucks from a transport company for regular water delivery in Ethiopia (OCHA, 2016) and the contract of ware-houses for humanitarian assistance in Syria after the conflict has begun (Logistic Cluster, 2014). Accord-ing to Blecken (2010), the purpose of purchasing in humanitarian operations is to ensure that humani-tarian organizations have the resources needed to support the demands in various operations in which they operate. Ertem and Buyurgan (2010) state that the purchasing function is decisive in response op-erations since the pre-positioned stocks and material donations are not enough to meet all the demand created by a disaster.

Purchasing in the humanitarian chain is performed by International and Local aid networks, and in some cases, by the Government itself (Taupiac, 2001; Bal-cik, Beamon, Krejci, Muramatsu, & Ramirez, 2010). According to Herlin and Pazirandeh (2012), there are two types of buyers in the humanitarian chain: large buyers, such as international NGOs and developed country governments, and small buyers, such as lo-cal NGOs and governments of developing countries. The major buyers are characterized by high volume of purchases, operations in various disasters, global reputation, recognized brands and legitimacy, strong purchasing power, and a relationship of interdepen-dence with global suppliers. On the other hand, small buyers usually deal with a limited number of suppli-ers in the local markets in which they operate, with a strong dependence on these suppliers and, therefore, have a small purchasing power.

Herlin and Pazirandeh (2012) explain that many humanitarian organizations buyers have conducted strategic processes to identify and develop their core competencies and hence outsource more and more activities, leading to a restructuring of its purchas-ing areas, which now play a strategic role within these organizations. Furthermore, Fudalinski and Pawlak (2012) point out that the major buyers usu-ally have a well-developed purchasing area, reveal-ing the relevance of this approach for disaster and humanitarian operations.

The last two approaches, one-off relationships and Corporate Social Responsibility partnerships, are based on the Stakeholder Theory (ST) and the Cor-porate Social Responsibility (CSR). In the Stake-holder Theory an organization is seen as essentially


composed of its relationships with their stakehold-ers. The central objective of ST is to expand the idea of an organization function, which is regularly seen as only the property of their owners – shareholders in public corporations – with limited liability for their effects upon others (Freeman, Harrison, Wicks, Parmar, & Colle, 2010). In this sense, the theory in general considers the relationships among stake-holders as its primary object of analysis in a direct and dyadic connection, however, some researches has argued that others complex relationship may be assumed among them, such as an intermediary role in the flow of resources (Frooman, 1999), influence (Rowley, 1997), identity (Rowley & Moldoveanu, 2003), and ideas (Friedman & Miles, 2002). Media is an example in which this type of relationship is observed in disaster and humanitarian operations, mostly because its function is generally to provide communication between two or more stakeholders while it still have their own environment, pressures, values, and ambitions (Friedman & Miles, 2006).

From this perspective, Corporate Social Responsibil-ity (CSR) further develops the ST in order to explain that companies should act having in mind the inter-ests of all stakeholders, including more than just the financial aspect (Freeman et al., 2010). One reason to justify the engagement of private stakeholders in social issues is the reputation capital concept, which is defined as a collective creation that describes the combined perceptions of multiple stakeholders re-garding a company’s performance and it is overall the stakeholder’s assessment of a company over time (Petrick & Quinn, 2000). Some authors use the CSR concept as one argument to encourage and justify the private sector involvement in disaster operations and to improve their engagement in the humanitar-ian supply chain (Van Wassenhove, 2006; Binder & Witte, 2007; Maon, Lindgreen, & Vanhamme, 2009; Maether, 2010; Tomasini, 2011).

However, some companies may act on social issues only in a responsive manner, after being pressured by its stakeholders (Perrini & Russo, 2010). In hu-manitarian logistics, these companies would engage in single actions, not being committed with the full operation nor with long-term partnerships. For in-stance, this is the case of small local business, which are the first responders and recognized as essential in the supply of medicine, food, shelter, debris re-moval, road repair etc. in the aftermath of a disaster (BCLC, 2012). These companies are the ones engaged in the one-off relationship approach that is derived

from the “wait and see” perspective facing the miti-gation and preparation, as detailed by Ingirige and Wedawatta (2014). This approach is based solely on answering society pressures concerning the envi-ronmental damage, natural resource scarcity, social gaps, and consumer demands (Menz, 2012).

Other companies, unlike, incorporate the CSR con-cepts in their business strategies and are always engaging in social issues that, in some way, affects one of their stakeholders. In humanitarian opera-tions, these companies are the ones that choose the CSR partnerships approach. This approach is char-acterized by the achievement of a maturity level in which, through the sustainability ideas private or-ganizations decide, as part of its goals, to develop actions that can improve its image for stakeholders (Wikström, 2010). Furthermore, the motivation for engaging in a CSR associated to disaster operations and humanitarian logistics from small and medium enterprises depends on the perceived exposure of the business location to natural hazards (Herbane, 2015; Yoshida & Dayle, 2005). In this situation, com-panies are more deeply involved in disaster and humanitarian operations also developing long-term partnerships, such as the partnerships among transportation companies (TNT, Agility, and UPS) and humanitarian organizations (Logistic Cluster, World Food Program, and World Economic Forum) (Gatignon & Van Wassenhove, 2008; Gatignon & Van Wassenhove, 2009; Stadtler & Van Wassenhove, 2012a; Stadtler & Van Wassenhove, 2012b).

Whereas Tomasini (2001) takes the CSR concept to analyze how companies can improve its commercial operations by learning from/ working with humani-tarian organizations, CSR concept has others discus-sion objects, such as the corporate reputation and the consequently reputational capital, that are relevant to enable others stakeholders to understand different types of rewards that private companies can acquire in return to engage in humanitarian operations.

The corporate reputation is one of the most important intangible assets of businesses today and is defined as “a collective construct that describes aggregate per-ceptions of multiple stakeholders about a company’s performance. It is a stakeholder’s overall evaluation of a company over time” (Podnar, Tuškej, & Golob, 2012). This concept can also be called shared value (Crane, Palazzo, Spence, & Matten, 2014). Through this definition, it is possible to realize the connec-tion between the concepts of corporate reputation and corporate social responsibility because, when


the company meets the demands of its stakeholders, the perception they have on the company improves, thereby improving corporate reputation.

Associated to the concept of corporate reputation, there is the concept of reputational capital, which is understood as one type of intangible wealth re-lated to the value of the brand and the identification of stakeholders with the company (Worden, 2003). In this sense, reputational capital is a fragile asset that involves a sense of belonging, takes too long to build, but can be easily damaged. Fombrum et al. (2000) associate this concept to the concept of repu-

tational risk, which is “the range of possible gains and losses in reputational capital for a given firm”. Since reputational capital is based on the value that each stakeholder perceives in the company, each stakeholder is also a source of reputational risk to the firm. The authors also state that being engaged in CSR activities can help manage the reputational risk, besides generating reputational capital and en-hancing performance. Table 1 shows how CSR activ-ities can increase the support of the firm’s stakehold-ers, which leads to a gain in the reputational capital and neutralizes the risks of losses in it.

Table 1. Stakeholder’s promise of support and neutralized risks due to CSR activitiesStakeholder Promise of suport Neutralized risks

Media Favorable coverage Threat of exposureCommunity Legitimacy - community protection Threat of ilegitimacyRegulators Legal action - Favorable Regulation Threat of legal actionCustomers Loyalty Threat of misunderstanding

Partners Collaboration Threat of defectionEmployees Commitment Threat of Rogue behavior Investors Value Threat to valueActivists Advocacy - seal of approval Threat of boycott

Source: Adapted from Fombrum et al. (2000)

Thus, the reputation capital is one of the most con-crete values perceived by stakeholders from pri-vate group in exchange for their engagement in di-saster and humanitarian operations, which varies according to the different engagement approaches. As an example on how organizations are trying to obtain such benefit, Vega and Roussat (2015) veri-fied the website of 17 world logistic service pro-viders and identified that: two firms (11.8%) does not make any mention of humanitarian activities; seven firms (41.2%) only include this activity in additional documents and; eight firms (47%) men-tion their humanitarian logistics activities directly on their web site pages suggesting greater concern. Such example reveals the attempt of logistic com-panies to bring value for their investors.

Corporate Social Responsibility states that the com-pany has the responsibility to meet the needs of all those who are in some way affected by its opera-tion. Thus, companies must act not only to avoid

the pressures exerted by internal stakeholders, but also to achieve a greater good in society (Russo & Perrini, 2010). Many logistic companies - such as TNT - are considering this motivation to join hu-manitarian operations (Gatignon & Van Wassen-hove, 2009), obtaining good coverage from media and, consequently, becoming recognized as a com-pany concerned with the society needs in disaster response, avoiding the threats from their stake-holders, as indicated by Vega and Roussat (2015) and also in Table 1.

According to Godfrey and Hatch (2007), CSR is a concept that includes different ways of acting. There are different kinds of social involvement, that goes from a merely donation to an incorporation of the CSR way of thinking into the company’s business strategy, turning CSR in “a source of opportunity, innovation, and competitive advantage” (Bosch-Ba-dia, Montllor-Serrats, & Tarrazon, 2013).


3. RESEARCH METHODOLOGY

The systematic literature review is conducted based on Seuring and Gold (2012) process, defined on four steps: material collection, descriptive analysis, cat-egory selection, and material evaluation, this last is composed of critical analysis and synthesis from the literature review (Torraco, 2005). The material collection considers ISI Web of Science, Science Di-rect, and Emerald academic databases, which covers journals directly related to disaster and humanitar-ian issues, such as Disasters, Disaster Risk Reduc-tion, and also the Journal of Humanitarian Logistics and Supply Chain Management. These academic da-tabases also covers journals that recently published special issues in Humanitarian Logistics, such as the Production and Operations Management (POM), In-ternational Journal of Production Economics (IJPE), Supply Chain Management: An International Jour-nal (SCMIJ), and other journals of operations and supply chain management.

The research considered four groups of keywords sufficiently broad to “uncover research that has been cast in conceptual frameworks different from their own but which include manipulations or mea-sures relevant to the concepts they have in mind” (Cooper, Hedges, & Valentine, 2009). The structure used to search the academic papers is any of key-words of the each group (OR) concatenated by the boolean operator (AND) among the four groups. In the first group the words: disaster, emergency, crisis, relief and humanitar (humanitarian and variations) are selected in order to restrict the pa-pers on issues related to disaster and humanitarian operations. The others three keywords groups cor-respond the stakeholders’ groups according to the Social-Public-Private Partnership (S3P) Relation-ship Stakeholder Model developed by Fontainha et al. (2015) presented in the Figure 1. The keywords identified by them in others stakeholder models and subsequently used in their work are also se-lected as follows:

Social stakeholders’ group:

• International aid network (United Nations, Red Cross, Red Crescent);

• Donor (donor – excluding organ and transplant and its variations)

• Local aid network (aid network, NGO, non-gov-ernmental, volunteer).

Public stakeholders’ group:

• Military (military);

• Government (public, govern and its variations);

• Regulatory agencies (regulat, i.e. regulatory and its variations – except gene and its variations in order to exclude paper of genetic subject).

Private stakeholders’ group:

• Private sector (private, company, firm, enterprise, industry);

• Direct supplier (supply);

• Media (media).

This procedure leads to 259 peer-reviewed papers selected for title and abstract reading. As an exclu-sion and inclusion criteria for the full reading, we eliminated the duplicated entries and selected only works reporting a direct study of interactions among stakeholders in any stage of a disaster lifecycle (miti-gation, preparation, response, and recovery - Altay & Green, 2006), regardless whether natural or man-made and whether sudden onset or slow onset (Van Wassenhove, 2006). In this sense, papers covering financial crisis and others emergences from solely medical perspective were discarded, resulting in 167 papers for full reading.

In order to filter the papers in which the central per-spective of stakeholders from private group in disas-ter and humanitarian operations are discussed, each of the 167 papers were completely read and classi-fied according to the main stakeholder discussed, taking the stakeholders defined by Fontainha et al. (2015) as a reference. Despite the fact that such clas-sification was performed by only one researcher, it was considered that in a great amount of papers the main stakeholder were clearly and easily identi-fied, taking for another reading in the end the cases in which any doubt aroused. This procedure led to the identification of 27 papers centered in the three stakeholders from private group. Such result rein-forces the gap on papers addressing complex rela-tionships in disaster and humanitarian environment considering the main perspective of stakeholders from private group.

According to the purpose of this research, a descrip-tive analysis is briefly presented and discussed in the next section, considering the distribution of all


papers per journal and per stakeholder from private group. Then, each of the 27 papers is also catego-rized within the three approaches detailed in pre-vious section: one-off relationship, contractual rela-tionship, and CSR partnership.

The material evaluation is based on the presentation of the findings identified on the engagement of stake-holders of private group in disaster and humanitari-an operations, evidencing their involvement in such operations together with at least one stakeholder from the public group and one stakeholder from social group. This evaluation is performed accord-ing to four analytical dimensions: a) the risks and barriers; b) the benefits for stakeholders from pri-vate group; c) the potential negative impacts; and d) the benefits for stakeholders from public and social groups. Then, these findings are critically analyzed in order to present a synthesis on the subject and a

research agenda for the improvement of the engage-ment of stakeholders from private group in disaster and humanitarian operations.

4. PRIVATE STAKEHOLDER’S ROLE IN DISAS-TER OPERATIONS

Figure 2 shows an increase of papers addressing is-sues from the private stakeholder central’s perspec-tive in disaster and humanitarian operations togeth-er with the involvement of stakeholders from the public and social groups after 2008. Despite that, the two publications before 2008 cover only contractual and one-off relationship approaches, what reflects a rising interest of this complex involvement of stake-holders from private group in disaster and humani-tarian operations, especially from a CSR partnership approach in recent publications.

Figure 2. Annual papers distribution per approach of

private stakeholder engagement in disaster/humanitarian operations together with public stakeholders and social stake-holders

The papers selected through the literature review were published mostly in business journals such as Corporate Governance: The international journal of business in society (3 papers), Journal of Man-agement Development and World Journal of En-trepreneurship, Management and Sustainable De-velopment (2 papers each) and others 20 journals with only one paper each, considering the private stakeholder as the main aspect in the analysis of the disaster/humanitarian operations. Yet, two papers

present the Media as the main stakeholder involved in the disaster and humanitarian operations, six pa-pers consider the Direct supplier as the main stake-holder and 19 papers consider the Private sector as the main stakeholder.

The benefits and challenges of each of the three ap-proaches are presented in following subsections, in accordance with the material obtained through the literature review.


4.1. One-off relationship approach

Risk and challenges for the own stakeholders from pri-vate group

Companies orientated towards the one-off relation-ship approach may rely on receiving external aid or support from different stakeholders in order to participate in the economic recovery of regions in humanitarian conflict or be involved in the disaster response (Clayton, K’nIfe, & Spencer, 2012; Dethier & Effenberger, 2012; Sardana & Dasanayaka, 2013). Despite that, corruption may arise in the humanitar-ian and disaster environment and private organiza-tions may face difficult procedures to obtain any aid or/and delay in receiving such aid, as observed after the tsunami that affected Sri Lanka in 2006 (Sardana & Dasanayaka, 2013).

Benefits for the own stakeholders from private group

The one-off relationship approach because of NGOs and community’s pressures may lead companies to the internalization of the sustainability concept, which provides an opportunity to achieve a new lev-el of awareness of their own supply chain (Balkau & Sonnemann, 2010). Moreover, private organiza-tions may act only in specific situations in order to postpone any expenditures or change in the busi-ness structure because of International aid networks pressure and others requests from Government and Regulatory agency (Bauner, 2011). For example, the engagement of pharmaceutical companies in pro-viding medicines for diseases in Africa, as reported by Colatrella (2008), but postponing the answer for the International aid networks pressures over the low investment for the development and supply of vaccines for neglected tropical diseases (NTDs).

Potential negative impact for stakeholders from public and social group

In order to achieve success on answering any pres-sure from others stakeholders considering their own benefit, even in a one-off relationship approach, Deri (2003) explain that private organizations may act consciously in favor of their own interests, regard-less any other stakeholder’s interest or pressure. In this sense, Deri (2003) summarizes seven rules that must be observed by any company in order to an-swer any external pressure: respond consistently as a global brand; be as transparent as possible; do not be forced into a “yes-or-no” public confrontation;

engage multiple partners and perspectives; do not rely solely on industry-wide actions; distinguish be-tween a NGOs’ rhetoric and its actual goals; know when to stand your ground.

Clayton et al. (2012) explain that if private organi-zation does not evolve or improve their business structures even after facing a disaster or humanitar-ian crisis, it leads to such companies to continuously rely on aid received by International and Local aid networks and from Government that could be ap-plied in others actions.

Benefits for stakeholders from public and social group

The pressures faced by private organizations also represent an opportunity for the development of new products in accordance to the stakeholders’ desires (Bauner, 2011). Moreover, any single action from organizations engaging the one-off relation-ship approach are important to the recovery of the local supply chains based on the population em-ployment (Sardana & Dasanayaka, 2013).

4.2. Contractual relationship approach

Risk and challenges for the own stakeholders from private group

The contractual relationship approach in disaster and humanitarian operations represents a risk to stakeholders from private group because they face consequences directly related to the event and also external and internal pressures related to the supply chain irregular operation. In this sense, the region affected may have not completely solved the sources of the disaster or humanitarian crisis and companies may suffer by either a new subsequent disaster or crisis or by an unexpected and complex environ-ment’s change after the company operations begin. In this case, companies may rely on financial sup-port from International aid networks and protection from Military to operate in disaster and humanitar-ian scenarios (Bray & Crockett, 2012).

Dependency from International aid network and Government initiatives to contract or finance stake-holders from private group to operate in disaster an humanitarian operations, low credibility from population to pay their debts, lack of employee training to operate in disaster and humanitarian sce-narios, and local corruption are also problems faced by stakeholders from private group that operate in such scenarios (Nkamnebe & Idemobi, 2011). More-


over, their capacity to provide supplies in disaster and humanitarian scenarios at a stable cost relies partially in the Government and Regulatory agency involvement to aid the supply chain (Smith, 1997).


Private stakeholders that rely on the contractual ap-proach may operate in the disaster response or in the humanitarian environment without any differ-ence, when compared to a regular environment, and still reach benefits as a normal supplier. In this sense, Media organizations increases their consumer ba-sis by providing a global communication structure among International aid networks and potential Do-nors (Cooley & Jones, 2013) and by the use of social media platforms to share information among stake-holders and make faster decisions during disasters (Yates & Paquette, 2011).

Despite the continuity of its operations, stakehold-ers from private group can improve their operations through contractual relationship education in di-saster and humanitarian environment (Ambituuni, Amezaga, & Emeseh, 2014; Kaiser, 2015). Hinrichs (2013), for example, indicates that corporations from a food supply chain in USA have improved their op-erations through the delivery of essential supplies to the population in disaster areas, an effect achieved af-ter some local pressure from Local aid networks and from the engagement with Government and Regula-tory agencies in order to address some issues related to the supply chain costs, subsidies and regulations.


When the engagement of stakeholders from private group is regular and not orientated to any disaster or humanitarian goal, apart from their own market environment goals, the media effect in raising dona-tions may not be enough to meet the need reported by International and Local aid networks as observed during the humanitarian need of Somalia in 2011 (Cooley & Jones, 2013). Despite some benefits ac-quired by other stakeholders from the use of tech-nological communication, it is observed that such technologies may be provided without any special adaptation for the use in disaster and humanitarian environments nor any commitment to the coordina-tion efforts during the disaster response (Yates & Pa-quette, 2011). Moreover, stakeholders from private group can engage with decision makers from Gov-

ernment and Regulatory agencies to obtain business advantages in contractual relationship in disaster and humanitarian environments, while damaging social and human rights as consequence of their op-erations (Spiegel, 2009).


Companies can indirectly contribute to the disaster and humanitarian operations from contractual en-gagements in several manners. In regions in conflict or post disaster, organizations directly contribute to the employment of local affected population and to a consequently economic development, consider-ing the support from International aid network and local Government, if necessary (Bray & Crockett, 2012). Moreover, by providing their products and services to the population, it is possible to enable long-term response and recovery to disaster and hu-manitarian scenarios (Nkamnebe & Idemobi, 2011; Yates & Paquette, 2011).

Since private stakeholder play an important role in how society is organized, Jain (2015) reinforces that Government and Regulatory agencies need to increase efforts to request a major commitment to the minimization of a disaster risk from infrastruc-ture providers. In this sense, Linnerooth-Bayer et al. (2009) explain that the private sector can provide fi-nancial security to the population against the occur-rence of sudden-onset disasters and encourage the adoption of risk mitigation measures for vulnerable population through insurance, partially financed by stakeholders from public and social groups.

From another perspective, private organizations are aware of the need to respect the market regula-tions regarding their own operations (Ambituuni et al., 2014; Kaiser, 2015; Spiegel, 2009). In this sense, companies can take an active position with Regula-tory agency in order to improve regulations related to operations in disaster and humanitarian scenarios for achieve better results to the company and the so-ciety (Ambituuni et al., 2014; Kaiser, 2015).

4.3. CSR partnership approach

Risk and challenges for the own stakeholders from private group

Stakeholders from private group need to be aware on the impacts in their operations and supply chain in consequence of a major support in disaster and humanitarian events. Kolady and Lesser (2008) re-


port that a great concern of stakeholders from pri-vate is to avoid a consequent profit loss after transfer their own technologies, products or services free of charge to the beneficiaries when joining in partner-ships with stakeholders from social group - in some cases it is necessary an intervention from Govern-ment and Regulatory agency to regulate and pre-vent any commercial losses.

Despite the argument of learning through opera-tions in partnership with other stakeholders in disaster and humanitarian operations, Borwan-kar and Velamuri (2009) report the results of one enterprise initiative that is still helping the com-munity without achieving any organizational learning or development besides the individual learning of their employees who have worked in humanitarian projects.

Although the success of several initiatives, some com-panies face and need to overcome inadequate finan-cial and human resources, lack of social, economic and health infrastructures, civil unrest and political conflict, and competitive high-priority health issues when joining efforts with stakeholders from public and social groups to delivery aid to the beneficiaries (Colatrella, 2008). Katamba et al. (2014) also explain that professionals in charge of CSR initiatives in pri-vate organizations consider the corruption that per-meates vulnerable societies as the major barrier to expand the CSR’s initiatives to broader human de-velopment goals, such as Millennium Development Goals set by UN and objectivized by Governments.


In line with the general ST and CSR literature, Ni-jhof, Bruijn, & Honders (2008) reinforce the bene-fits of a company’s engagement in CSR projects in order to mitigate the effects of the business opera-tions, recognizing their responsibility to solve social imbalance problems and prevent environmental impact. Nijhof et al. (2008) further explain that CSR initiatives can be orientated to prevent company reputation damage or develop commercial opportu-nities; strength the organizational identity; or reflect on the organization’s position in society. Some of these motivations are reinforced by Pedersen (2009), who investigated that the companies develop a CSR partnership in humanitarian development as result

of the own companies’ values and their self-interest in responding the International aid network’ and Government’ requests on the humanitarian devel-opment issues.


Tencati, Russo, and Quaglia (2008) explain that global companies need to be aware of the CSR’s un-intended consequences in the supply chain, such as the protectionism - a company’s decision to only work with other organizations that also have CSR and/or that respect specific human rights. Martin (2013) also explains that, although organizational issues rely on the engagement of leaders in a top-down perspective, an intention to support human rights and others structural social and humanitarian issues needs to be addressed from a proactive and holistic perspective, including bottom-up engage-ment and support by other public and social stake-holders. The negative impact generated from such misleading on management can lead to shortage on the supply of deliverables planned for disaster and humanitarian scenarios (Martin, 2013).


Some benefits are observed in the disaster litera-ture, such as the provision of financial and mate-rial resources to support local aid networks and encourage of their employees and suppliers to be volunteers (Borwankar & Velamuri, 2009; Hansen & Spitzeck, 2011; Mele & Mammoser, 2011). As re-ported by Hansen and Spitzeck (2011), CSR partner-ship initiatives and their social impacts can also be intrinsically measured and managed by Local aid networks, ensuring the benefits for humanitarian organizations.

5. DISCUSSION

Stakeholders from private group get involved in disaster and humanitarian operations in different forms, as observed in the three approaches dis-cussed. Table 2 presents the synthesis of their risks, challenges and benefits and also the impact and ben-efits to others stakeholders from public and social groups, as described in previous sections.


Table 2. Synthesis of the three approaches of private sector engagement in disaster and humanitarian op-erations

One - off relationship Contractual relationship CSR Partnership

Risks and challenges for private sector

Dependency on exter-nal aid or support from different stakeholders to participate in the recov-ery and vulnerability to others problems, such as corruption and delay on receiving aid

(Clayton et al., 2012; Dethier & Effenberg-er, 2012; Sardana & Dasanayaka, 2013)

Unstable and damaged environment, which affect the costs and security of operations, employees and other problems, such as corruption

(Bray & Crockett, 2012; Nk-amnebe & Idemobi, 2011; Smith, 1997)

Profit loss and/or low ef-ficient operations when compared to private opera-tions, which may lead to frustration feeling. The occurrence of other envi-ronmental characteristics such as corruption and inadequate resources and infrastructure

(Kolady & Lesser, 2008; Borwankar & Velamuri. 2009; Colatrella, 2008; Kat-amba et al., 2014)

Benefits for private sector

Possibilities of improve-ment on the organiza-tion's position in the supply chain and, at least, continuity of their business structures

(Balkau & Sonnemann, 2010; Bauner, 2011)

Increasing the total of consumers and operations improvement

(Cooley & Jones, 2013; Yates & Paquette, 2011; Am-bituuni et al., 2014; Kaiser, 2015; Hinrichs ,2013)

Prevention of reputation damage, development of commercial opportuni-ties, empowerment of the organizational identity and of the organization’s posi-tion in society (Nijhof et al., 2008; Pedersen, 2009)

Potential negative im-pacts for stakeholders from public and social

groups

Organizations’ actions that do not consider the interests from other stakeholders. Continuous aid resources draining

(Deri, 2003; Clayton et al., 2012)

No special attention for the provision of prod-ucts/services considering the particularities of the disaster and humanitarian environment.

(Cooley & Jones, 2013; Yates & Paquette, 2011; Spiegel, 2009)

The use of a top-down approach regular in pri-vate environment over a bottom-up engagement and a holistic perspective

(Tencati et al., 2008; Martin, 2013)

Benefits for stakehold-ers from public and

social groups

Development of new products/services that meet others stakeholders interests and specific ac-tions that aid the recov-ery of the supply chains affected by a disaster

(Bauner, 2011; Sardana & Dasanayaka, 2013)

Employment of local af-fected population and pro-viding financial security to them and the society, also aiding the recovery by the provision of their products/services

(Bray & Crockett, 2012; Nkamnebe & Idemobi, 2011; Yates & Paquette, 2011; Jain, 2015; Linnerooth-Bayer et al., 2009; Ambituuni et al., 2014; Kaiser, 2015; Spiegel, 2009).

Provision of financial, hu-man and material resources to support local aid net-works

(Borwankar & Velamuri, 2009; Hansen & Spitzeck, 2011; Mele & Mammoser, 2011)

Some challenges are structural and permeate all these three approaches of the private sector engagement


in disaster and humanitarian operations. Corrup-tion appears as a common and chronic problem in which companies are directly affected in the one-off relationship approach (Sardana & Dasanayaka, 2013). In the same way, Spiegel (2009) reports that companies can treat it as an environmental issue or as a market variable in a contractual approach; and, in the CSR partnership approach, it is observed as a structural characteristic in disaster and humanitar-ian environment (Katamba et al., 2014). In this sense, Hsieh (2009) indicates that, when compared to the public stakeholders, private corporations have ad-vantages to deal with the humanitarian assistance and they justify this finding based on three factors: (1) employees of private companies are less prone to corruption, (2) companies are more prepared to redirect their resources in crises, and (3) companies have their own knowledge to solve the problem.

As observed in Table 2, the unpredictability of the results from private organization is higher in the one-off relationship than in the contractual relation-ship. In this sense, it is not possible to identify previ-ously whether or not private organizations actions orientated by the one-off relationship will lead to the improvement of their own operations and/or po-tential negative effects on others stakeholders from public and social groups. On the other hand, it is considered that private organizations actions orien-tated by contractual relationship may not consider any special attention for the disaster and humanitar-ian environmental characteristics. However, at least, their operations do not cause any prior negative ef-fect on stakeholders from public and social groups.

Despite the benefits of mutual learning resulted from an engagement in CSR’s initiatives with di-saster and humanitarian operations, some private stakeholders do not identify this type of return in their organizational learning (Borwankar & Velam-uri, 2009). This argument is difficult to be proven so to convince private stakeholders to change their ap-proach, and, because of an unachieved result in this nature, companies can be frustrated and turn back to a less engaging approach or CSR strategy. This is also a critic point since organizations in contractual relationship approach can achieve some organiza-tional learning (Hinrichs, 2013).

Central differences in characteristics between pri-vate and social stakeholders are also causes for the companies’ frustration when they engage in such initiatives. Since corporations operate and plan in a long-term timeframe, including their CSR initia-

tives, a sustainable supply chain partnership with stakeholders from social group becomes vulner-able due to the high turnover faced by NGOs and other Local and International aid networks. These challenges are clearly evidenced by the pharmaceu-tical initiatives shown by Colatrella (2008) and Bal-aisyte, Besiou, and Wassenhove (2011), but not by other private organizations, such as transportation companies shown by Gatignon and Van Wassen-hove (2008), Gatignon and Van Wassenhove (2009), Stadtler and Van Wassenhove (2012a) and Stadtler and Van Wassenhove (2012b). In these transporta-tion examples from TNT, Agility and UPS in part-nership with Logistic Cluster, World Food Program and World Economic Forum, a truthful engagement in humanitarian logistics is observed because of the direct relation of their primary service and the need from international aid networks. However, such en-gagement has begun in 2002 as a truly altruistic ini-tiative from the company TNT, as reported by Gati-gnon and Van Wassenhove (2009) and later used as an example by the international aid networks to at-tract more similar partners by Stadtler and Van Was-senhove (2012b).

Despite the increased reputation capital achieved as a resulted from initiatives in the CSR partnership approach, it is observed that stakeholders from pri-vate group that consider the contractual and one-off relationship approaches can also obtain some level of reputation capital benefits with a lower engage-ment. For this reason, except in situations in which the companies already altruistically engage in CSR, the main challenge to attract attention and get a real engagement is through a clear indication on how companies can internalize knowledge and improve their operations from the experience with stakehold-ers from social and public groups.

Sharing knowledge/education between business and humanitarian logistics, as stated by Thomas and Kopczak, (2005), Van Wassenhove (2006), Maon et al. (2009) and Tomasini (2011), appears as a vague argument from companies’ perspective since it do not necessary is directly converted in benefit for them. As observed in the research, transport com-panies engagement in disaster and humanitarian operations started the CSR initiatives in a truly al-truistic approach, and then was followed by others companies. For this reason, the success in some ini-tiatives and failures in others, this issue need to be further investigated in order to address the risks and challenges presented in Table 2.


6. CONCLUSIONS AND FINAL REMARKS

Considering the importance of private stakehold-ers, the role of these companies goes far beyond the delivery of profits to its shareholders. They need to address social issues, such as the engagement in di-saster and humanitarian operations. Professionals in disaster environment’s fields have already identi-fied that companies have different levels of engage-ment in such activities (Kapucu, 2006; Nirupama & Etkin, 2012), but these differences have not been deeply debated yet.

The strategy to be adopted by humanitarian logis-tics and other professionals in the disaster environ-ment, with companies that address disaster and humanitarian issues directly or indirectly, has to be addressed differently, according to the approach in which these companies adopt in the relationship with their stakeholders (contractual relationships, one-off relationships and CSR - Corporate Social Responsibility partnerships). Contractual relation-ships comply on traditional commercial agreements to provide goods and services. One-off relationships and CSR - Corporate Social Responsibility partner-ships, on the other hand, are based on the Stakehold-er Theory and the Corporate Social Responsibility. Whereas the first one considers companies that en-gage in single actions in a responsive manner, the latter incorporate the CSR concepts in their business strategies through partnerships. Risks, challenges, and benefits for private sector and the potential negative impacts and benefits for stakeholders from public and social groups are the main factors that drive companies to choose a different approach for the relationship.

Thus, the results synthesized in Table 2 represent an important agenda for future research in the subject, indicating not only the risks and the potential nega-tive effects for others stakeholders that need to be addressed but also the benefits that could be exploit-ed by professionals and academics that work with private organizations in different approaches. In this sense, it is suggested further research on the devel-opment of policies to minimize the risks of corrup-tion that may arise in all three forms of engagement and policies for private stakeholders including the perspective of other stakeholders’ wants and need when engaging in disaster and humanitarian opera-tions - which also permeates the potential negative impact for others stakeholders in all the three forms of engagement. Future research may also consider the perspective other stakeholders in relation to the

stakeholders of the private group. Moreover, as the literature review was limited to academic peer-re-viewed papers, future studies may consider prac-tioner works.

Despite the intuitive impression that private orga-nizations must evolve from one-off to contract re-lationship and then to CSR partnerships, Stewart, Kolluru, & Smith (2009) explain that 85% of critical infrastructure in USA for recovering from a disaster are owned by private sector. This situation reinforc-es the importance of humanitarian logistics consid-ers contractual approach as a greater opportunity to develop partnerships with private sector, and not only focusing on CSR partnership approach.

Lastly, scholars and practitioners in disaster and hu-manitarian field need to be more aware on the com-plexity that permeates the argument of improve-ment on the reputation capital and organizational learning, especially for companies that operate in one-off and contractual relationship approaches. For this reason, it is understood that the benefits from the relationship between business and humanitarian logistics still are a prominent path for improvement that must be better justified and investigated.

ACKNOWLEDGMENTS

The authors acknowledge the support of CNPq (PhD scholarship and Productivity Re-search -311723/2013-6, 141130/2014-8), CAPES (88887091739/2014-01), and FAPERJ (110149/2014; 210325/2014; 203051/2015; 211207/2015).

7. REFERENCES

Abidi, H., Leeuw, S., & de Klumpp, M. (2015). The value of fourth-party logistics services in the humanitarian supply chain. Journal of Humanitarian Logistics and Supply Chain Management, 5(1), 35-60.

Altay, N., & Green III, W. G. (2006). OR/MS research in disaster operations management. European Journal of Operational Research, 175(1), 475-493.

Ambituuni, A., Amezaga, J., & Emeseh, E. (2014). Analysis of safety and environmental regulations for downstream pe-troleum industry operations in Nigeria: Problems and pros-pects. Environmental Development, 9, 43-60.

Balaisyte, J., Besiou, M., & Van Wassenhove, L. N. (2011). Tibotec: Partnership with HIV/AIDS Alliance in Uganda – Working Together for Development. INSEAD case study 04/2011-5742.

Balcik, B., Beamon, B. M., Krejci, C. C., Muramatsu, K. M., &


Ramirez, M. (2010). Coordination in humanitarian relief chains: Practices, challenges and opportunities. International Journal of Production Economics, 126(1), 22-34.

Balkau, F., & Sonnemann, G. (2010). Managing sustainability per-formance through the value-chain. Corporate Governance: The international journal of business in society, 10(1), 46-58.

Bastos, M. A. G., Campos, V. B. G., & Bandeira, R. A. de M. (2014). Logistic Processes in a Post-disaster Relief Operation. Proce-dia - Social and Behavioral Sciences, Transportation: Can we do more with less resources? – 16th Meeting of the Euro Work-ing Group on Transportation – Porto 2013, 111(1), 1175-1184.

Bauner, D. (2011). International private and public reinforcing dependencies for the innovation of automotive emission con-trol systems in Japan and USA. Transportation Research Part a-Policy and Practice, 45(5), 375-388.

Business Civic Leadership Center. (2012). The role of business in disaster response. Retrieved from U.S. Chamber of Com-merce Foundation website, https://www.uschamberfoun-dation.org/promising-practice/role-business-disaster-re-sponse-report.

Binder, A., & Witte, J. M. (2007). Business engagement in hu-manitarian relief: Key trends and policy implications. An HPG Background Paper, Overseas Development Institute, London, pp. 1-52.

Blecken, A. (2010). Supply chain process modelling for humani-tarian organizations. International Journal of Physical Distri-bution & Logistics Management, 40(8-9), 675-692.

Borwankar, A., & Velamuri, S. R. (2009). The potential for man-agement development in NGO-private sector partnerships. Journal of Management Development, 28(4), 326-343.

Bosch-Badia, M. T., Montllor-Serrats, J., & Tarrazon, M. A. (2013). Corporate social responsibility from Friedman to Porter and Kramer. Theoretical Economics Letters, 3, 11-15.

Bray, J., & Crockett, A. (2012). Responsible risk-taking in conflict-affected countries: The need for due diligence and the impor-tance of collective approaches. International Review of the Red Cross, 94(887), 1069-1087.

Caruson, K., & Macmanus, S. A. (2011). Emergency manage-ment: Gauging the extensiveness and quality of public-and private-sector collaboration at the local level. Urban Affairs Review, 47(2), 280-299.

Chatterjee, R., & Shaw, R. (2015). Public private partnership: Emerging role of the private sector in strengthening India’s disaster resilience. In T. Izumi & R. Shaw (Ed.), Disaster Risk Reduction: Methods, Approaches and Practices (pp. 187-212). Japan: Springer.

Clayton, A., K’nIfe, K., & Spencer, A. (2012). Using integrated assessment to develop policy options - trade, land use and biodiversity. World Journal of Entrepreneurship, Manage-ment and Sustainable Development, 8, 170-182.

Colatrella, B. (2008). The Mectizan Donation Program: 20 years of successful collaboration - a retrospective. Annals of Tropi-cal Medicine and Parasitology, 102, 7-11.

Cooley, S., & Jones, A. (2013). A forgotten tweet: Somalia and social

media. Ecquid Novi-African Journalism Studies, 34(1), 68-82.

Cooper, H. M., Hedges, L. V., & Valentine, J. C. (2009). The Hand-book of Research Synthesis and Meta-Analysis. New York, USA: Russel Sage Foundation.

Cozzolino, A. (2012). Humanitarian Logistics: Cross-Sector Coop-eration in Disaster Relief Management. London, UK: Springer.

Crane, A., Palazzo, G., Spence, L. J., & Matten, D. (2014). Contest-ing the Value of “Creating Shared Value”. California Man-agement Review, 56(2), 130-153.

Deri, C. (2003). Make alliances, not war, with crusading external stakeholders. Strategy & Leadership, 31(5), 26-33.

Dethier, J., & Effenberger, A. (2012). Agriculture and develop-ment: A brief review of the literature. Economic Systems, 36(2), 175-205.

Ertem, M. A., & Buyurgan, N. (2011). An auction-based frame-work for resource allocation in disaster relief. Journal of Hu-manitarian Logistics and Supply Chain Management, 1(2), 170-188.

Fombrum, C. J., Gardberg, N. A., & Barnett, M. L. (2000). Op-portunity platforms and safety nets: Corporate citizenship and reputational risk. Business and society Review, 105(1), 85-106.

Fontainha, T. C., Leiras, A., & Bandeira, R. A. de M. (2015). S3P Stake-holder relationship model. 26th Annual POMS Conference - Pro-duction and Operations Management Society, pp. 1-10.

Freeman, R. E. (1984). Strategic management: A stakeholders ap-proach. Massachusetts, USA: Pitman Publishing Inc..

Freeman, R. E., Harrison, J. S., Wicks, A. C., Parmar, B. L., & Colle, S. (2010). Stakeholder theory: The state of art. Cam-bridge University Press, United Kingdom.

Friedman, A. L., & Miles, S. (2002). Developing stakeholder theo-ry. Journal of Management Studies, 39(1), 1-21.

Friedman, A. L., & Miles, S. (2006). Stakeholders: Theory and practice. New York, USA: Oxford University Press.

Fritz Institute. (2012). Humanitarian Supply Chain. Fritz Institute.

Frooman J. (1999). Stakeholder influence strategies. Academy of Management Review, 24, 191-205.

Fudalinski, J., & Pawlak, K. (2012). Conditions of shaping the procurement function in the process of development of the sector of non-profit organizations. In: Dolhasz, M. (Ed.). Contemporary dilemmas of management. Krakow Society for Education, Krakow, pp. 57-79.

Gatignon, A., & Van Wassenhove, L. N. (2008). Paving the Road to Healthy Highways - A Partnership to Scale Up HIV/AIDS Clinics in Africa Safety in Numbers: Reducing Road Risk with Danida’s Multi-sector Partnership. INSEAD case study 1003/20089-55823.

Gatignon, A., & Van Wassenhove, L. N. (2009). When the music changes, so does the dance: The TNT/WFP partnership, ‘mov-ing the world’ five years on. INSEAD case study 02/2010-5596.


Godfrey, P. C., & Hatch, N. W. (2007). Researching corporate so-cial responsibility: An agenda for the 21st century. Journal of Business Ethics, 70(1), 87-98

Guidry, P. E., Vaughn, D., Anderson, R. P., & Flores, J. (2015). Business continuity and disaster management: Mitigating the socioeconomic impacts of facility downtime after a disas-ter. IEEE Industry Applications Magazine, 21(5), 68-77.

Hansen, E. G., & Spitzeck, H. (2011). Measuring the impacts of NGO partnerships: The corporate and societal benefits of community involvement. Corporate Governance: The inter-national journal of business in society, 11(4), 415-426.

Hellingrath, B., Link, D., & Widera, A. (2013). Managing hu-manitarian supply chains: Strategies, practices and research. Hamburg-Germany: DVV Media Group.

Herbane, B. (2015). Threat orientation in small and medium-sized enterprises: Understanding differences toward acute interruptions. Disaster Prevention and Management: An In-ternational Journal, 24(5), 583-595.

Herlin, H., & Pazirandeh, A. (2012). Nonprofit organizations shaping the market of supplies. International Journal of Pro-duction Economics, 139, 411-421.

Hinrichs, C. C. (2013). Regionalizing food security? Imperatives, intersections and contestations in a post-9/11 world. Journal of Rural Studies, 29, 7-18.

Hsieh, N. (2009). Corporate social responsibility and the priority of shareholders. Journal of Business Ethics, 88, 553-560.

Inauen, M., Olivares, M., & Schenker-Wicki, A. (2010). Unmas-tered risks: From crisis to catastrophe: An economic and man-agement insight. Journal of Business Research, 63, 337-346.

Ingirige, B., & Wedawatta, G. (2014). Putting policy initiatives into practice: Adopting an “honest broker” approach to adapting small businesses against flooding. Structural Sur-vey, 32(2), 123-139.

Jain, G. (2015). The role of private sector for reducing disaster risk in large scale infrastructure and real estate development: Case of Delhi. International Journal of Disaster Risk Reduc-tion, 14(3), 238-255.

Kaiser, M. J. (2015). Hurricane clean-up activity in the Gulf of Mexico, 2004-2013. Marine Policy, 51, 512-526.

Kapucu, N. (2006). Interagency communication networks during emergencies: Boundary spanners in multiagency coordination. American Review of Public Administration, 36(2), 207-225.

Katamba, D., Nkiko, C. M., Tushabomwe-Kazooba, C., Mpisi, S. B., Kemeza, I., & Wickert, C. M. J. (2014). Integrating corpo-rate social responsibility into efforts to realize millennium development goals. World Journal of Entrepreneurship, Management and Sustainable Development, 10(4), 314-333.

Kolady, D., & Lesser, W. (2008). Can owners afford humanitarian donations in agbiotech - The case of genetically engineered egg-plant in India. Electronic Journal of Biotechnology, 11(2), 1-8.

Koliba, C. J., Mills, R. M., & Zia, A. (2011). Accountability in governance networks: An assessment of public, private, and nonprofit emergency management practices following hur-

ricane Katrina. Public Administration Review, 71, 210-220.

Linnerooth-Bayer, J., Warner, K., Bals, C., Hoeppe, P., Burton, I., Loster, T.,et al. (2009). Insurance, developing countries and climate change. Geneva Papers on Risk and Insurance-Issues and Practice, 34(3), 381-400.

Logistic Cluster (2014). Syria crisis, warehouse assessment (Erbil, North Iraq). Retrieved from Relief Web website, http://relief-web.int/sites/reliefweb.int/files/resources/Logistics%20Clus-ter_SYRIA_Erbil%20Warehousing%20Assessment_14010.pdf

Maether, J. (2010). The role of global logistics companies in di-saster relief efforts: An investigation into benefits, challenges and critical success factors of cross-sectors collaboration. Thesis presented in partial fulfillment of the requirements or the degree of Master of Logistics and Supply Chain Manage-ment, Massey University, Albany.

Mankin, L. D., & Perry, R. W. (2005). Preparing for the Unthink-able: Managers, Terrorism and the HRM Function. Public Personnel Management, 34(2), 175-193.

Maon, F., Lindgreen, A., & Vanhamme, J. (2009). Developing sup-ply chains in disaster relief operations through cross-sector socially oriented collaborations: a theoretical model. Supply Chain Management: An International Journal, 14(2), 149-164.

Martin, J. (2013). Business and Human Rights: What’s the Board Got to Do with It? University of Illinois Law Review, 3, 959-999.

Mele, D., & Mammoser, T. L. (2011). Humanistic corporate com-munity involvement: Walgreens/MEF one-on-one program. Journal of Management Development, 30(6), 582-593.

Menz, K. M. (2012). Corporate social responsibility: Is it reward-ed by the corporate bond market? A critical note. Journal of Business Ethics, 96, 117-134.

Nijhof, A., Bruijn, T. de, & Honders, H. (2008). Partnerships for corpo-rate social responsibility. Management Decision, 46(1), 152-167.

Nirupama, N., & Etkin, D. (2012). Institutional perception and support in emergency management in Ontario, Canada. Di-saster Prevention and Management, 21(5), 599-607.

Nkamnebe, A. D., & Idemobi, E. I. (2011). Recovering of micro cred-it in Nigeria. Management Research Review, 34(2), 236-247.

Office for the Coordination of Humanitarian Affairs (2016). Weekly Humanitarian Bulletin, Ethiopia. Retrieved from Re-lief Web website, http://reliefweb.int/sites/reliefweb.int/files/resources/humanitarian_bulletin_28_march_2016.pdf.

Pedersen, E (2009). The many and the few: Rounding up the SMEs that manage CSR in the supply chain. Supply Chain Management: An International Journal, 14(2), 109-116.

Perrini, F., & Russo, A. A. (2010). Investigating stakeholder theo-ry and social capital: CSR in large firms and SMEs. Journal of Business Ethics, 91, 207-221.

Petrick, J. A., & Quinn, J. F. (2000). The integrity capacity con-struct and moral progress in business. Journal of Business Ethics, 23, 3-18.

Podnar, K., Tuškej, U., & Golob, U. (2012). Mapping semantic meaning of corporate reputation in global economic crisis


context: A Slovenian study. Public Relations Review, 38(5), 906-915.

Rowley, T. J. (1997). Moving beyond dyadic ties: A network theory of stakeholder influences. Academy of Management Review, 22(4), 887-910.

Rowley, T. J., & Moldoveanu, M. (2003). When will stakeholder groups act? An interest and identity based model of stake-holder group mobilization. Academy of Management Re-view, 28(2), 204-219.

Sardana, G. D., & Dasanayaka, W. S. B. (2013). Economic recov-ery from natural disaster. Competitiveness Review, 23(4/5), 384-397.

Sarmiento, J. P., Hoberman, G., Ilcheva, M., Asgary, A., Majano, A. M., Poggione, S., & Duran, L. R. (2015). Private sector and disaster risk reduction: The Cases of Bogota, Miami, Kings-ton, San Jose, Santiago, and Vancouver. International Journal of Disaster Risk Reduction, 14(3), 225-237.

Seuring, S., & Gold, S. (2012). Conducting content-analysis based literature reviews in supply chain management. Supply Chain Management: An International Journal, 7(5), 544-555.

Smith, L. D. (1997). Price stabilization, liberalization and food se-curity: Conflicts and resolutions? Food Policy, 22(5), 379-392.

Spiegel, S. J. (2009). Labour challenges and mercury manage-ment at gold mills in Zimbabwe: Examining production pro-cesses and proposals for change. Natural Resources Forum, 33(3), 221-232.

Stadtler, L., & Van Wassenhove, L. N. (2012a). Building on les-sons learnt: Disaster relief operations at agility. INSEAD case study 06/2012-5821.

Stadtler, L., & Van Wassenhove, L. N. (2012b). The logistics emer-gency teams: Pioneering a new partnership model. INSEAD case study 10/2012-5895.

Stewart, G. T., Kolluru, R., & Smith, M. (2009). Leveraging pub-lic-private partnerships to improve community resilience in times of disaster. International Journal of Physical Distribu-tion & Logistics Management, 39(5), 343-364.

Taupiac, C. (2001) Humanitarian and development procurement: A vast and growing market. International Trade Forum, 4, 7-11.

Tencati, A., Russo, A., & Quaglia, V. (2008). Unintended con-sequences of CSR: Protectionism and collateral damage in global supply chains: The case of Vietnam. Corporate Gover-nance: The international journal of business in society, 8(4), 518-531.

Thomas, A. (2003). Humanitarian Logistics: Enabling Disaster Response. Fritz Institute

Thomas, A., & Kopczak, L. (2005). From logistics to supply chain management: The path forward in the humanitarian sector. San Francisco, USA: Fritz Institute,

Tomasini R. M. (2011). Helping to learn? Learning opportunities for seconded corporate managers. Journal of Global Respon-sibility, 2(1), 46-59.

Tomasini, R. M., & Van Wassenhove, L. N. (2009a). From pre-paredness to partnerships: Case study research on humani-tarian logistics. International Transactions in Operational Research, 16(5), 549-559.

Tomasini, R., & Van Wassenhove, L. (2009b). Humanitarian Lo-gistics. 1 edition. Houndmills, Basingstoke; New York, USA: Palgrave Macmillan.

Torraco, R. J. (2005). Writing integrative literature reviews: Guidelines and examples. Human Resource Development Review, 4(3), 356-367.

Van Aken, J. E., Berends, H., & Van Der Bij, H. (2007). Problem solving in organizations: A methodological handbook for business students. Cambridge UK: Cambridge University Press.

Van Niekerk, D., Ndlovu, E., & Chipangura, P. (2015). Experi-ences of Africa: Status and potentials. In T. Izumi & R. Shaw (Ed.), Disaster risk reduction: Methods, approaches and practices (pp. 83-104). Japan: Springer.

Van Wassenhove, L. N. (2006). Humanitarian aid logistics: Sup-ply chain management in high gear. Journal of the Opera-tional Research Society, 57(5), 475-489.

Vega, D., & Roussat, C. (2015). Humanitarian logistics: The role of logistics service providers. International Journal of Physi-cal Distribution & Logistics Management, 45(4), 352-375.

Wikström, P. (2010). Sustainability and organizational activities: Three approaches. Sustainable Development, 18, 99-107.

Worden, S. (2003). The role of integrity as a mediator in strategic leadership: A recipe for reputational capital. Journal of Busi-ness Ethics, 46, 31-44.

Yates, D., & Paquette, S. (2011). Emergency knowledge manage-ment and social media technologies: A case study of the 2010 Haitian earthquake. International Journal of Information Management, 31(1), 6-13.

Yoshida, K., & Deyle, R. E. (2005). Determinants of small busi-ness hazard mitigation. Natural Hazards Review, 6(1), 1-12.


The Leadership Process During an Organizational Crisis

Rodrigo Antônio Silveira dos Santos Professor at Universidade da Força Aérea – Rio de Janeiro – RJ, Brazil

[email protected]

Rodrigo Bandeira-de-Mello Professor at Fundação Getulio Vargas, Escola de Administração de Empresas de São Paulo – São Paulo – SP, Brazil

[email protected]

Cristiano José Castro de Almeida Cunha Professor at Universidade Federal de Santa Catarina – Florianópolis – SC, Brazil

[email protected]

ABSTRACT: This article reports results of a qualitative study that examined the leadership process during an organizational crisis in the Brazilian electrical sector. The studied organization is a company involved with the generation and distribution of electric energy, which faced a crisis because of the rupture of electricity-distribution cables that affected the energy supply chain for a whole city, during approximately 52 hours. In this context, the authors analyzed the crisis’ stages and the organizational crisis management phases, in order to identify the leadership tasks adopted by organizational lead-ers during the crisis response. The major challenges brought with the crisis were identified and it was analyzed the leadership tasks used to address challenges: sensemaking, decision making, meaning making, terminating and learning.

Keywords: Crisis leadership, crisis management, crisis response, critical infrastructure breakdown, or-ganizational leadership.


94

Santos, R. A. S., Mello, R.B., Cunha, C. J. C. A.: The Leadership Process During an Organizational CrisisISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 1 p 94 – 10995

1. INTRODUCTION

During the past years, many scholars have conduct-ed conceptual and empirical studies on the topic of organizational crises (Boin et al., 2005; Boin et al., 2010; Hale, Dulek, & Hale, 2005; Hermann, 1963; Lagadec, 2009; Pearson & Clair, 1998; Pearson & Mitroff, 1993; Shrivastava, 1987; Smart & Vertinsky, 1977; Weick, 1988; Weisæth, Knudsen Jr, & Tønnes-sen, 2002). Firstly, Hermann (1963) identified that the occurrence of crises is frequent in the organizational quotidian, making possible the research of an im-portant means of change in organizations: the crisis itself. Understandably, specialized research present-ed different definitions and types of organizational crisis (Mitroff, 2004; Pauchant & Mitroff, 1992). One definition affirms that “an organizational crisis is a low-probability, high-impact event that threatens the viability of the organization and is characterized by ambiguity of cause, effect and means of resolu-tion” (Pearson & Clair, 1998). These unique features showed the importance of being prepared for orga-nizational crises and introduced the study of crisis management (Fink, 1986). It can be defined as “a systematic attempt by organizational members with external stakeholders to avert crises or to effectively manage those that do occur” (Pearson & Clair, 1998).

Recent studies proved that a crisis can strike a com-pany that is not prepared for the constraints brought with it (Hart & Boin, 2001; Barton et al., 2015; Bazer-man & Watkins, 2004; Boin & Gralepois, 2006; Boin & Rhinard, 2008; Kovoor-Misra, Zammuto, & Mitroff, 2000). As pointed by Mitroff (2004), a crisis in one lo-cale can swiftly escalate into a crisis for an entire or-ganization, justifying the need for appropriate struc-tures to focus on crisis management. Then, people in relevant corporate roles should be concerned with the prevention, response and recovery of crises. This reality corroborates with Smart and Vertinsky (1977). These authors suggest that key decisions in crises are often made by a small, tightly knit group of individu-als. Besides, recent studies concluded that crisis and leadership are closely intertwined phenomena (Boin & Hart, 2003; Boin et al., 2005; Hannah et al., 2009; Mi-troff, 2004). People in organizations experience crises as episodes of threat and uncertainty, that requires urgent action (Rosenthal, Boin, & Comfort, 2001). In such distress, it is a natural inclination to look to leaders to “do something” and solve all the problems while the organization is stretched to its limits.

The literature about crisis and leadership suggests that times of crisis may significantly affect the rela-

tionship between leaders and followers (Halverson, Murphy, & Riggio, 2004; Hannah et al., 2009; Hunt, Boal, & Dodge, 1999; Pillai, 1996). Probably, the changes in this relationship is related to the fact that a crisis involves something new, which demands an ability to learn, as previously learned experience may come up short when the ordinary steady state of the organization is disrupted (Moynihan, 2009; Weisæth et al., 2002). Leadership researchers call this situation as the “disequilibrium dynamics”, when the current knowledge owned by the organization cannot solve the crisis’ constraints. In this way, or-ganizational actors may mobilize to produce a new solution and promote the necessary adaptation for the company (Heifetz & Linsky, 2002; Heifetz, 1994; Heifetz, Grashow, & Linsky, 2009).

Besides, the leadership challenge of mobilizing peo-ple during a crisis becomes even more difficult be-cause the crisis stretches the company to the limits, while concepts of uncertainty and risk are very pres-ent. Despite this situation, little attention has been paid in the literature to leadership processes during organizational response to crises and extreme con-texts (Hannah et al., 2009; Silveira-dos-Santos, 2012). Mobilizing people during a crisis involve a lot of questions that are not being asked on the researches about crises. Although many papers focus attention on charismatic leadership in crisis situations (Beyer & Browning, 1999; Bligh, Kohles, & Meindl, 2004; Halverson et al., 2004; Pillai, 1996), the researchers do not pay particular attention to the leadership pro-cess itself. While there are many approaches to crisis preparedness and the leaders’ charismatic behav-ior (Fink, 1986; Halverson et al., 2004; Mitroff, 2004; Pauchant & Mitroff, 1992), there are few researches on how the leadership processes are developed. In fact, leadership processes required to react a crisis involve key aspects that are not taken into account, such as technical, organizational, cognitive and tem-poral factors.

It is very important to understand the ability of or-ganizational systems to maintain the desired levels of work when a crisis arrives. Will the organizational structure and process remain the same during the cri-sis? Are the decisions taken with the needed speed? It is also important to understand how organizational leaders understand the crisis and how they commu-nicate these meanings to all stakeholders. As the crisis involves new circumstances, a sensemaking process occurs (Weick, 1988; Weick, 1995). So, it is important to investigate the leadership capabilities to manage


sensemaking processes. In the same way, the role and perception of time in organizations under extreme circumstances can change completely.

Driving the present study is the absence of pub-lished research examining the questions above. As a first step into a better understanding of crisis leader-ship processes, this study tries to focus on the lead-ership challenges presented during organizational crisis response. It is at the response stage of a crisis that its characteristics of short decision time, com-plexity and ambiguity surface (Bouillette & Quaran-telli, 1971). Because of that, it is at the response stage that the leadership challenges are better represent-ed. Nevertheless, empirical studies about leadership processes during extreme contexts are rare (Hannah et al., 2009; Silveira-dos-Santos, 2012). This paper’s aim, then, is to analyze an organizational crisis in the Brazilian Electrical Sector, focusing the leader-ship challenges and identifying the crisis leadership tasks used to address each challenge.

With more than 8.5 million square kilometers and a great hydrographic basin, Brazil has one of the larg-est energetic potential in the world. The installed ca-pacity of the Brazilian energy matrix reached more than 141.680 MW on january 2016 (Brasil, 2016). These numbers demonstrate that the Brazilian Elec-trical Sector is a large industry in the country, re-flecting an important and strategic sector for the Brazilian Economy.

This study, then, is structured in four major sections. Firstly, the main theoretical background in organi-zational crisis, crisis management and crisis leader-ship are presented. After that, it is shown the meth-odological assumptions that guided the research. Then, the main findings are presented, followed by a theoretical discussion to present the main contri-butions of this study.

2. THEORETICAL BACKGROUND

Crises come in many shapes and forms. Human conflicts, man-made accidents, economic problems or natural disasters shatter the natural order of so-cieties and all of these events could be defined as crises. Fink (1986) affirms that a crisis is an unstable time or state of affairs in which a decisive change is impending, either one with the distinct possibil-ity of a highly undesirable outcome, or one that can result with an extremely positive outcome. Never-theless, the negative connotation of the word crisis often prevails and when a crisis occurs, people auto-

matically think that it arrives as a barrage of urgent, unexpected and unpleasant events, allowing little time to organize or plan appropriate responses, and making people and organizations to operate at their extreme. In this context, the organizational leaders’ behaviour and decisions will be decisive to the re-sults achieved after the crisis period. At this section, the subjects of organizational crisis, crisis manage-ment and crisis leadership will be explored.

2.1 Organizational Crisis

Any crisis that affects one or more organizations could be called an organizational crisis. For Pear-son and Clair (1998), an organizational crisis is “a low-probability, high-impact event that threatens the viability of the organization and is characterized by ambiguity of cause, effect and means of resolu-tion, as well as by a belief that decisions must be made swiftly”. This is a wide-ranging definition, which covers some common elements that are pres-ent in different kinds of organizational crisis, like breakdown of key equipment, major plant disrup-tion, product tampering, decline in major earnings, hostage taking, terrorism, natural disasters or other kinds of organizational crises.

Specifically, previous research has proved that or-ganizational crises: (1) are highly ambiguous situa-tions where causes and effects are unknown (Boin et al., 2005; Pearson, Roux-Dufort, & Clair, 2007; Quar-antelli, 1988), creating a sensemaking process that is carried out while the crisis unrolls (Laere, 2013; Patriotta & Gruber, 2015; Weick, 1988); (2) have a low probability of occurring, although pose a major threat to the survival of an organization (Bazerman & Watkins, 2004; Roux-Dufort & Lalonde, 2013) and to organizational stakeholders (Alpaslan, Green, & Mitroff, 2009); (3) offer temporal constraints, giving little time for the leaders to make decisions and re-spond to the crises (Boin & Smith, 2006; Shaw et al., 2007); (4) disrupt the organizational status quo, pre-senting a dilemma in need of decision that will result in change for better or worse (Fink, 1986; Sommer & Pearson, 2007); (5) change the existing relationships between leaders and followers, as the followers be-come more easily influenced by their leaders under the crisis stress (Halverson et al., 2004).

This is, of course, an academic shortcut on the way toward understanding organizational crisis. Boin et al. (2005) show that, in real life, it is not always clear when exactly organizations experience a situation


in terms of crisis. Some situations seem crystal clear and others are clearly debatable. In this way, the def-inition of a situation in terms of organizational cri-sis is the outcome of a political process. Crises, then, are the result of multiple events, which interact over time to produce a threat with devastating potential. But this result will only be considered a crisis if or-ganizational leaders and/or stakeholders perceive the threat and impute “meaning” to the unfolding crisis. Of course, the earlier one situation is identi-fied and considered as a crisis, the higher are the chances to prevent the crisis threats (Mitroff, 2004).

a. Distinct phases of a crisis

If it is possible to draft a time continuum for a crisis, it would have, at least, three major phases: the incuba-tion period (Turner, 1976), the critical period (Stein, 2004) and the aftermath (Boin, McConnell, & Hart, 2008). First of all, the incubation period, which can be also referred as the precrisis stage (Shrivastava, 1987) or the prodomal crisis stage (Fink, 1986), cor-responds to the period of time where the organiza-tion is on its steady state and no danger or threats are identified. It corresponds to the organization’s ordinary state, with the normal structure and cur-rent activities running on. Fink (1986) affirms that the prodromal stage is the warning stage, when the leaders should improve the organizational abilities to identify any kind of signal that can demonstrate the escalation of a crisis. Mitroff (2004) calls these abilities as “Signal Detection” and Weick & Sutcliffe (2001) call it “Mindfulness”. In this way, Fink (1986) says that it is easier to manage a crisis in the pro-dromal stage, because if the organization is able to identify and act on the crisis escalation signals, the leaders have the opportunity to avert the crisis. It is also important to remember that if the leaders rec-ognize these signals but are unable to dispose of it for whatever reason, just having a sense of what is about to happen will help the organization to pre-pare for the critical period.

The critical period begins with the “precipitating event” (Turner, 1976) or “triggering event” (Shriv-astava, 1987; Weick, 1988) that leads to the crisis. The triggering event marks the turning point (Fink, 1986) and represents the onset of a qualitatively dif-ferent period. Whereas the incubation period gener-ally occurs over a lengthy period of months, years or even decades, the critical period is usually the much briefer time of the minutes, hours or days of the crisis itself. Fink (1986) call this phase as the acute

crisis stage and it is usually the stage which most people have in mind when they speak of a crisis. If the prodromal phase alerts to the fact that a hot spot is brewing, the acute crisis phase tells that the worst has erupted. It is in this phase that the nega-tive aspects of the crisis appears, all at once: (1) the information flows faster and intermittently (Boin et al., 2010; Smart & Vertinsky, 1977); (2) the options of communication channels reduce (Hale et al., 2005; Wester, 2009); (3) all the stakeholders became in-volved (Acquier, Gang, & Szpirglas, 2008; Alpaslan et al., 2009; Pearson & Mitroff, 1993); (4) time is limit-ed (Boin et al., 2010; Hannah et al., 2009); (5) decision making must be quick and effective (Santella, Stein-berg, & Parks, 2009; Sommer & Pearson, 2007). One of the major difficulties in managing a crisis during this phase is the speed and intensity in which a se-ries of constraints appear, leading the organization to the aftermath period.

Also known as the chronic crisis stage (Fink, 1986), the aftermath is a period of recovery, where the organization tries to respond to the constraints presented in the earlier stage. The chronic stage can linger indefinitely and it ends when the crisis is resolved. When the aftermath is over, organiza-tions reached their new ordinary state, which can be equal or different to the steady state that prior to the crisis. Some authors say that the crisis cycle begins again and the organization reaches a new prodro-mal stage, for future crises (Chekkar-Mansouri & Onnee, 2013; Elliott, 2009; Fink, 1986).

b. Crisis Management

Crises can happen in any kind of organizations and every crisis will cross the stages presented above. According to Fink (1986), sometimes all phases may occur within a very short space of time. At other times, there is an extended, long-fused prodrome stage. However, it is very important to identify the crisis signals in the incubation period, trying to avoid the occurrence of the triggering event or, at least, to prepare the organization for the critical period. It is important to remember that a crisis, like other orga-nizational events, is a fluid, unstable, dynamic situ-ation and the recurrent happenings are in a state of constant flux (Shepherd & Sutcliffe, 2015; Weick & Sutcliffe, 2001). In this context, the operative word is recognize. An organization must recognize any kind of evidence that can point to an unrolling crisis, in order to intervene proactively (Fink, 1986).


That is the reason why Mitroff (2004) affirms that signal detection is at the heart of crisis manage-ment. According to this author, all crises send out a trail of early warning signals. If these signals are picked up and acted upon, then a crisis can be pre-vented in the precrisis stage, preserving the organi-zation and the stakeholders. According to Mitroff (2004), early signal detection is vital because crises expand quickly. In the same idea, Weick and Sut-cliffe (2001) demonstrates that the secret under the high rates of success of High Reliable Organiza-tions (HROs) is their capacity to act mindfully, what means that HROs strive to maintain an underlying style of mental functioning that is distinguished by continuous updating and deepening of increasing-ly plausible interpretations of what the organiza-tional context is, what problems define it, and what remedies it contains.

These are the same practices recommended by cri-sis management researchers (Boin et al., 2005; Fink, 1986; Pauchant & Mitroff, 1992; Pearson et al., 2007; Roe & Schulman, 2008). According to Pearson & Clair (1998), organizational crisis management is a systematic attempt by organizational members with external stakeholders to avert crises or to effectively manage those that do occur. Crisis management con-sists of three distinct phases: crisis prevention, crisis response and recovery from the crisis (Fink, 1986). The crisis prevention occurs in the prodromal stage of the crisis, when the organization tries to iden-tify crisis signals and act upon them with the aim to avert the crisis occurrence. The response stage is entered when avoidance efforts fail and events trig-ger a crisis. At this point, organizations shift their resources and efforts to minimizing damage to the environment, the organization and the stakeholders. Then, the recovery stage involves attempts to learn from the event and implement the changes needed.

Traditionally, crisis management involves manage-ment at staff level in a situation characterized by a critical period of time, in which leadership decisions will, for better or worse, determine the future of the organization (Barton et al., 2015; Weisæth et al., 2002). In this way, organizational leaders have a spe-cial responsibility to help safeguard the organization and its stakeholders from the adverse consequences of a crisis. Leaders who take this responsibility seri-ously would have to concern with all crisis’ phases and with all crisis management’s stages (Boin et al., 2005), as will be commented in the next section.

c. Crisis Leadership

Northouse (2007) affirms that leadership is a process whereby an individual influences a group of people to achieve a common goal. By this definition, it is implied that leadership is a process where a leader affects and is affected by followers. It emphasizes that leadership is not a linear, one-way event, but rather an interactive event, which would not happen without influence. Following the same ideas, Yukl (2006) says that leadership is the process of influ-encing others to understand and agree about what needs to be done and how to do it, and the process of facilitating individual and collective efforts to ac-complish shared objectives. After defining the term leadership, it is important to say that this paper will adopt the premise that any leadership attempt dur-ing a crisis, in order to implement crisis manage-ment practices, can be called crisis leadership.

According to Mitroff (2004), what characterizes cri-sis leadership is the continuous responsibility to influence individuals in order to manage four key factors during all stages of a crisis. These factors are: (1) crisis types; (2) crisis mechanisms; (3) crisis sys-tems; and (4) crisis stakeholders. For crisis types, it is understood the particular set of crises that an or-ganization chooses to prepare. Then, crisis mecha-nisms include early warning signals detection, dam-age control systems and business recovery systems. The crisis systems covers the mechanisms by which a crisis unrolls and the crisis stakeholders are all of the various parties, institutions, and even societies, that affect and are affected by a major crisis. In this way, what differentiates crisis leadership from cri-sis management is that the first recognizes the need to manage these four factors before, during and after a crisis, addressing these factors by the adoption of the crisis management practices (Mitroff, 2004).

Boin et al. (2005) define crisis leadership as the set of strategic tasks that encompasses all activities as-sociated with the stages of crisis management. These authors defend that crisis leadership involves five critical tasks: sensemaking, decision making, mean-ing making, terminating and learning.

Sensemaking means that organizational leaders must recognize from vague, ambivalent, and contra-dictory signals that something out of the ordinary is developing. The critical nature of these develop-ments is not self-evident and the leaders have to “make sense” of them (Boin et al., 2005; Patriotta & Gruber, 2015; Weick, 1988). In other words, this first


task means that leaders must appraise the threat and decide what the crisis is about. The second task of crisis leadership is decision making because crises bring various pressing issues to be addressed. In crisis circumstances, the situation remains unclear and volatile, shortening the time to think, consult and gain acceptance for decisions. In this way, crises force organizations to confront issues they do not face of a daily basis, involving tough value tradeoffs and presenting a challenge for leadership (Heifetz, 1994; Heifetz et al., 2009). The next task is related to meaning making because a crisis generates a strong demand from stakeholders to know what is go-ing on and to ascertain what they can do to protect their interests. In this context, leaders are expected to reduce uncertainty and provide an authoritative account of what is going on, why it is happening, and what needs to be done (Boin et al., 2005; Mait-lis & Christianson, 2014; Maitlis, 2005). This means that, after the two previous tasks, when leaders have made sense of the events, arrived at some sort of situational appraisal and made some choices for action, they must get others to accept their definition of the situation, imputing “meaning” to the unfold-ing crisis in such a way that their efforts to manage it are enhanced.

The first three tasks of crisis leadership are related to understanding and acting upon crisis constraints. After that, the next two tasks are related to finish-ing the crisis and learning with it. In this way, the fourth task is terminating the crisis. According to Boin et al. (2005), a sense of normalcy will have to return sooner or later. So, it is a leadership task to make sure that this happens in a timely and expedi-ent fashion. Crisis termination is two-fold because it is about shifting back from emergency to routine; and it requires some form of downsizing of crisis operations at the same time of rendering account for what has happened and gaining acceptance for this account (Fink, 1986). When this process is com-pleted, the crisis has terminated and the ordinary state of the organization is back. After that, it is time for the fifth task, learning something with the crisis and making organizational lesson drawing. Of course, the crisis experience offers a reservoir of potential lessons for contingency planning and training for future crisis. In this way, as a crisis situ-ation involves something new, it demands an abil-ity to learn during and after the crisis as the previ-ous learned experience may come up short (Boin et al., 2005; Deverell, Hansén, & Management, 2009; Elliott & Smith, 2007; Moynihan, 2009).

3. METHODOLOGICAL APPROACH

As an initial investigation of leadership processes during an organizational crisis, this study was de-signed to discover and organize concepts. A qualita-tive research approach immersed the researchers in the data and encouraged their objectivity and open-ness to new findings. The qualitative research is an effort to understand situations in their uniqueness as part of a particular context and its interactions. This understanding demonstrates that this kind of research does not attempt to predict what may hap-pen in the future. Although, it aims to understand the nature of the studied phenomenon and its set-tings – what it means for participants to be in that setting, what their meanings are, etc. (Merriam, 1998). The same author explains that the qualitative research assumes that meaning is embedded in peo-ple’s experience and that this meaning is mediated through the investigator’s own perceptions.

In such a way, the researcher is the primary instru-ment for data collection and analysis, which creates the demand for the investigator to physically go to the organization in study (the fieldwork) and inter-view its stakeholders (Merriam, 1998). So, the meth-odological procedures of this research range the selection of an organization, interviewing its stake-holders and the analysis of the interview’s transcrip-tions, as follows.

a. Sample

In order to reach the research’s aims, the Brazilian Electrical Sector was selected because of its turbu-lent context in the recent years, caused by several organizational crises, such as lack of energy for the industry demands, insufficient raining to mobilize the hydroelectric power stations and predictions of an electrical collapse in the recent future. In this context, it was selected a company involved with the generation and distribution of energy that has recently faced organizational crises. As the research was mainly conducted in the Brazilian state of Santa Catarina, and there is only one company allowed to distribute energy at that state, the company CE-LESC S.A. was chosen to be studied. It is important to say that the company was formally consulted and agreed with this research.

After identifying the organization and with its ap-proval for this study, the researchers listed the re-cent crises in which the company was engaged. The criteria for chosing a crisis episode for study


were the following: (a) a crisis that reached at least 50.000 consumer units; (b) was solved after at least 48 hours; and (c) happened during the last 15 years, in order to make possible contacts with people that was directly involved in crisis response activities. As a result, 5 crises with big proportions were listed and one crisis was selected.

The chosen crisis was a blackout in the city of Flo-rianópolis, capital of Santa Catarina state. The rup-ture of one energy transmission line inside a bridge caused a huge power outage and the electricity sup-ply was interrupted for the whole city, wich is an island, affecting more than 135.000 consumer units, for more than 55 hours. This crisis was unique be-cause it affected an important city, capital of Santa Catarina state, for more than two days, bringing great constraints inside the company and for the whole community. It was the first time during the last 50 years that the city ran out of energy for more than 48 hours uninterruptedly.

The other 4 crises that were identified happened in smaller cities, which were not a state capital. Besides that, an important difference between them must be commented. The other 4 crises were related to nat-ural phenomena, such as hurricanes, floodings or waterloggings. Because of that, the organization al-ready had a protocol to respond to the crisis and the leaders pursued the response activities that should be conducted on those situations. On the other side, the Florianópolis’ blackout was caused by an infra-structural collapse that the organization did not un-derstand the reasons. Then, the leaders had to learn about the crisis and develop new mechanisms and leadership processes to solve the crisis.

This characteristic was favorable to this research’s design and the Florianópolis’ blackout was selected because of its dimensions, the need to grasp the cri-sis until it unrolls and because it mobilized a great amount of employees to work on crisis response. So, the leadership processes would emerge naturally and could be explored with more emphasis.

After identifying the crisis that would be studied, the research participants were recruited and select-ed to represent the leaders and followers involved in the crisis response. They were identified with a snowball sampling strategy and a total of 1 execu-tive and 3 managers were selected. All of them were directly involved in the crisis response, in different hierarchical levels.

b. Data gathering

Data were gathered through extensive interviews with the research participants described above and through detailed reviews of secondary data sourc-es. The use of interviews in the qualitative research is a justifiable and legitimate means of gathering information for additional insights and theory de-velopment (Seidman, 1998; Spradley, 1979; Strauss & Corbin, 1998). This approach, its execution, and the drivers behind its use are consistent with argu-ments that qualitative methods derive from a com-bination of interpretivist sociological traditions and symbolic interacionism (Godoi, Bandeira-de-Mello, & da Silva, 2006).

A multitude of organizational documents and re-ports was consulted, and one researcher performed participant observation inside organizational rou-tines for aproximately 6 months, in order to under-stand organizational structure and culture. With this contact, the company’s Director of Operations, an executive position, was interviewed and indicated three managers that worked with him during the Florianópolis’ blackout episode. Three interviews were conducted with each informer, totalyzing 12 formal and semistructured interviews, whith more than 600 minutes of dialogue. All interviews were recorded, transcribed and confronted with organi-zational reports, local observations and media cov-erage about the studied crises. All data was collected approximately four years after the crisis and all re-spondents still work in the company.

c. Data analysis

Data analysis steps were conducted with the help of Atlas.ti software, in search for codes. Data coding fol-lowed an inductive approach (Strauss & Corbin, 1998), with the codes emerging after data collection. All tran-script elements related to the leadership processes dur-ing the crisis response were assigned with a code. Each stage of codification was accompanied by empirical validation on data and happened in cycles until theo-retical saturation was reached. Then, five core catego-ries emerged from the data and were identified after data analysis processes, such as follows:

a) crisis constraints (kinds of problems caused by the crisis);

b) organizational structure (social coalitions designed to solve and communicate specific problems);


c) cognitive factors (sensing and identifying problems, establishing priorities and learning mechanisms);

d) development of solutions (kinds of strategies cre-ated to solve problems); and

e) motivation mechanisms (how the leaders moti-vate their followers).

From this coding scheme, patterns emerged, which were validated and qualified across the crisis situ-ations described in the interviews. The media cov-erage for the studied crisis was also very important to validate the categories that resulted from data analysis. Figure 1 exemplifies the data analysis structure, showing the first order concepts identified during the interviews and the second order themes that emerged from the data and were considered the main categories for data analysis.

Figure 1: Example of data structure from concepts to themes

First-Order Concepts Second Order Themes

Crisis Constraints

Organizational Structure

Cognitive Factors

Development of Solutions

Motivation Mechanisms

“Our biggest problem was logistics. We worked with more than 500 people” (Respondent C). “At that time, all cellphones ran out of battery. It was a problem because we had to walk to communicate. So, communication was a great problem” (Respondent B). “Security was a question that bothered a lot” (Respondent C).

“We didn’t have a specific department to deal with that. Then, our Director became to call all the people that worked with transmission lines” (Respondent A). “There wasn’t any contingency plan. This kind of problem was quite impossible to happen. But it unfortunately happened” (Respondent D). “The groups were divided while people were arriving to help” (Respondent D).

“I was returning from lunch when the energy was interrupted. I was driving and all the traffic signals switched off. We didn’t know what was happening” (Respondent B). “My secretary counted. I gave more than 100 interviews during one day and a half trying to explain the problem to the customers” (Respondent C). “Then we figured out that the problem was on the bridge” (Respondent A).

“At the beginning of the night, we decided to build a new line, but we didn’t know how to do it” (Respondent A). “The the ideas were coming ... what about an air line? No, it is impossible to cross the sea! And an engineer decided to walk through the bridge and figured out that the structure had some spaces that we could hang the isolators for the new line” (Respondent B). “He did the project inside his head and we began to work” (Respondent B).

“I didn’t noticed lack of motivation. A man, to be on the top of a lamppost, at three o’clock in the morning, working for more than 15 hours, risking his life ...” (Respondent C). “They did it because they respect the company, they are proud of our organization. Mainly the people we brought from other cities ... solving the problem was a matter of honour ...” (Respondent C). “It was a kind of sinergy ... when anyone arrived and crossed the bridge to help us, a kind of energy was there ...” (Respondent B).


4. MAIN FINDINGS

Through the researchers’ immersion in the data (i.e., through repeated iteration between interview ses-sions, fact finding in secondary literature sources, and data analysis), came a set of models used to de-scribe how the leadership processes unfolded dur-ing the examined organizational crisis. Below are brief descriptions of the research findings across the five categories that emerged in the data analysis.

a. Crisis Constraints

“[...] then we realized that it would take long. It was impossible to fix or restore it with all that fire in there. So, until we could fully under-stand and realize what was going on, there was nothing to do. Let’s assume that, if only one of the lines had been reached, we would have some alternatives. But no, the two transmission lines were irreparably affected, they had no recovery. Something like that was quite impossible to happen. The city was without energy and we had no plan. Can you imagine what could hap-pen?” (Respondent B).

Having in mind that the examined crisis caused the lack of energy in a whole town during approxi-mately 52 hours, a set of problems arose during the crisis response. The leaders decided to build a new transmission line and restore the electricity supply chain and delivery in the city. During the creation of the new structure, a lot of constraints emerged and became a challenge to the leaders, as follows:

• Social Pressures: the inhabitants organized pro-tests and sent communications to the media claiming for the electricity back in their homes;

• Security Problems: as the city had no electricity, some thieves tried to assault homes, shops and citizens;

• Time Restriction: the energy delivery had to be restored as quick as possible;

• Technical Restriction: build a new transmission line in a few hours was not easy. This kind of job, when done as usual, takes months. During cri-sis response, the company needed to make it in a couple of days. As a result, a lot of technical prob-lems arose, such as lack of the adequate equip-ment or the absence of appropriate projects;

• Physical Restriction: as there was time restric-tion, the employees worked more than 16 hours, uninterruptedly, in order to restore the energy delivery. As a result of the extreme work condi-tions, the employees became tired, what could generate accidents;

• Organizational Communication: as they had no energy in the whole city, communicating by phone or e-mail was impossible, once they could not charge their computers or cell phones during the new supply chain creation;

• Decision Making: the decision making process-es to solve the crisis were very complex because the communication between executives, manag-ers and operators was very difficult and the flow of information was slow and sporadic;

• Leadership Stress: as there were severe con-straints during the crisis, and the leaders should deal with all of them, there were psycho-physio-logical implications on them, bringing different emotions as the crisis unfolded.

b. Organizational Structure

“We didn’t have a contingency plan or a different structure to follow in cases like that ... to make these decisions, call this guy, call that guy ... we didn’t have that. After this episode, we made a contingency plan and we know exactly who should be called. But during that time, we didn’t have such structure.” (Informer A).

“And this decision was taken here, within this informal structure. Then they decided to call people, and they delegated a lot of things when a new person arrived there. For example, some-one started to look for the needed items in the stocks, someone kept in touch with the guys who were designing the projects out there, on time, and so on.” (Informer D).

In order to consider all information about the crisis, the organizational structure has changed. Although the company had no crisis mobilization plan, a group of executives and managers was randomly structured to decide how the organization would respond to the crisis. The company stablished the address of an electricity substation that was near to the involved bridge as the Crisis Response Head Quarters and a lot of engineers went there to help the company. In this way, an informal coalition was


formed to make the strategic decisions. It is impor-tant to say that two of the research informers made part of this small group of people that made impor-tant decisions, such as the decision to build a new transmission line and the directions that this new line would have. Then, the new supply channel was divided in four parts, and it was randomly designed one manager for each part.

During the operational tasks to build the new trans-mission line, the company respected the organiza-tional formal structure of directors, managers and employees. So, the organizational structure was adapted to better respond to the crisis. The first en-gineers that arrived at the Crisis Response Head Quarters formed a group to make strategic deci-sions about the crisis and its response. Then, four engineers were designed to manage the services at different places. Each manager had employees that already work with transmission lines and they fol-lowed power relations due to their formal position and hierarchy in the company.

c. Cognitive Factors

“This cable was different because it was an un-derground facility, the only one that the com-pany had at the time. It has a very small vul-nerability exactly because it is under the bridge structure, so it is not susceptible to any kind of collision, it is not susceptible to anything, it is a quiet line there. So, it was completely un-predictable and we didn’t believe it was hapen-ing. We had to understand what caused the fire and the explosions and we had to learn how we could solve that ” (Informer A).

As the crisis unfolded, different people, either from the studied organizations or other institutions, con-ducted the sense giving processes. When the subject was the new supply channel as a whole, the priori-ties were established by the board of directors and the social coalition formed in the Crisis Response Head Quarters, near the locale of the cable rupture. On the other hand, when the problem sensing and identification was linked to one specific part of the supply chain that was being constructed, the sense giving was conducted by the specific manager re-lated to the identified problem. This distinction on conducting the sense giving was not formalized and its equilibrium was found during the crisis, in an emergent manner.

d. Development of solutions

“The ideas that emerged ... well, let’s try to make an electric transmission airline, because inside the bridge is not possible. And of course, first thing that comes to mind of an engineer, it is certainly impossible to do this, how do we make an airline, right, let’s assume that nor-mal distances between the bridge towers, they oscillate between 200, 250, 180 meters on aver-age. There, we have a 700 meters distance to connect lines. For a conventional airline of 700 meters, we would have to provide a structure of, at least, 100 meters of high on each side. Where we get that? Then the other idea, let’s try to make a line by another bridge, the oldest one. This idea did not progress due to the civil defense authorities ... ” (Informer B).

The company was not prepared for this kind of crisis and there was not any kind of plan to avert and respond to crises. As a result for this improper crisis management, without crisis prevention and signal detection, the vast majority of decisions and strategies created were emergent (Mintzberg, 1987), without any kind of previous deliberation. As the problems were identified, someone tried to create a possible solution until the constraint was solved. The needed knowledge to respond to the crisis was provided for a vast number of people and the solu-tions were being made at the same time as the crisis was being unfolded.

e. Motivation Mechanisms

“When people crossed the bridge and went over, a kind of energy was there, coming from ... I don’t know why ... and the person had that spirit, it was built from ... what I see now, is that it was a very strong, interesting meaning, every-one made a choice, I will be part of this story, but well, I’ll give my blood too. There was a guy here, who climbed a lamppost of those, he worked straight up there, we sent him a glass of water, apple, banana, he ate everything there and kept working. The guy did not came down until he finished his job over there.” (Informer C).

The research participants revealed that the employ-ees kept all the time motivated, in spite of the long work journeys, without being necessary any kind of motivation mechanisms, such as financial payments,


promotions or others. According to the informers, solving the crisis and restoring the energy delivery was a question of honour for the company and its employees. In this way, they kept motivated all the time, in order to restore the normality for the citi-zens as soon as possible. As a result, the participants made clear that the company solved the problem and the employees became more united and friend-ly to each other after the crisis.

5. DISCUSSION

In order to analyse the leadership process during the selected event, it is important to understand why the lack of electricity in the town was considered a crisis.

a. Crisis definition

It is important to perceive that all the crisis charac-teristics were present in the studied case, as the cable rupture was a low probability event that brought high consequences for the organization and for a great number of external stakeholders, the citizens included. The situation brought with it a lot of con-straints that imposed a severe pressure for quickly resolutions, as commented above, and gave great challenges for the organizational leaders. In accor-dance with Boin et al. (2005), it was not clear that the crisis was unfolding and the organizational lead-ers and stakeholders only perceived the threats af-ter the occurrence of the triggering event, the cable rupture. Besides, the definition of the situation as a crisis was only decided after the whole city became out of energy. At this point, the organizational lead-ers perceived the major problem, the power outage, and decided to randomly create a crisis group that got together near the rupture point to start the sen-semaking process, discover the causes of the event and impute “meaning” to the unfolding crisis. The definition of the situation as a crisis was a political process (Boin et al., 2005), developed by the leaders and employees that were together in the Crisis Re-sponse Head Quarters.

b. Crisis stages

About the crisis stages, the three major phases were evident. The precrisis stage (Fink, 1986) can be rep-resented by the whole period of time prior to the ca-ble rupture, in which no crisis signals were detected and no crisis prevention procedures existed. In this way, the organization did not act mindfully (Weick & Sutcliffe, 2001) and the incubation period of the

crisis (Turner, 1976) was not recognized, as no cri-sis signals were identified. So, the organization had no “signal detection mechanisms” (Mitroff, 2004) and had no abilities to identify any information that could demonstrate the critical stage of the existing electricity-distribution system and the escalation of the crisis. Maybe the crisis could be averted if the organization had such mechanisms.

The critical period (Stein, 2004) started with the trig-gering event, the rupture of the cable rupture. This stage, also known as acute crisis stage (Fink, 1986), remained for the first 6 hours after the precipitating event. A lot of contingencies became relevant and, in accordance with Hale et al. (2005), the communica-tion’s channels options reduced immediately after the cable rupture. At the same time, all stakehold-ers, internal and external, became involved, in ac-cordance with Pearson & Mitroff (1993), including the citizens and the media. Because of the social problems, time became relevant and the decision making should be done as quick as possible. So, this stage existed for 6 hours, until the main decisions were made and the leaders agreed in the causes of the crisis and what should be done.

The chronic crisis stage (Fink, 1986) started when the leaders decided what to do and finished when the new transmission line was ready. This period, when the city was out of electricity, also can be considered the crisis aftermath (Garland, 1998).

c. Crisis Management

In the studied event, the majority of crisis manage-ment practices were situated in the crisis response stage (Boin et al., 2010; Hale et al., 2005; Leidner, Pan, & Pan, 2009). The crisis prevention stage could not be observed, as the company had not any kind of plan to avert and respond to crises. Incubation processes thus remained latent and undiscovered. Although, the recovery from the crisis could be ob-served as the participants mentioned the creation of a contingency plan after the crisis, in order to pre-pare the organization for future crises. This means that the organization is trying to learn from the crisis and has developed a kind of “plan for action” for future problems.

This paper focused in the response stage because the majority of crisis leadership tasks were related to this crisis management phase. As the studied cri-sis developed, a number of constraints appeared and the leaders should have focused their attention


to them. These unfolding circumstances were pre-sented to the leaders as challenges to act upon. After the data analysis, five main categories emerged to link these, as shown in the figure 1.

In this way, the crisis leadership tasks assumed by the leaders were realized in order to address these five main challenges.

d. Crisis leadership

All the crisis leadership tasks presented by Boin et al. (2005) were present in this study. Sensemaking processes were necessary to understand the causes and the consequences of the electricity-distribution cables. In this occasion, leaders and employees that were together in the rupture point, after the trigger-ing event, grasped the crisis as it unfolded. The sen-semaking task took place at the same time that the decisions were being made. These two tasks, sense-making and decision making, took place together and, as the crisis “sense” was being made, the leaders discussed the possible reactions to the crisis events, creating the decisions in an emergent manner. After deciding what to do, the meaning making task took place and the crisis explanations were distributed to internal and external stakeholders by the communi-cation channels still available. Finally, when the crisis constraints were controlled and the organizational ordinary state was back, the terminating task ended

and gave place to the learning task, when the organi-zation started to make a contingency plan based on the lessons learned with the crisis.

Besides, it is important to comment that the follow-ers were more easily influenced by their leaders dur-ing the crisis, as the employees were more likely to acquiesce to their leaders and agreed to keep work-ing for long periods. These results are in accordance with Halverson et al. (2004), whose work discussed that followers are more likely to acquiesce to their leaders under stress and are more receptive to in-formation provided under stress. For this reason, it was not necessary to develop any kind of motiva-tion mechanisms, such as financial payments, pro-motions or others. According to the managers inter-viewed, solving the crisis and restoring the energy delivery was a question of honour for the company and its employees. In this way, they kept motivated all the time, in order to restore the normality for the citizens as soon as possible. This fact demonstrates that the meaning making task was successful.

In the crisis context, the five crisis leadership tasks – sensemaking, decision making, meaning making, terminating and learning – were developed to ad-dress the five leadership challenges that took place in the crisis response – crisis constraints, organiza-tional structure, cognitive factors, development of solutions and motivation mechanisms – as summa-rized in Table 1.

Table 1 – The crisis leadership tasks through the crisis response’s leadership challenges.

they kept motivated all the time, in order to restore the normality for the citizens as soon as

possible. This fact demonstrates that the meaning making task was successful.

In the crisis context, the five crisis leadership tasks – sensemaking, decision making,

meaning making, terminating and learning – were developed to address the five leadership

challenges that took place in the crisis response – crisis constraints, organizational structure,

cognitive factors, development of solutions and motivation mechanisms – as summarized in

Table 1.

Table 1 – The crisis leadership tasks through the crisis response’s leadership challenges.

i. Responding the crisis constraints

Beyond the lack of electricity problem, the crisis brought a lot of constraints that the

leaders had to respond immediately. The main problem, allied to these constraints, forced the

leaders to practice all the five crisis leadership tasks together, for each constraint. In this way,

for each separate problem, the leaders had to understand what was going on, decide what to

do, convey the internal and external stakeholders that it was the correct decision, act upon the

problem and learn with it for the future. The leadership tasks related to the response to the

crisis constraints took place during the critical period stage and the chronic crisis stage.

ii. Adapting the organizational structure

The crisis leadership tasks related to the organizational structure were only two,

decision making and meaning making. The first strategy adopted by the leaders was to

randomly select some employees, forming a technical group to deal with the crisis. This

Crisis Response's leadership challenges

Crisis phases Crisis management stages Related leadership tasks

Sense makingCritical Period Decision making

Crisis Constraints and Crisis Response Meaning makingChronic Crisis Stage Terminating

LearningDecision makingMeaning making

Critical Period Crisis Response Sense makingCognitive Factors and and

Chronic Crisis Stage Recovery StageCritical Period

Development of Solutions and Crisis ResponseChronic Crisis Stage Learning

Meaning MakingTerminating

Meaning making

Decision Making

Organizational Structure Critical Period Crisis Response

Motivation Mechanisms Chronic Crisis Stage Crisis Response


e. Responding the crisis constraints

Beyond the lack of electricity problem, the crisis brought a lot of constraints that the leaders had to respond immediately. The main problem, allied to these constraints, forced the leaders to practice all the five crisis leadership tasks together, for each constraint. In this way, for each separate problem, the leaders had to understand what was going on, decide what to do, convey the internal and exter-nal stakeholders that it was the correct decision, act upon the problem and learn with it for the future. The leadership tasks related to the response to the crisis constraints took place during the critical pe-riod stage and the chronic crisis stage.

f. Adapting the organizational structure

The crisis leadership tasks related to the organi-zational structure were only two, decision making and meaning making. The first strategy adopted by the leaders was to randomly select some em-ployees, forming a technical group to deal with the crisis. This decision divided the organizational structure for decision making in two: an informal structure for strategic decisions related to the cri-sis and a formal one to deal with operational deci-sions related to the construction of a new trans-mission line. The leadership tasks related to the organizational structure took place only during the critical period.

g. Dealing with cognitive factors

Dealing with cognitive factors was at the heart of the crisis response. Creating a “meaning” to the crisis – sensemaking – and propagating this meaning through all the stakeholders – meaning making – were not easy tasks. It is interesting to remember that these two crisis leadership tasks were conducted by different people. When the subject was the new supply channel as a whole, the priorities were established by the board of di-rectors and the social coalition formed near the lo-cale of the cable rupture. On the other hand, when the problem sensing and identification was linked to one specific part of the supply chain that was being constructed, the sense giving was conduct-ed by the specific manager related to the problem identified. The leadership tasks related to dealing with cognitive factors took place during the criti-cal period and the chronic crisis stage.

h. Creating and developing solutions

The decision making task was always in the cen-tre of the crisis response stage. Unfortunately, the company was not prepared for the crisis and it had not any kind of crisis management plans. Because of that, the decisions were taken so far as the sen-semaking processes were developed. This is true for the major crisis, the lack of energy, and for the numerous crisis constraints that unfolded with the crisis. In this way, the vast majority of decisions and strategies created were emergent (Mintzberg, 1987). To address this leadership challenge, it was used the decision making task during the critical period and the chronic crisis stage. After the solution of the con-straints, the leaders tried to learn with them in order to avoid similar problems in the future.

i. Developing motivation mechanisms

To address this leadership challenge, the leaders used two crisis leadership tasks: meaning mak-ing and terminating. By communicating the crisis “meaning” to the employees, the leaders could stim-ulate a culture where restoring the energy delivery was a question of honour for the company and its employees. For each constraint that was resolved, the employees had become more united to resolve the other problems. So, disseminating the correct “meaning” and warranting the termination of the constraints were enough to keep the employees mo-tivated, in spite of the long work journeys, without being necessary to create other motivation mecha-nisms, such as financial payments or promotions.

6. FINAL CONSIDERATIONS

This paper analyzes an organizational crisis in the Brazilian Electrical Sector, focusing on the lead-ership challenges during the crisis response and identifying the mechanisms used to mobilize peo-ple and respond to the crisis. As a result, it was pos-sible to identify that, prior the studied crisis, the company had not any kind of crisis management preoccupation. Then, the crisis got the organiza-tion unprepared. Happily, the company responded successfully to the crisis, although in an improper manner. So, the importance of being prepared to an organizational crisis was demonstrated. Besides, it was documented the leadership challenges brought with crises and a five categories model was devel-oped to analyze the crisis leadership tasks during the crisis response.


Unfortunately, the occurrence of crisis leadership tasks during the crisis prevention stage could not be observed, as the organization had no signal de-tection mechanisms to identify and avoid possible crises. In this way, the precrisis stage was not anal-ysed. On the other hand, the critical period and the chronic crisis stage were observed and it was pos-sible to analyse the crisis management’s response stage as a whole, focusing the leadership challenges presented by the crisis, such as the crisis leadership tasks used by the leaders to respond to the crisis and lead the organization back to its ordinary state. So, the leaders used the five crisis leadership tasks pre-sented by Boin et al. (2005) – sense making, decision making, meaning making, terminating and learning – to address the challenges brought with the crisis. According to each challenge, the leaders used differ-ent crisis leadership tasks, as summarized in table 1.

This study brings important theoretical contribu-tions, as it corroborates with previous research on crisis management and crisis leadership, such as Smart & Vertinski (1977), Fink (1986), Pearson & Mi-troff (1993), Halverson et al. (2004), Boin et al. (2005) and Hale et al. (2005), showing that: (1) key decisions were made by a small, tightly knit group of individu-als; (2) all stakeholders, internal and external, became involved; (3) the communication’s channels options reduced immediately after the precipitating event; (4) the relationship between leader and followers has changed, as followers were more likely to acquiesce to their leaders under stress and were more receptive to information provided under stress; (5) the leaders used five crisis leadership tasks to respond to the cri-sis; (6) it is more difficult to grasp and react to a cri-sis after the critical period. Besides, it illustrates the leadership processes that were undertaken in order to respond to an organizational crisis.

On a practical basis, it was important to present a descriptive case study, which showed real prob-lems, faced by an organization during the response of a huge crisis. The main categories that emerged from data are important to help other companies to plan crisis management systems and procedures. However, it is important to say that this study was restricted to observe one crisis episode. As a sugges-tion for future research, other crisis events can be studied in order to validate these contributions and observe if the main categories of this research will also be present.

Finally, it was possible to observe practically the paradoxical nature of crisis (Nathan, 2000), as there

were positive and negative outcomes to the stud-ied event. In one hand, the negative aspects of the crisis were present because of the lack of energy in the whole town and with all the constraints brought with the crisis and already discussed in this paper. On the other hand, a very positive outcome was achieved, as the employees became more united and friendly to each other after the crisis. This fact dem-onstrates that a crisis can have positive outcomes if the organizational leaders use the correct leadership tasks during the crisis response. In this way, facing a crisis situation may not be so bad if the organization is well prepared.

7. REFERENCES

Acquier, A., Gang, S., & Szpirglas, M. (2008). From stakeholder to stakesholder management in crisis episodes: A case study in a public transportation company. Journal of Contingencies and Crisis Management, 16(2), 101–114.

Alpaslan, C. M., Green, S. E., & Mitroff, I. I. (2009). Corporate governance in the context of crises: Towards a stakeholder theory of crisis management. Journal of Contingencies and Crisis Management, 17(1), 38–49.

Barton, M. A., Sutcliffe, K. M., Vogus, T. J., & DeWitt, T. (2015). Performing under uncertainty: Contextualized engagement in wildland firefighting. Journal of Contingencies and Crisis Management, 23(2), 74–83.

Bazerman, M. H., & Watkins, M. (2004). Predictable surprises: The disasters you should have seen coming, and how to pre-vent them. Leadership for the common good. Boston: Har-vard Business School Press.

Beyer, J. M., & Browning, L. D. (1999). Transforming an indus-try in crisis: Charisma, routinization and supportive cultural leadership, 4(3), 483–520.

Bligh, M. C., Kohles, J. C., & Meindl, J. R. (2004). Charisma under crisis: Presidential leadership, rhetoric, and media responses before and after the September 11th terrorist attacks. The Leadership Quarterly, 15, 211–239.

Boin, A., & Hart, P. (2003). Public leadership in times of crisis: Mis-sion impossible? Public Administration Review, 63(5), 544–553.

Boin, A., Hart, P., Stern, E., & Sundelius, B. (2005). The politics of crisis management : public leadership under pressure. Cam-bridge, UK: Cambridge University Press.

Boin, A., & Gralepois, M. (2006). Organizing for future crises: The case of integrated risk management in French cities. In-ternational Journal of Emergency Management, 3(1), 91–97.

Boin, A., Hart, P. ‘T, McConnell, A., & Preston, T. (2010). Leader-ship style, crisis response and blame management: The case of hurricane Katrina. Public Administration, 88(3), 706–723.

Boin, A., McConnell, A., & Hart, P. (2008). Governing after cri-sis : The politics of investigation, accountability and learning. Cambridge, UK: Cambridge University Press.


Boin, A., & Rhinard, M. (2008). Managing transboundary crises: What role for the European Union? International Studies Re-view, 10(1), 1–26.

Boin, A., & Smith, D. (2006). Terrorism and critical infrastruc-tures: Implications for public-private crisis management. Public Money and Management, 26(5), 295–304.

Bouillette, J. R., & Quarantelli, E. L. (1971). Types of patterned variation in bureaucratic adaptations of organizational stress. Social Inquiry, 41, 39–45.

Brasil. (2016). Boletim de monitoramento do sistema elétrico rel-ativo ao mês de janeiro de 2016. Brasília: Ministério de Minas e Energia.

Chekkar-Mansouri, R., & Onnee, S. (2013). Towards a theoreti-cal. Understanding of the recurrence of crises embracing the issue of organizational learning: An illustration with the case of two hostile takeover bids faced by société générale (1988, 1999). Journal of Contingencies and Crisis Managementcies, 21(1), 56–68.

Deverell, E., Hansén, D., & Management, C. (2009). Learning from crises and major accidents: From post-crisis fantasy documents to actual learning in the heat of crisis. Journal of Contingencies and Crisis Management, 17(3), 143–145.

Elliott, D. (2009). The failure of organizational learning from cri-sis: a matter of life and death? Journal of Contingencies and Crisis Management, 17(3), 157–168.

Elliott, D., & Smith, D. (2007). Voices from the terraces: From “Mock Bureaucracy” to “Learning From Crisis” Within the UK’s Soccer Industry. In C. M. Pearson, C. Roux-Dufort, & J. A. Clair (Eds.), International Handbook of Organizational Crisis Management. Thousand Oaks: SAGE Publications.

Fink, S. (1986). Crisis management : Planning for the inevitable. New York, USA: American Management Association.

Garland, C. (1998). Understanding trauma: A psychoana-lytical approach. Tavistock Clinic series. New York, USA: Routledge.

Godoi, C. K., Bandeira-de-Mello, R., & da Silva, A. B. (2006). Pes-quisa Qualitativa em Estudos Organizacionais: Paradigmas, Estratégias e Métodos. São Paulo: Saraiva.

Hale, J. E., Dulek, R. E., & Hale, D. P. (2005). Crisis response communication challenges: Building theory from qualitative data. Journal of Business Communication, 42(2), 112–134.

Halverson, S. K., Murphy, S. E., & Riggio, R. E. (2004). Charismat-ic leadership in crisis situations: A laboratory investigation of stress and crisis. Small Group Research, 35(5), 495–514.

Hannah, S. T., Uhl-Bien, M., Avolio, B. J., & Cavarretta, F. L. (2009). A framework for examining leadership in extreme contexts. Leadership Quarterly, 20(6), 897–919.

Hart, P., & Boin, A. (2001). Between crisis and normalcy: the long shadow of post-crisis politics. In U. Rosenthal, A. Boin, & L. K. Comfort (Eds.), Managing Crises: Threats, Dilemmas, Op-portunities. Springfield: Charles C. Thomas.

Heifetz, R. A. (1994). Leadership without easy answers. Cam-bridge, Mass.: Belknap Press of Harvard University Press.

Heifetz, R. A., & Linsky, M. (2002). Leadership on the line: stay-ing alive through the dangers of leading. Boston, Mass.: Har-vard Business School Press.

Heifetz, R., Grashow, A., & Linsky, M. (2009). The practice of adaptive leadership: Tools and tactics for changing your or-ganization and the world. Boston, Mass.: Harvard Business School Publishing.

Hermann, C. F. (1963). Some consequences of crisis which limit the viability of organizations. Administrative Science Quar-terly, 8(1), 61–82.

Hunt, J. G. J., Boal, K. B., & Dodge, G. E. (1999). Crisis-responsive charisma on followers: An experimental examination of two kinds of charismatic leadership, 10(3), 423–448.

Kovoor-Misra, F., Zammuto, R. F., & Mitroff, I. I. (2000). Crisis preparation in organizations: Prescription vs. reality. Tech-nological Forecasting and Social Change, 63, 43–62.

Laere, J. (2013). Wandering through crisis and everyday organiz-ing: Revealing the subjective nature of interpretive, temporal and organizational boundaries. Journal of Contingencies and Crisis Management, 21(1), 17–25.

Lagadec, P. (2009). A new cosmology of risks and crises: Time for a radical shift in paradigm and practice. Review of Policy Research, 26(4), 473–486.

Leidner, D. E., Pan, G., & Pan, S. L. (2009). Journal of strategic information systems the role of IT in crisis response : Les-sons from the SARS and Asian Tsunami disasters. Journal of Strategic Information Systems, 18(2), 80–99.

Maitlis, S. (2005). The social processes of organizational sense-making, 48(1), 21–49.

Maitlis, S., & Christianson, M. (2014). Sensemaking in organiza-tions: Taking stock and moving forward. The Academy of Management Annals, 8(1), 57–125.

Merriam, S. B. (1998). Qualitative research and case study ap-plications in education (2nd ed.). San Francisco: Jossey-Bass Publishers.

Mintzberg, H. (1987). The strategy concept .1. 5 Ps for Strategy. California Management Review, 30(1), 11–24.

Mitroff, I. I. (2004). Crisis leadership: Planning for the unthink-able. Hoboken, NJ: Wiley.

Moynihan, D. P. (2009). From intercrisis to intracrisis learning. Journal of Contingencies and Crisis Management, 17(3), 189–198.

Nathan, M. (2000). The paradoxical nature of crisis. Review of Business, 21(3/4), 12–16.

Patriotta, G., & Gruber, D. A. (2015). Newsmaking and sense-making: Navigating temporal transitions between planned and unexpected events. Organization Science, 26(6).

Pauchant, T. C., & Mitroff, I. I. (1992). Transforming the crisis-prone organization: Preventing individual, organizational, and environmental tragedies. The Jossey-Bass management series (1st ed.). San Francisco: Jossey-Bass Publishers.


Pearson, C. M., & Clair, J. A. (1998). Reframing crisis manage-ment. Academy of Management Review, 23(1), 59–76.

Pearson, C. M., & Mitroff, I. I. (1993). From crisis-prone to crisis-prepared. Academy of Management Executive, 7, 48–59.

Pearson, C. M., Roux-Dufort, C., & Clair, J. (2007). International handbook of organizational crisis management. Los Angeles: Sage Publications.

Pillai, R. (1996). Crisis and the emergence of charismatic lead-ership in groups: An experimental investigation. Journal of Applied Social Psychology, 26(6), 543–562.

Quarantelli, E. L. (1988). Disaster crisis management:A summary of research findings. Journal of Management Studies, 25(4), 373–385.

Roe, E., & Schulman, P. R. (2008). High reliability management: Operating on the edge. High reliability and crisis manage-ment. Stanford, Calif.: Stanford Business Books, an imprint of Stanford University Press.

Rosenthal, U., Boin, A., & Comfort, L. K. (2001). Managing crises: Threats, dilemmas, opportunities. Springfield, Ill.: Charles C Thomas.

Roux-Dufort, C., & Lalonde, C. (2013). Exploring the theoretical foundations of crisis management. Journal of Contingencies and Crisis Management, 21(1), 1–3.

Santella, N., Steinberg, L. J., & Parks, K. (2009). Decision making for extreme events: Modeling critical infrastructure interde-pendencies to aid mitigation and response planning. Review of Policy Research, 26(4), 409–422.

Seidman, I. (1998). Interviewing as qualitative research: A guide for researchers in education and the social sciences (2nd ed.). New York: Teachers College Press.

Shaw, D., Hall, M., Edwards, J. S., & Baker, B. (2007). Responding to crisis through strategic knowledge management. Journal of Organizational Change Management, 20(4), 559–578.

Shepherd, D. A., & Sutcliffe, K. M. (2015). The use of anthropo-morphizing as a tool for generating organizational theories. The Academy of Management Annals, 9(1), 97–142.

Shrivastava, P. (1987). Bhopal: Anatomy of a crisis. Ballinger se-ries in business in a global environment. Cambridge, Mass.: Ballinger Pub. Co.

Silveira-dos-Santos, R. A. (2012). Uma teoria substantiva do pro-cesso de liderança em momentos de crise organizacional: O caso das empresas de distribuição de energia elétrica. Uni-versidade Federal de Santa Catarina, Florianópolis.

Smart, C., & Vertinsky, I. (1977). Designs for crisis decision units. Administrative Science Quarterly, 22(4), 640–657.

Sommer, A., & Pearson, C. M. (2007). Antecedents of creative de-cision making in organizational crisis: A team-based simu-lation. Technological Forecasting and Social Change, 74(8), 1234–1251.

Spradley, J. P. (1979). The ethnographic interview. New York: Holt, Rinehart and Winston.

Stein, M. (2004). The critical period of disasters: Insights from sense-making and psychoanalytic theory. Human Relations, 57(10), 1243–1261.

Strauss, A. L., & Corbin, J. M. (1998). Basics of qualitative re-search: Techniques and procedures for developing grounded theory (2nd ed.). Thousand Oaks: Sage Publications.

Turner, B. A. (1976). Organizational and interorganizational de-velopment of disasters. Administrative Science Quarterly, 21(3), 378–397.

Weick, K. E. (1988). Enacted sensemaking in crisis situations. Journal of Management Studies, 25(4), 305–317.

Weick, K. E. (1995). Sensemaking in organizations. Foundations for organizational science. Thousand Oaks: Sage Publica-tions.

Weick, K. E., & Sutcliffe, K. M. (2001). Managing the unexpected: Assuring high performance in an age of complexity. Uni-versity of Michigan Business School management series (1st ed.). San Francisco: Jossey-Bass.

Weisæth, L., Knudsen Jr, Ø., & Tønnessen, A. (2002). Technologi-cal disasters, crisis management and leadership stress. Jour-nal of Hazardous Materials, 93(1), 33–45.

Wester, M. (2009). Cause and consequences of crises: How per-ception can influence communication. Journal of Contingen-cies and Crisis Management, 17(2), 118-125.

Submitted 30.03.2016. Approved 14.06.2016 Evaluated by double blind review process.

Social Interaction and Price Transmission in Multi-Tier Food Supply Chains

Maria Widyarini Professor at Parahyangan Catholic University – Bandung – West Java, Indonesia

[email protected]

Togar M Simatupang Professor at Bandung Institute of Technology, School of Business and Management – West Java, Indonesia

[email protected]

Per Engelseth Professor at Molde University College – Molde, Norway

[email protected]

ABSTRACT: This research focuses on social interaction associated with price transmission in a multi-tier rice supply chain. A case study and qualitative methods are employed to examine a well-established supply network in Karawang District in Indonesia. Farmers and traders used their existing network in selling rice crops to traders and adopted a payment scheme for cash-and-carry transactions. Informa-tion on the market situation was obtained through personal interviews and observations including text messaging with farmer and trader informants. Evidence reveals that social relationships are vital in transmitting price information among networked actors to maintain the flow of rice, mitigate risk, and avoid losses due to poor quality of the rice product. Findings show that social interaction enables actors in an end-to-end rice supply chain to deal with the assurance of supply rationing.

Keywords: Social interaction, price transmission, pricing, information seeking, supply chain.


110

Widyarini, M., Simatupang, T. M., Engelseth, P.: Social Interaction and Price Transmission in Multi-Tier Food Supply ChainsISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 1 p 110 – 128111

1. INTRODUCTION

Price transmission of goods, such as rice, in net-works is dependent on common network struc-tures. Although there is variation in complexity, price transmission always involves sequentially interdependent stages of transformation (Alder-son, 1965; Thompson, 1967). The supply network of such goodsis impacted by trading, and the price transmission is an inherent component in trading goods and services. In the food industry, paying at-tention to this end-to-end food supply has become pertinent in recent years. The traceability require-ments of food products entail a need to inform about product safety and quality features, includ-ing all transformations from harvest or fishing (En-gelseth, 2009, 2012). Managing the flow of goods and services plays a key role during aproduction involving product transformation. The aim of a production is to achieve customer value,which is vital in supply chain management (SCM) because it is ultimately associated with end-user perceptions. Value, in this perspective, is associated with ben-efit perceptions weighed against sacrifices, and it is negotiated through the chain of information and communication (Engelseth, 2016). Price is a vital component in this negotiation. Being negotiable, price is not a fixed metric. Research about the sup-ply network in this study is intended to provide an empirical understanding that rice pricing furthers our knowledge of the SCM of food supply.

The first stream of research associated with price transmissionis concerned with the price transmis-sion phenomena in which the negotiation of price associated with customer value is considered as a perception. Research about price transmission along a food chain was conducted by Vavra and Goodwin (2005) who analyzed the vertical price relationships which were characterized by the magnitude, speed and nature of the adjustments of an abrupt “shock” change in prices at one level and in the up- or down-stream prices along the food chain. Jensen and Moller (2007) studied price transmission in the Dan-ish food chain, including its vertical price interac-tion characterized by the degree of completeness of pass-through, speed and type of price adjustments through the supply chain. Their discussion about the vertical price transmission involved the creation of a single set of measures to define the speed, direc-tion and magnitude of the impacts of price adjust-ments. The results of the study showed that there were few social interaction phenomena among the

actors in the supply chain (Jensen & Moller, 2007; Vavra & Goodwin, 2005).

The second stream on price transmission deals with the underlying factors that help explain price transmission. Aramyan and Kuiper (2009) stated that factors explaining price transmission were market power, adjustment costs, public interven-tions, publicity/food scares, and perishability of the products. Bunte (2006) argued that price transmis-sion addressed the imperfect price transmission corresponding to the market power in the agri-food chains. Little explanation has been provided about social interactions among the actors during the price transmission in the agri-food supply chains. Previous research treats social interactions as an exogenous factor in price transmission.

Price transmission in agri-food supply chains is an essential component of trading in a supply chain, an exchange process that involves income distribution among the supply chain actors (Bunte, 2006). Dur-ing the price transmission, there is social interaction which is understood to play an important role in shaping the price. Thus, this study attempts to de-scribe and investigate the social interaction phenom-ena of the price transmission along the rice supply chain. Friedman (1980) stated the role of prices in an organized economic activity is to transmit infor-mation, to provide an incentive to react to the infor-mation and to determine income distribution. These three functions are closely interrelated to social in-teractions. In addition, by adopting a transvection model of Alderson (1965), the role of price trans-mission is viewed rom the end-to-end food supply perspective. The transvection model has since been expanded by taking into consideration the interde-pendency theory (see Engelseth, 2016; Hammervoll, 2014; Thompson, 1967).

Taking the Aldersonian transvection perspective of goods supply, Engelseth and Felzensztein (2012) focused on “transformation” and end-user util-ity concerns. Production creates “service” that has values (Penrose, 1959). A bundling of goods and intangible deliverables are perceived and valued by the customer (Vargo & Lusch, 2004). Thompson (1967) studied the provision of service taking place in the supply network context by considering rice pricing which involves both interdependent hu-man social interactions and negotiation processes, or called “mutual adjustment”. Further, Orton and Weick(1990) studied the trading in an end-to-end supply chain structure, involving a long-linked


technology. Such trading creates reciprocal interde-pendencies in a marketing channel, consisting of a series of intermediaries trading. This is in line with Engelseth’s argument that said that commodity-like food supplies need to be flowed through a seriesof markets (Engelseth, 2016).

This case study, conducted in Karawang District of West Java, seeks to describe the practice of rice pric-ing in a supply chain structure, which is sequentially interdependent. These sequential interdependencies are naturally still prevalent in rice supply from an operational view point. This study focuses on price transmission in intermittent trading events involv-ing interdependency, mutual adjustments, and pric-ing in association with value creation which is built through interaction between the buyer and seller in the supply chain.

This research attempts to show the ways how, and the reasons why, the actors in the agri-food chains interacted and made decisions during the price transmission. First, the study contributes by de-scribing an end-to-end supply chain analysis as a framework to get a better understanding of the so-cial interaction among rice actors. This is in line with Engelseth (2016), who pointed out that, even in cases of market trading of commodity-like food products, relationships that integrate actors in these markets create trust and trading skills. Second, this study aims to apprehend price transmission among ac-tors and what constitutes the interrelation between the conceptual theories and the existing conditions. Third, this study critically examines the transvection theory-based price transmission analysis (Alderson, 1965) and explores its potential connection with oth-er theoretical frameworks.

This paper is structured as follows. The first part of the paper contains the literature review that dis-cusses pricing and long-linked supply. The second part explains the research model based on the trans-vection model, value creation, and supply network. The third part presents the research model and is followed by method as the fourth part. The fifth part is the analysis and is followed by discussion as the sixth part. The last part of this paper provides the conclusion and suggestions for future research.


This section starts with the description of pricing and price transmission and is continued by outlin-ing aresearch gap and the concept of social interac-

tion predominantly associated with interdependen-cy (Thompson, 1967) in the scope of the transvection model (Alderson, 1965).

2.1 Pricing

French (1997) argued that price contains market in-formation which affects actors’ perceptions (buyer/seller) of product value and decisions during an in-teraction process. Vavra and Goodwin (2005) stated that price plays a leading role through sending in-formation to align buyers and sellers in the market. Pricing is the strategic decision-making process where actors need information to determine the right unit price (Dutta, Zbaracki, & Bergen, 2003). The three functions of pricing are: informing, pro-viding incentive to produce, and distributing in-come from the ultimate buyers to retailers, whole-salers, manufacturers or owners of resources, and vice versa (Friedman, 1980; Mankiw & Taylor, 2014).

2.2 Price Transmission

Bunte (2006), supported by Aramyan and Kuiper (2009), defined a situation where prices at one level of a supply chain react to changes at another level as “price transmission”. In some situations, the price changes at one level are not transmitted to other levels. These levels can be interpreted as tiers in the supply chain structure. Vavra and Goodwin (2005) studied price transmission from farm prices to the retail level by measuring the speed and the extent of price changes during a transmission process. Jen-sen and Moller (2007) investigated price transmis-sion patterns which can be characterized by degree, speed and type of price adjustments through the supply chain. Acosta and Valdes (2014) showed the vertical price transmission through the assessment of the nature, extent of adjustment, and speed with which disruptions are transmitted along the differ-ent actors in a milk supply chain in Panama.

2.3 Research Gap

Previous research on pricing in supply chains has been mainly concerned withthe explanation of what technically constitutes price transmission, focusing on factors that explain price transmission as a pro-cess.In line with the price transmission phenomena, Vavra and Goodwin (2005) reviewed the mecha-nism of asymmetric price transmission by measur-ing vertical price transmission empirically along a food chain. The vertical price adjustments were


described by the speed, direction, and magnitude relative to the initial market shock experienced by agents at different levels of the activity. Other re-searches on price transmission were completed and explained by Jensen and Moller (2007), as well as by Acosta and Valdes (2014). Aramyan and Kuiper (2009) stated that, to understand prices are transmit-ted along the agri-food supply chains, it is necessary to comprehend the chain structure and the impact of prices on each link of the chain through several factors: market power, adjustment costs, public in-terventions, and product perishability. According to Bunte (2006), price transmission is a market indi-cator among actors in the oligopolistic and oligop-sonistic interdependence, which gives impact on the price adjustment lags and causes an asymmetry in the price shock reaction.

Price transmission as a social interaction has re-ceived less attention (Acosta & Valdes, 2014; Jensen & Moller, 2007; Vavra & Goodwin, 2005). The prima-ry focus of previous research on price transmission was concentrated on evaluations of the links between farm, wholesale, and retail prices. Vavra and Good-win (2005) supported Jensen and Moller (2007) and Acosta and Valdes (2014) in using the price transmis-sion analysis to review the mechanisms of asymmet-ric price transmission and in measuring the degree of price transmission to explain the speed and magni-tude of price transmission. Bunte (2006) explored and analyzed the transmission of price changes from the farm level to consumer level by using the empirical data to measure pricing performance.

2.4 Social Interaction

Social interaction is defined as a situation in which behaviors of one actor influence the behaviors of others, and vice versa (Manski, 2000; Scheinkman, 2008; Thompson & Hickey, 2005; Turner, 1988). Scheinkman (2008) stated that social interactions can be called non-market interactions as the interactions are not regulated by the price mechanism. Godes

et al. (2005) defined social interaction as an action which is taken by an individual engaged in the sell-ing of the product or service actively and impacts those who use the product or service. Social interac-tion on price transmission involves action influenc-ing adjacent actors to communicate, seek informa-tion and transmit price information to negotiate and exchange. Social contact and communication is a condition for ongoing social interaction. Communi-cation is the process of delivering information from a communicator to another party using symbols such as words, sounds and motion screen (Heath & Bryant, 2012; Krendl,Ware, Reid, & Warren, 1996).

Social interaction in information seeking is shown by actors who give price signals and conditions of commodities sold. Negotiation starting from the price signals and commodities offered does not generate up-front acceptance in the dyadic relation-ship. During a negotiation, a “tug of war” between a value claim and how the service is produced take places before a price agreement is reached (Bichler, Kersten, & Strecker, 2003). The same pattern is found being used by the actors who perform trans-actions throughout the supply chain to transmit price information. The types of social interactions are: exchange, cooperation, and coercion (Lin, 2001; Thompson & Hickey, 2005).

2.5 Transvection Model

Alderson’s end-to-end marketing channels model is applied to understand the end-to-end supply chain context of price transmission (Alderson, 1965). Alder-son’s transvection model is mainly logistical placing focused not on transactions but on goods transforma-tions. This model is also on how goods transforma-tions are supported by step-wise decision-making. Transvection logic, upon understanding features of supply utility from the end-user perspective, literally traces the flow of upstream goods accounting for how goods are directed by a sequence of decision-making events or “sorts” in Alderson’s term.


Figure 1. The Transvection Model

As shown in Figure 1, the goods flow represents “production”, the features of the creation of the service provided (Penrose, 1959), while the ob-jective of this flow is defined as customer value. Alderson (1965) described production as a piece-meal transformation of goods; value claiming was defined as the difference between customer-per-ceived benefits against costs. Adding the nature of interdependencies to this model would be to ac-count the overall food chain as suggested by the transvection model. In physical distribution, vari-ous types of resources were combined and trans-formed through processes in a sequential manner. This involved planning, forecasting or buffering supply. In cases when planning failed, supplies would be rationed in these sequentially interde-pendent forms of supply.

Following Alderson’s transvection model, actors were involved in sorts as flexible joints in the supply network structure; that is: how they were intercon-nected through exchange processes, how informa-tion was used to support a negotiation and intercon-nect people with goods transformation processes (Alderson & Martin, 1965). Thus, transvection was more than a transaction, as it was not limited only on the successive negotiations of exchange but also included decision-making, negotiation and agree-ment. Case descriptions were, however, modeled based on a reverse-type inquiry and on their actual downstream flow (Engelseth, 2012), as shown in Figure 1 as a chain of value-creating events.

The transvection, in a very limited degree, accounts for the role of information in supporting goods transfor-mation in the supply chain. As information technology develops, it is increasingly important to consider the role of information and its configuration in supporting goods supply (Engelseth, 2009). Hammervoll (2014) pointed out that logistics should increasingly consider the exchange economy as divergent from the com-monly focused on production economy.

3. THE RESEARCH MODEL

The Context-Mechanism-Outcome (C-M-O) is ap-plied to elaborate the research model in this study. The C-M-O points out contextual factors vital in un-derstanding concepts that may explain price trans-mission and social interactions in a long-linked rice value chain (Gill &Turbin, 2001; Pawson & Tilley, 1997). The C-M-O is proposed to provide a view of modeling causation; i.e., how causation in the “… social world should be constructed” and that the “basic realist formula” is “context + mechanism = outcome” (Pawson &Tilley, 1997).

Three elements that define the context of a social in-teraction are (1) the physical setting or place, (2) the social environment and (3) the activities surround-ing the interaction (World Bank, 2010). A context is used to understand the dynamics of social inter-actions within communities and assessing how its various interactions relate to each other. The context in this study is identified based on social capital an-dused to explore the social interaction phenomena


among rice supply chain actors. Healy and Cote (2001) defined social capital as networks together with shared norms, values, and understandings that facilitate cooperation within or among groups. This definition is supported by the World Bank (2010) and Seragedin and Grootaert (1997), who claimed that social capital refers to a society that includes institutions, networks or relationships, attitudes, values, powers, and norms that govern the interac-tions among actors. The actor’s role consists of the behavior of someone who holds a particular status in using social networks to interact with other ac-tors, who is bound by society’s norms and who has the power to influence the interaction. Therefore, there are four contextual dimensions of social capi-tal in this study, namely: actor’s role, social network, norms, and power.

Mechanism is defined as a social explanation for hu-man behavior that explains the interaction among actors (Prashanth, Marchal, Kegels, & Criel, 2014). Mechanism is identified based on social interaction which refers to an action chosen by an actor. Ac-tors use social contact and communication to influ-ence the actions of other actors. Actors participate through social interaction (1) to obtain the informa-tion about price, condition, and needs; (2) to deter-mine the achievement of actors’ transaction goals (pricing), and (3) to find a comparison which can be used during negotiation. Thus, the mechanism of so-cial interaction is identified through: (1) how infor-mation is used to set a price and to signal the value claiming (Engelseth, 2013) and (2) how the negotia-tion process occurs among actors. The actors’ roles determine the positions of actors, as price setters or price takers during a social interaction. As price set-ters, actors seek information that is used to negotiate and set the price (pricing), contrary to price takers who tend to wait for the information.

Information seeking refers to activities connected to assessing, searching and dealing with information sources, particularly in networked environments (Choo, 1999). During information seeking, actors identify possible sources, differentiate and choose a few sources, locate or make contact with them, and interact with the sources in order to obtain the desired information (Choo, 1999; Wilson, 1999). Choo (1999) stated that the purpose of information seeking resembles a problem-solving or decision-making process. Actors select a source which has a greater probability of providing relevant, usable, and helpful information. The amount of time and ef-fort influence selection and the use of sources that is required to locate, contact, and interact. Pricing as a fundamental information component influences hu-man perception of value claiming (Friedman, 1980).

“Negotiation” is defined as a process among self-in-terested actorsin order to reach an agreement to satis-fy preferences and constraints of the concerned actors involved (Carraro, Marchiori, & Sgobbi, 2005; Sycara & Dai, 2010). As a process, negotiation covers the fol-lowing characteristics: (1) it involves communication among the actors involved, (2) it involves incomplete information, (3) it possibly has conflicting preferences over actions and outcomes, and (4) it is not well struc-tured, in that there are no well-defined rules for creat-ing legal sequences of communication actions (Sycara & Dai, 2010). Alderson’s transvection model (1965) was employed to explore how actors were involved in sorting, seeking and using price information to support a negotiation during price transmission.

Outcomes provide the key evidence to supportthe phenomena (Salter & Kothari, 2014). The outcome of negotiation is shown through the transaction, which consists of the payment process and exchange among actors. The CMO research model is illustrat-ed in Figure 2.


Figure 2. C-M-O Research Model of Social Interaction on Price Transmission

As shown in Figure 2, the actors’ role consists of the expected behavior, rights, and duties of someone who holds a particular status (Thompson & Hickey, 2005). Part of the actors’ role is a local decision-mak-ing event that relies on the interaction between the product and production process, as well as between information and knowledge transformation (Engels-eth, 2012). In the supply chain, a social network is the total web of an individual’s relationships and group memberships that provides linkages between one individual and another (Thompson & Hickey, 2005). Norms represent protocols or rules of behavior and commitments developed by each group to guide members in working together (Young, 2007). Norms makes human beings act predictably in certain situ-ation and often are not written. The behavioral ac-tion norms during actors’ social interaction include monitoringand information sharing. Negotiators may reach some norms when conducting negotia-tion. Society’s norms and values bind actors’ inter-actions and behavior (World Development Report, 2015), while power is defined as resources, ability and capacity to produce an effect or to bring influ-ence to bear on a situation or actors (Dahl, 1957). There are two types of power in terms of resources, namely: allocative resources, which allow actors to control material objects (i.e., natural and physical materials) and authoritative resources (non-material sources of power, which result in the domination of some actors overothers) (Giddens, 1984).

Social interaction is a necessary iterative process con-sisting of (1) information seeking and pricing and (2) negotiations. The information seeking process involves active, via face-to-face, communication and passive information seeking through media informa-tion (Wilson, 1999). Supported by Engelseth (2009) and Alderson’s transvection model (1965), transvec-tion model is able to link pricing as value claiming to information sharing in supporting value creation.

Pricing is part of a social interaction that is associ-ated with aspects of production and exchange con-nectivity (Engelseth, 2012). Following Alderson’s transvection model, pricing includes the elaboration on the actors involved in value creation, how they are interconnected through exchange processes, and how information is used to support the negotiation and interconnection among actors as well as between actors and goods transforming processes. Festinger (1954) argued that information is the prime motive for negotiation. Forget, Monteiro, D’Amours, and Frayre (2008) stated that negotiation is used as a co-ordination mechanism to find an acceptable agree-ment between partners. Phillips, Simsek, and van Ryzin (2014) stated that prices are negotiated among participating actors and are used to determine the final price. A transaction would not occur if the sell-er’s priceis greater than what the buyer is willing-ness to pay and vice versa (Phillips, Simsek, & van Ryzin, 2014). This is consistent to Friedman (1980),


who said that pricing acts as a fundamental infor-mation component that influences human percep-tion of customer value or economics exchange. The social interaction outcome of an agreed negotiation is called a transaction, which covers two activities, namely: payment and exchange.

4. METHOD

This research employed a case study to analyze so-cial interactions during price transmission in a rice supply network. Two main components were stud-ied, namely the long-linked supply network and pricing, with a focus on price transmission and the rice actors’ behavior in the decision-making pro-cess. The method of this research is a case study, which isconsidered suitable by Yin (2013) because it tries to answer the research questions “how” and “why”. This study attempts to understand the so-cial interaction phenomena among actors in the supply chain, and how and why the actors in the agri-food chain interacted and made decisions dur-ing the price transmission. Following Pagell and Wu (2009) and Ketokivi and Choi (2014), a case study fits to the phenomenon of social interaction in describing price transmission.

This study is focused on price transmission, in which social interactions in the rice supply network is re-garded as a unit to be analyzed. Based on a prelimi-nary study, the selection criterion for the region was built on having the complete representatives of differ-ent rice actors in the region and the possibility to trace the price transmission process among rice actors, and the region was part of major contributor to the na-tional rice production. Thus, the Karawang district, in West Java, Indonesia was chosen. The next step is to apply the research model in Figure 2 to the empirical evidence of the rice supply in Karawang District.

All informants in the area were contacted in the field and asked about their willingness to take part t in the study. During the research, observations and in-terviews were conducted to obtain a better under-standing about social interactions in the rice supply network. Semi-structured interviews were used. First, it was important to get complete information about the actors involved in Karawang District rice value chain with the different actors’ roles, and a semi-structured protocol gives the researcher the flexibility to focus on what is unique about each ac-tor’s role in the rice value chain. Second, there was some theoretical supporting for items included in

the protocol such as C-M-O. So, it was important to understand how social interaction and price trans-mission issues were addressed using C-M-O as a contextual approach.

Multiple investigators were employed as part of testing the research validity, because peer review enhances confidence in the findings and allows the case to be viewed from the different perspectives of multiple observers (Yin, 2013). In order to triangu-late, each investigator in this study used the same method, namely in-depth interview, observationand probing. The actors’ gestures in transaction activities were observed and noted, as part ofthe research’s in-ternal validity process. The observation during this research covered watching what the actors did, lis-tening to what they said and sometimes asking for clarification. During observations, some notes and pictures were taken regarding the issues observed. For important issues, probing and active listening were used to obtain more information. The purpose for this was to find the non-verbal essence of trading events (Yin, 2013). Probing included conducting in-depth interviews with respondents.

Nineteen farmers, nine millers, three traders and one wholesaler were interviewed and observed. In total 60 hours of interviews took place. The interviews were conducted on site. Each quote and comment in the Sudanese language was translated into English. The use of a Sudanese translator helped researchers to understand unclear taped conversations taken of several of the interviewees.

The qualitative research validity was taken by ex-ploring the depth, richness and honesty of the data, triangulation, and the objectivity of the researcher (Cohen,Manion, & Morrison, 2005). The internal validity of the study was achieved through all the questions asked to the respondents, based on the research aims and research phenomena. The gener-alization, as part of the external validity, was made by observing similar or the same responses on top-ics from respondents on the interview transcripts.To ensure reliability, several steps were taken dur-ing the study: all questions were asked in a natu-ral voice, using clear wording and the interviews were recorded. Any unclear questions queried by respondents were repeated and no interventions were made by the researcher through gestures or unnecessary comments during the interview ses-sions so that respondents were free to describe their thoughts and beliefs.


5. CASE STUDY: SUB-DISTRICT OF TIRTAMU-LYA, KARAWANG DISTRICT

The Karawang District is located in the nothern part of the West Java Province and covers an area of 1,753.27 km2 or 3.73%. Karawang has fertile soil, suitable for agriculture, which supported by three irrigation areas located in Karawang: North, Central and West Tarum. These irrigations are used for rice fields, brackish wa-ter ponds and electricity. The area of rice paddy fields in the Karawang Districtc overs around 98,079 hect-ares, and the District employs technical, semi-technical and non-technical irrigation systems. The rice supply chain in the District is carried out by various operators. The majority of traders and millers in the District oper-ate through network relationships to ensure not only the smooth flow of rice but also the working capital. The relationships and social networks are largely de-termined by contacts and by the ability to command the buying and selling price. In this study, rice actors, agricultural shop owner, farmers, village intermediar-ies, wholesalers, and agricultural extension workers.

Context: Actors’ Role, Social Networks, Power, and Norm

Actors’ Role

The rice actors in the KarawangDistrict are classified into five operating groups, namely: agriculture shop owner, farmers, village intermediaries, wholesalers, and agricultural extension workers. The input sup-plier is categorized into seed and fertilizer suppliers, agricultural tools suppliers, capital owners and land owners. The five operating groups act as follows. The agriculture shop owner provides inputs to the suppliers, as shown in the following statement:

“We provide the suppliers such inputs as seed, fertilizer, pesticide and tools to be supplied to the rice farmers.” (Saprodi Shop A, Tirtamulya)

The farmers’ role in the rice supply chain starts from the production process, from planting the rice seed until harvesting. Afarmer’s rice field is between 1and 2 hectares. Rice farming activities include planting, growing and harvesting. This is made evident by the following statement:

“Farmer production is planting Ciherang seed, growing and harvesting.” (Farmer C, Pasirmalang)

The farmers’ ability in determining the paddy plant-ing period, rice price increases, and good seed qual-ity is supported by the following statements:

“The price of rice crops increasefrom December for the next three months.” (Farmer A, Tirtamulya)

“The quality of Ciherang seed produces good rice crops.” (Farmer C, Pasirmalang).

The brokers or traders work is based on profit com-mission from such activities as milling, drying and transporting. Brokers, as capital providers for farm-ers, can be found during planting and harvesting. The role of rice mill owners is to process rice crop-sinto milled rice. The transporters collect unhulled paddy or milled rice and transport it to the interme-diaries or wholesalers in central markets. The main activities of traders in sub-districts or villages are to buy rice crops in a certain quantity from farmers and sell the milled rice in various types and quantities to wholesalers. The traders’ role is supported by the following statement:

“We buy rice crops from farmers and transfor-mthem into milled rice.” (Trader B, Tirtamulya).

At the central rice market, wholesalers stated that they buy rice from West Java and Central Java as they have capital to buy various types of rice and rice in large amounts. The transaction at the central market is conducted by cash payment and pricing is based on rice quality (grading and moisture). The wholesalers’ role is stated in the following state-ment:

“We buy rice from many traders coming from West Java and theCentral Java area.” (Whole-saler A, Central Market)

The government extension workers in the District help farmers by giving out free samples of good seed, advising them on how to apply planting pat-terns to maintain soil fertility and conducting an ir-rigation rotation system. The role of agricultural ex-tension workers is as stated as follows:

“We organize an irrigation system to ensure two harvests time per year. We advise farmers how to apply planting patterns for paddy and palawija. These patterns are implemented to avoid pest attacks.” (BP3K, Tirtamulya)

Social Networks

The interviews show that farmer A is interconnected with the agriculture shop in terms of paddy seed, fertilizer and pesticide during planting and harvest-ing, as is described in the following statement:


“I buy seed, fertilizer and pesticide from an ag-riculture shop.” (Farmer A, Tirtamulya)

Famer B in Sukajaya stated that:

“The agriculture shop owner allows credit pay-ment for seed, fertilizer and pesticide.”

Farmer and regular trader networks were apparent through the rice crops transactions. Farmer A stated that, during harvesting, he went to the village trad-ers to offer his rice crops:

“I go to traders’ houses to offer our rice crops.” (Farmer A, Tirtamulya)

Farmer B stated that during the harvesting season, village traders visit his rice field:

“During the harvesting period, the regular vil-lage trader visits our rice field.” (Farmer B, Su-kajaya)

Both farmers A and B confirmed that regular village traders acted as part of their networks.

A similar network was found at trader level. Trader B was linked with particular farmers and either they came to his house or he visited them:

“…I have regular farmers who usually visit my house. But sometimes I visit them in their rice field.” (TraderB, Tirtamulya)

Trader A networks with his regularwholesaler through the milled rice heoffers.

“I deliver milled rice to my regular wholesaler at Central Market.” (Trader A, Tirtamulya)

Power

Farmers do not have enough storage to keep their-rice crops and have little power to allocate resources owing to financial risks, such as: low quality and quantity of rice crops because of a pest attacks dry seasons, or higher production costs. This is con-firmed in the following statement:

“Sometimes we are facing financial loss due to a pest attack, a dry season, a higher input produc-tion cost or low buying price. But, still farmers continue their activities as they are already in the production stage.” (Farmer A, Tirtamulya)

The traders commonly have one or two milling ma-chines, a dryer, and a storage facility with a capacity

for around 5-8 tons ofmilled rice, and they collect rice crops from farmers in their respective areas. Traders have the power to set the price because they have the capacity storage and capital. The following statement confirms:

“Traders in this area have one to two mill-ing machines, one storage with the capac-ity of around 6-7 tons to stock milled rice and rice crops, and they generally have one dryer. (Trader B, Tirtamulya)

Trader C stated that both the moisture of rice crops and the color of seed are used to determine the qual-ity and price offered to the farmers:

“In checking the quality of rice crops, we check the moisture and seed color by taking out around 20 seeds from the rice crops. A clear color indicates that the quality of the rice crops is good.” (Trader C, Tirtamulya).

Wholesalers have both more capital and storage capacity for buying the traders’ milled rice. Whole-salers determined the amount and the quality of rice being offered, as is shown in the following statement:

“We buy rice from traders who frequently come to our place. The buying decision is determined by the rice quality and quantity, price being offered, and capacity storage.” (Wholesaler A, Central Market)

Wholesalers offer a price to traders based on the quality of the milled rice. Wholesalers conduct ran-dom checks on the moisture and color; good quality is indicated by such physical characteristics as color (clear but not white) and shape (round or oval). This is confirmed by the following comment:

“We check the quality of traders’milled rice based on several physical characteristics, such as the moisture, the color and the shape of the milled rice. If all the characteristics are accept-able, then we offer the price.” (Wholesaler A, Central Market)

Norms

Farmers commit to sell crops to regular village trad-ers and conduct buying and selling activities with others, if necessary, using brokers or informants, as described below:


“We prefer to sell our rice crops to our regular traders. We use informants or brokers for sell-ing our rice crops to other village traders when harvests occur simultaneously in several areas at the trader level.” (Farmer B, Sukajaya)

A similar situation also applies to traders, as is stat-ed below:

“The rice crop is supplied by regular farmers in respective areas. Buying from other areas has to be done by villager brokers or rice informants.” (Trader B, Tirtamulya)

The interaction between farmers and traders is based on their belief that they will support each other. The interviews showed that, if farmers are in trouble, as-sistance comes from either a family member or a trader who has built a relationship with them. The relation-ship among actors is not only based on a commercial basis, but on family, religious or ethnical links.

“Assistance is given by either a family member or even a trader if we suffer a financial loss or financial trouble.” (Farmer A, Tirtamulya)

“Family support is important when we encoun-ter difficulties as a farmer, and this will enable us to continue.” (Farmer D, Pasir Malang)

Traders conduct only random checks onthe quality of rice crops as farmers are considered to provide honest and accurate information about their crops’ quality:

“We do not check all the rice crops as we believe that farmers do not mix their rice crops with oth-er lower quality crops.” (Trader B, Tirtamulya)

Traders have the capital for buying and paying in cash for rice crops in the different qualities and quantities being offered by farmers, as is stated in the following statement:

“Rice crops are bought based on the quality and quantity and our storage capacity as well. The transactions are based on an agreed price and paid in cash.” (Trader B, Tirtamulya)

Mechanism: Information Seeking and Pricing, and Negotiation

Social interaction is used by actors to obtain recent information about price and supply conditions. Farmers receive price information from their social network by visiting a trader or other farmers. Farm-ers only tend to seek price information to compare it

with their offering price.

“The price information is brought by traders. Sometimes we check the price by asking other farmers about the price. There is no other infor-mation source.” (Farmer B, Sukajaya)

Traders seek information by sending short text mes-sages to their informants or visiting wholesalers.

“We use the telephone to contact wholesalers or send short text messagesto our informants to get up-to-date price information at the Central Market.” (Trader B, Tirtamulya)

As price setters for traders, wholesalers use price in-formation at the central market to inform traders of the buying price. Wholesalers transmit price infor-mation by sending short text messages to traders or transporters.

“The buying price information at the Central Market is transmitted to traders or sometimes to transporters who deliver rice to our stores. The amount of buying rice supply stocks is based on our storage capacity and quality of rice crops.” (Wholesaler A, Karawang Central Market)

The pricing process between farmers and traders or traders and wholesalers is based on the rice qual-ity and storage capacity. All actors are involved in seeking and using information to support pricing. Traders use value creation as information to deter-mine value claiming to farmers. Traders determine their offer price based on information received from trader network or wholesalers. Traders’ knowledge and experience are used to determine the rice crops’ quality, the rice supply in the market and the profit (i.e profit is defined as how well actors control the costs over revenue). This is supported by the follow-ing statement:

“Current price information from wholesalers or traders is used to determine pricing for farm-ers. The offer price also considers several factors such as quality, quantity, profit and rice supply stocks.” (Trader B, Tirtamulya)

Similar to traders, wholesalers use market price in-formation, rice quality and capacity storage for pric-ing to traders as stated below:

“Pricing to traders is based on current market information, quality of traders’ milled rice, rice stocks and target profit” (Wholesaler A, Kar-awang Central Market)


Based on the interviews, farmers expected prices to increase in every year from December until March, due to the rain season, a different harvesting period, the quality and quantity of rice production and a shortage of rice stock at the market.

“… based on my experience, the price of rice crops increase from December for the next three months.” (Farmer A, Tirtamulya)

Based on the interviews about negotiation, Farmer A stated that the farmers’ actual selling price of the rice crop covers only the farmers’ production and harvest costs.

“Traders check the unhulled paddy quality be-fore giving the price. The production cost in-cludes seed, fertilizer, pesticide, maintenance, and rice crops.” (Farmer A, Tirtamulya)

A transformation process or value creation is used to determine the quality and pricing from traders to farmers, as stated in the following statement:

“I check the quality and quantity of the farmers’unhulled paddy before offering the price. Negotiation is rarely used because most farmers have already had price information. The price offered to the farmers covers three activities: milling, storing and transporting.” (Trader B, Tirtamulya)

The wholesaler pricing process is based on the rice quality offered, as stated by trader B.

“The value creation from the farmers’ rice crops to the traders’ milled rice has an impact on the quality and the offer price to traders. The infor-mation received by traders about the quality of milled rice supports the pricing from traders to wholesalers.” (Trader B, Tirtamulya)

Farmers have little power in negotiations due to the limited resources they own. Thus, the price trad-ers offer to the farmers tends to be approved by the traders. Traders are price setters for the farmers.This situation is evident in the following statement:

“We conduct a price agreement with the farmers. This means when the farmers’offer price is too high, we bargain with the farmers. If the farmers agree with the bargain, then we buy the rice; but sometimes the price is di-rectly agreed without negotiation.” (Trader C, Tirtamulya)

As price setters, traders use their relationship with the farmers, their capital and storage capacity and the quality of rice crops to obtain their excess amount.

“The selling price of the rice crop is based on the quality of the rice crop and my capacity (money and storage). And sometimes I also consider my relationship with regular farmers in determin-ing my buying price.”. (Trader B, Tirtamulya).

Traders in this study also stated that wholesalers have more capital so they can determine both the quantity of rice to buy and the offer price at the market.

“The selling-buying prices are based on the moisture quality and quantity of the milled rice offered by regular traders and my storage capacity.” (Wholesaler A, Karawang Central Market)

Based on observation, both wholesalers and traders exercise information and power (experience, capital, regular relationships, and storage capacity) and net-works in relation to selling actors. Negotiation is based on the quality and quantity of both the rice crops and the milled rice, and this occurs only if the traders’ buy-ing price is lower than sellers’ expectations.

Outcome

The interviews show that sales are not forced on to actors. There is no price-based competition among farmers, traders, and wholesalers. Statements made by all respondents say that transactions and nego-tiation are based on relationships. The payment scheme adopted is cash and carry.

“The payment scheme is cash and carry.” (Farmer A, Tirtamulya)

Sellers (farmers, traders, and wholesalers) under-stand that the buyers’ willingness to pay is based on a pricing process which is dependent on the quality and quantity of both the rice crop and rice milled.

“Payment is based on the agreed price, which is based on the quality and quantity of the rice crops.” (Farmer A, Tirtamulya)

Like farmers, the wholesalers also state:

“The agreement between wholesalers and trad-ers is according to the offer price,which is based on the quality and quantity of the milled rice.” (Wholesaler A, Central Market)


Price Transmission

From the interviewes, ΔPf+ indicates the increas-ing price of the production input at the farmer lev-el, which is countered byΔPt+, which indicates the increasing price at trader level. ΔPt+ indicates the increased price of production input at trader level, which is countered by ΔPw+, which indicates the

increasing price at the wholesaler level. Farmer in-terviewees argued that, when the rice price at the market increases, farmers still did not benefit from the situation. On the contrary, when the market price decreases, the adjustment of the wholesalers’ buying price of rice is fast and directly affects the ad-jacent actors. Figure 4 describes price transmission among actors in the Karawang District.

Figure 4. Price Transmission along the Rice Supply Chain

When the market price increases, wholesalers tend to keep the information hidden due to stocks’ availabil-ity. The social interaction between wholesalers and traders during increased prices is dominated by power (market stock availability and information). Moreover, based on his experience, wholesaler A has the ability to identify the period of increased prices and to set the price to market, as stated in the following statement:

“Based on my experience, prices increase from December to March, but it depends on the rain fall and my storage. Mostly, I buy once the price drops and sell when the price starts to increase. The information about the increased price is ac-cessed by traders through their transporters or short text messagessent by other traders. I do not inform other traders of the increased pric-es as I have enough stocks at the storage. The increased price has an impact on my expected target profit.” (Wholesaler A, Central Market)

On the contrary, when the market price decreases, wholesalers inform traders, as stated in the follow-ing statement:

“But when the market price decreases, we in-form traders and mostly traders will respond by adjusting their offer price.” (Wholesaler A, Central Market)

Traders seek information from wholesalers regard-ing up-to-date price information:

“Usually, wholesalers respond to the increased market price fast and keep the information of the increased price secret. But when the market price decreases, wholesalers respond to this situation immediately by sending short text messages to me or to my transporters.” (Trader B, Tirtamulya)

Traders have no ability to respond to the increased price, but have power to respond to the decreased


price due to their having capital, rice stocks and quality rice stocks by adjusting their offer price to farmers. Traders inform farmers regarding falling prices by immediately sending short text messag-esor by visiting regular farmers’ rice fields. This is stated in the following statement:

“When the market prices fall, wholesalers in-form traders through short text messages and I deliver this information to farmers as well. This can be done through sending short text messages or visiting farmers’ rice fields. The price adjustment made by traders are based on famers’offer price.” (Trader C, Tirtamulya)

Farmer A stated that when the offer price of rice crops increases, due to the increased cost of the pro-duction input, he informs traders by visiting them in their houses or by sending them short text mes-sages. The increase of the farmers’ offer price is not immediately responded to by traders. This situation is reflected in the following statement:

“When both the price of production input and the offer price increases, I inform traders direct-ly through short text messages or during trader visits.” (Farmer A, Tirtamulya)

When the market price increases, wholesalers de-termine their target profit by keeping the price in-formation secret to traders, unless traders ask the wholesalers about it. Moreover, wholesalers are able to store more milled rice when the buying price of milled rice decreases. On the contrary, when the market price decreases, wholesalers inform the trad-ers of the decreased price and adjust the buying price of the milled rice to traders immediately, as is stated in the following statement:

“But when the market price decreases, I inform traders of the decreased price and adjust the of-fer price to traders. Negotiation takes place if the wholesaler feels that the traders’offer price will reduce the wholesalers’ target profit.” (Wholesaler A, Central Market)

Similarly, traders adjust the price according to the information about the decreased price made by wholesalers. The wholesaler receives higher profit than trader as its cost only covers warehouse, trans-portation to customers and labor, while the trader bears several activities such as milling, drying, stor-ing, transportation, and overhead cost. The whole-saler’s capacity storage and number of customers contribute to wholesale profit realization as well.

Based on interview, wholesaler agreed that amount of capital, storage capacity and number of customer of wholesaler were more than trader. It can be stated that wholesaler receives higher profit than trader as stated as follow:

“Negotiation takes place if wholesalers feel that the traders’ offer price will reduce wholesalers receiving a certain amount of profit. The stor-age capacity, quality and capital, up-to-date price information, rice supply in the market and expected target profit determine the adjust-ment of the offer price to traders. I am able to set the market price as I manage market storage. But there is no competition among wholesalers at the Central Market.” (Wholesaler A, Central Market)

“I adjust the offer price against the wholesalers’ offer price, and negotiation takes place if trad-ers feel that the traders’ received profit is lower than expected. The traders’ target profit is ad-justed by considering such factors as transpor-tation cost, milling cost, rice supply, quality, quantity and up-to-date market information. All of these factors have an impact on traders’ margin.” (Trader B, Tirtamulya)

However, the profit gained by traders is better than that received by farmers. Traders determine the offer price, based on the quality of rice, capacity storage of rice, capital, and rices tocks at the market. Farm-ers determine their offer price based on the quantity and quality of the rice crops. The risk of pest attacks and dry season influence the farmers’ production costs and the farmers’ target offer price as well. Farmers are the risk takers compared to traders, as farmers have a limited power to determine their tar-get margin because they have less authoritative (less capital and no storage) and allocative power to set the price, as is confirmed in the following statement:

“Traders bring unhulled paddy, based on the rice crops’ quality, from regular farmers. Qual-ity is important to traders as good milled rice is determined by the good quality rice crops. While getting a good quality rice crops some-times requires more expenses, such as more fertilizer cost, good seed and good monitoring from the planting period until the harvesting period. Therefore, the farmers’ profit is not as big the that of traders, as farmers have less capital to set the price than the trader does.” (Farmer D, Tirtamulya)


Another reason to lowered margin for farmers is due to rice as a perishable good, both wholesalers and retailers are reluctant to increase their buying price as they bear the risk of being left with a spoiled product. This often occurs when rice available abun-dantly after harvesting period, farmers do not have other alternatives to accept lowered selling price.

5. DISCUSSION

The farmers, owing to having limited capital and no storage for their rice crops, have no power to set the selling price. The farmers, as price takers,compare the information they receive with their offering price. The traders or wholesalers use the information they receive to determine their target profit. It can be seen that both the wholesalers and traders receive greater target profit from the rice prices than the farmers. This fact is in line with the claim made by Vavra and Goodwin (2005). Their claim said that traders use their power to employ pricing strategies which result in complete and rapid cost increases but slower and less complete transmission of cost savings.

This study found that pricing is embedded in a sup-ply network; hence profit received by actors in the supply network increases along the rice supply net-work from farmer, through trader, to wholesaler. The study also revealed that several rice actors could receive unfavorable prices, owing to a lower quality of rice crop or pest attacks, which thus affected their target profit. Aramyan and Kuiper (2009) consid-ered the dry season, cost adjustment, and power to be the factors that explain price transmissions. The costs that farmers have to obtain their rice crops in-clude: input production, planting, and maintenance; while the costs that traders have include: milling, drying, storage, and transporting the milled rice. For those actors, the profit they receive depends on both the information about rice prices in the market and the rice stocks of the wholesalers. This condition is supported by French (1997), who stated that market information is embedded on value creation of prod-ucts and this influences actors’ behaviors and deci-sions during interaction processes.

Moreover, the study conducted by Friedman discov-ered that the social interaction between farmers and the village traders had been formed as the traders received rice crops from regular farmers. It showed that pricing is built through regular relationships between the actors, which is a sort of mechanism to make a pricing decision without a centralized direc-

tion, and sometimes without requiring the actors to speak to one another or to like one another (Fried-man, 1980). The pricing process among the actors in the Karawang District enables them to cooperate and maintain their relationships in terms of trans-actions. This research also found that the farmers, traders, transporters and wholesalers valued their social relationships. This situation is supported by Fafchamps and Minten (1999), who showed that a relationship is fairly valuable as a majority of traders reported that it was very difficult for them to find a new supplier if they lost one. Social interaction en-ables actors in a rice supply chain to deal with sup-ply allocation in order to maintain the regularity of rice supply among actors.

This study shows that the price relationship between the farmers and traders tends to be asymmetric, as the farmers argued that the net profit hey receive is lower than the traders. It indicates that the trad-ers use their power, norms, networks and roles to claim value from the farmers. But, the traders said the wholesalers use their power and roles to set the price, i.e., through their capital, rice stocks and in-formation. Thus, the wholesalers have the ability to determine the retailers’ and farmers’ rice pricedue to their allocative (capital and stocks) and authori-tative power (information). This situation is sup-ported by Vavra and Goodwin (2009) and Jensen and Moller (2007), all of whom mentioned that the slow responses among actors are related to storing, transporting, and processing agri-food products, as well as to their adjustment to the condition at retail and the nature of price reporting. Therefore, in agri-food chains, a response to retail prices, to changes in wholesales or farm level prices is not immediate, but distributed over time (Acosta & Valdes, 2014).

In this study, the willingness of the traders to accept the rice crops being offered by the farmers shows the level of relationship between traders and farm-ers. This interaction is based on the norms of reg-ular relationship of both actors in conducting sell-ing and buying activities. This fact is supported by Syahyuti (2008) and Fafchamps and Minten (1999), who stated that an economic activity relies on one’s willingness to take a risk that facilitates transaction and pushes a collective action at the end. Moreover, Syahyuti (2008) said that social norm is an unspoken and unwritten bond among actors, and this situation is also applicable in Karawang, as the farmers and traders said that they used informants or brokers for the transactions outside their village area. This situ-


ation was identified during the interviews and ob-servations of the actors’ networks in the Karawang District. It can be stated that the social norms form acceptable behaviors that are highly valued. With such social norms, the cooperation and relationship among the actors work well without the need for legal or formal regulation to regulate their interac-tions in the Karawang District.

The case illustrates that rice being a physical prod-uct, the value creation was analyzed in sequential order in the rice supply chain. Value creation was used as the information to determine value claim-ing. The study reveals that the traders act as inter-mediaries between the wholesalers and the farmers through established social interactions and trans-actions. By applying the transvection model, sort-ing the intermediary activity is associated with the value creation management that is done through co-ordinating the rice crops of the farmers’ value per-ceptions and their intermediary value perceptions in terms of a transformation process. Value claiming in exchange contains the actors’ perception about the quality and quantity of the rice crops or milled rice, rice stocks, target profit and information exchanges to determine an offer price.

Theoretically, actors should be dominantly and se-quentially interdependent (Alderson, 1965; Thomp-son, 1967), but this case study indicates that the ac-tors’ activities are guided by social interactions that are varied, depending on their institutionalized norms of conduct. The transvection model is con-sidered to contribute in giving an understanding of value rice creation and value claiming among the rice actors in the Karawang District. The uncertainty of both the rice stock and the price are controlled in the Karawang case by the social network, power, and norms in the supply network. In other words, it is controlled not through technical integration but through developed social interactions; network and norms are used to deal with obtaining the assurance of supply allocation, and power is used to determine offer prices (Fafchamps & Minten, 1998, 1999; Sya-hyuti, 2008).

Therefore, how actors think, decide, and act dur-ing an interaction process and a price transmission process helps to describe and explore the pricing mechanism among actors. Patterns of social inter-actions shown through a price transmission among actors can be developed and studied analytically. The achievements of farmers’ and traders’ com-mon goals can be identified through transaction ac-

tivities. Friedman’s study found that during social interactions, actors not only transmit information but also distribute income through value claiming (Friedman, 1980). In this case study, the actors tried to find other actors within their social networks to get and simplify the information process by focus-ing on pricing and negotiation. Thus, social inter-action among actors during price transmission in-cluded: discussing the relationship between prices at market, conducting information seeking, setting pricing, and conducting negotiation processes. This study also shows that social interactions are influ-enced by roles, networks, norms and power. Power is concerned with resources or the ability to bring in-fluence to bear on a situation or actors, while norms are concerned with the commitment developed among the actors in their selling and buying activi-ties. Thus, the patterns of their social interactions are associative interaction ones, which encourage the achievement of accommodation and adjustment. Therefore, this case study provides an explanation of actors’ social interactions on a price transmission along a rice supply chain.

6. CONCLUSIONS

This research reveals that the price transmission among actors is guided by a social interaction which is varied among actors and is dependent on the es-tablished social norms, power, and network as well. This case study shows that information at the central market was obtained through personal contacts with other wholesalers, traders, farmers, or through mes-sengers, while the role of public sources, such as the newspaper, radio or public services is marginal. The negotiations show that value claiming has been in-fluenced by regular relationships, which are shaped by social interaction, power and norms of social net-works among the actors. In the absence of formal institutions, social capital seems to play its role, i.e., in mitigating the rice-related risks such as high price fluctuations, pest attacks and weather anomalies.

It can be stated that there are three reasons why the actors participate in social interactions. They are (1) to obtain information about the up-to-date condition, quantity needs, and offer price of the rice crops in a more confident transaction, (2) to become involved from the beginning to create a sense of belonging and sense of responsibility; and (3) to achieve their target profit in their own transactions. The study shows that daily social interactions among the ac-tors are followed by social actions.


This case study has contributed in the creation of methods for exploring a price transmission process among actors’ social interactions by integrating the transvection model into a long-linked rice supply chain. The discussion describes and analyze the case of social interaction during price transmission. The most important finding of this case study is that the asymmetric price roles cause adjustments to behav-iors during the interactions. This situation occurs when the market institution is weak, which makes traders and wholesalers become more efficient in developing their credible suppliers and networks to allow more simple ways to trade: granting and receiving transaction offers, forwarding orders and simplifying quality checking. Therefore, the use of the transvection model helps to explore the social phenomena during the actors’ interactions through the value creation (product transformation) and pricing process (value claiming). This study contrib-utes several outcomes, namely (1) generating a new approach to explain and identify the factors affect-ing the price transmission and (2) describing and ex-plaining the social interactions among the actors in the price transmission phenomena.

Logistics researchers argue that managing a supply chain is not the same as operating a machine. The supply chain is a social structure where interactions between humans create solutions, which represents both the patterns of exchange and production. The economy of production is vital in rice supply since this is a form of physical distribution where features of rice transformation are directly associated with value creation. The use of the C-M-O as a contextual approach provides a better understanding on so-cial capital as a context, and social interaction as the mechanism to explain price transmission among ac-tors. This research provides evidence that integrat-ing the transvection theory into social interaction phenomena during a price transmission is impor-tant to create knowledge about value of norms and power by which actors are facilitated to get a shared understanding during price transmission. The ben-efits of social capital for the rice actors are shown in a reduction in the high transaction costs as trad-ers and farmers are able to deal with each other in a more trustworthy manner by granting and receiving price offers, in price information exchanging, and in economizing quality inspections.

The evidence indicates that the relationship between the farmers and the transporters helps the traders to economize their transaction costs. As for the re-

lationship between the traders and the farmers, it helps them to receive a better profit. However, in agricultural commodity trade, the presence of social networks enables the traders to reduce their trans-action costs in a situation of imperfect information while gaining higher profit. To the academia, this research could assist more scrutinized research and aid in analyzing a price transmission in an agri-food supply chain. In order to empower rice farmers, re-searchers must consider the existence of social inter-actions between farmers and village traders (traders, millers, and transporters). In this case study, the so-cial value is found when analyzing the agricultural commodity trade.

The use of a single case study addresses the phenom-ena of social interactions in price transmission along a specific rice value chain. The description might vary when applied to other value chains due to different social settings. Further work should be focused on confirmatory studies where multiple case studies are conducted to confirm the price transmission phe-nomena in a long-linked rice supply chain. While the result of this study demonstrates the significant effect of social interaction on price transmission in the rice commodity of an economy, it would be useful to ex-tend the study to other commodities or sectors.

7. REFERENCES

Acosta, A., & Valdes, A. (2014). Vertical price transmission of milk prices: Are small dairy producers efficiently integrated into markets? Agribusiness Journal, 30(1), 56-63.

Alderson, W. (1965). Dynamic marketing behavior: A functional-ist theory of marketing. Homewood, IL: Richard D. Irwin.

Alderson, W., & Martin, M.W. (1965). Toward a formal theory of transactions and transvections. Journal of Marketing Re-search, 2(2), 117-127.

Aramyan, L.H., & Kuiper, M. (2009). Analyzing price transmis-sion in agri-food supply chains: An overview. Measuring Business Excellence, 13(3), 3-12.

Bichler, M., Kersten, G., & Strecker, S. (2003). Toward a struc-tured design of electronic negotiations. Group Decision and Negotiation, 12(4), 311-335.

Bunte, F. (2006). Pricing and performance in agri-food supply chains. In C.J.M. Ondersteijn, J.H. M. Wijnands, R.B.M. Huirne, & O. van Kooten (Eds.), Quantifying the agri-food supply chain (pp. 37-45). Rotterdam: The Netherlands: Springer.

Carraro, C., Marchiori, C., & Sgobbi, A. (2005). Advances in ne-gotiation theory: Bargaining, coalitions and fairness [Policy Working Paper No 3642]. Washington, DC: The World Bank.

Choo, C.W. (1999). Closing the cognitive gaps: How people pro-cess information. London: UK: Financial Times of London.


Cohen, L., Manion, L., & Morrison, K. (2005). Research methods in education (5th ed.). London, UK: Routledge Falmer.

Dahl, R.A. (1957).The concept of power. Behavioral Science, 2(3), 201-215.

Dutta, S., Zbaracki, M.J., & Bergen, M. (2003). Pricing process as a capability: A resource-based perspective. Strategic Manage-ment Journal, 24(7), 615-630.

Engelseth, P. (2009). Food product traceability and supply net-work integration. Journal of Business and Industrial Market-ing. 24(5/6), 421-430.

Engelseth, P. (2012). Modelling transformations in a complete fresh food value network. In H. Jodlbauer, J. Olhager, & R.J. Schonberger (Eds.), Modelling Value (pp. 373-391). Selected papers of the 1st International Conference on Value Chain Management. Berlin, Germany: Physica-Verlag.

Engelseth, P. (2013). Multiplex uses of food product standards. International Food and Agribusiness Management Review, 16(2),75-94.

Engelseth, P. (2016). Aligning end-to-end seafood supply through a series of markets. International Journal of Produc-tion Economics, 173, 99-110.

Engelseth, P., & Felzensztein, C. (2012). Intertwining relation-ship marketing with supply chain management through Alderson’s transvection. Journal of Business and Industrial Marketing, 27(8), 673-685.

Fafchamps, B., & Minten, B. (1998). Relationship and traders in Madagascar [Discussion Paper No 23]. Stanford, CA: Depart-ment of Economics, Stanford University.

Fafchamps, B., & Minten, B. (1999). Social capital and the firm: Evidence from agricultural trade. Social Capital Initiative [Working Paper No 17]. Washington, DC: The World Bank.

French, N. (1997). Market information management for bet-ter valuations part I: Concepts anddefinitions of price and worth. Journal of Property Valuation and Investment, 15(5), 403-411.

Friedman, M. (1980, February 21). The power of the market. The Listener, 229-231. London: Immediate Media Company Lon-don Limited.

Forget, P., Monteiro, T., D’Amours, S., & Frayre, J.M. (2008). Collaborative agent-based negotiation in supply chain plan-ning using multi-behaviour agents [Working Paper No CIR-RELT-2008-54], Québec, Canada: Inter-university Research Centre on Enterprise Networks, Logistics and Transporta-tion (CIRRELT).

Giddens, A. (1984). The constitution of society: The outline of the theory of structuration. Oxford, UK: Polity Press.

Gill, M., & Turbin, V. (2001). Evaluating realistic evaluation: Evi-dence from study of CCTV. Crime Prevention Studies, 10, 179-199.

Godes, D., Mayzlin, D., Chen, Y., Das, S., Dellarocas, C., Pfei-ffer, B., Libai, B., Sen, S., Shi, M, &Verlegh, P. (2005). The firm’s management of social interactions. Marketing Letters, 16(3/4), 415-428.

Hammervoll, T. (2014). Service provision for co-creation of val-ue: Insights from exchange and production economy per-spectives. International Journal of Physical Distribution and Logistics Management, 44(1/2), 155-168.

Heath, R.L., & Bryant, J. (2012). Human communication theory and research: Concepts, context and challenges (2nd ed.). New York: Routledge.

Jensen, J.D., & Møller, A.S. (2007). Vertical Price Transmission in the Danish food marketing chain. Copenhagen: Institute of Food and Resource Economics, University of Copenhagen.

Ketoviki, M., & Choi, T. (2014). Renaissance of case research as a scientific method. Journal of Operations Management, 32(5), 232-240.

Krendl, K.A., Ware, W.H., Reid, K.A., & Warren, R. (1996). Learn-ing by any other name: Communications research traditions in learning and media. In D.H. Jonassen (Ed.), Handbook of research for educational communications and technology (pp. 93-111). New York: Macmillan.

Lin, N. (2001). Social capital: A theory of social structure and ac-tion. London, UK and New York, USA: Cambridge Univer-sity Press.

Manski, C.F. (2000). Economic analysis of social interactions. The Journal of Economic Perspectives, 14(3), 115-136.

Mankiw, N.G., & Taylor, M.P. (2014). Economics. London, UK: Cengage Learning.

Healy, T., & Côté, S. (2001). The well-being of nations: The role of human and social capital. education and skills. Paris, France: Organisation for Economic Cooperation and Development.

Orton, J.D., & Weick, K.E. (1990). Loosely coupled systems: A re-conceptualization. The Academy of Management Review, 15(2), 203-223.

Pagell, M., & Wui, Z. (2009). Building a more complete theory of sustainable supply chain management using case studies of 10 exemplars. Journal of Supply Chain Management, 45(2), 37-56.

Pawson, R., & Tilley, N. (1997). Realistic evaluation. London, UK: Sage.

Penrose, E. (1959). The theory of the growth of the firm. Oxford, UK: Oxford University Press.

Phillips, R., Simsek, A.S., & van Ryzin, G. (2014). Estimating buy-er willingness-to-pay and seller reserve prices from negotia-tion data and the implications for pricing [Working Paper No 2014-1]. New York: Center for Pricing and Revenue Manage-ment, Columbia Business School.

Prashanth, N.S., Marchal, B., Kegels, G., & Criel, B. (2014). Evalu-ation of capacity building program of district health manag-ers in India: A contextualized theoretical framework. Front Public Health, 2(89), 1-14.

Salter, K., & Kothari, A. (2014). Using realist evaluation to open the black box of knowledge translation: A state-of-the-art re-view. Implementation Science, 9(115), 1-14.

Scheinkman, J.A. (2008). Social interactions. In S. Durlauf, & L. Blume (Eds.), The new palgrave dictionary of economics (2nd ed.). New York: Palgrave Macmillan.


Serageldin, I., & Grootaert, C. (1997). Defining social capital in-tegrating the economist’s and the sociologist’s perspective. Washington, DC: The World Bank.

Syahyuti, S. (2008). The role of social capital in agricultural trade. Forum Penelitian Agro Ekonomi, 26(1), 32-43.

Sycara, K., & Dai, T. (2010). Agent reasoning in negotiation. In D.M. Kilgour, & C. Eden (Eds.), Handbook of Group Deci-sion and Negotiation (pp. 437-451). New York: Springer.

Thompson, J.D. (1967). Organizations in action. New York: Mc-Graw Hill.

Thompson, W.E., & Hickey, J. V. (2005). Society in focus: An in-troduction to sociology (5th ed.). Boston, MA: Pearson.

Turner, J.H. (1988). A theory of social interaction. Stanford, Cali-fornia: Stanford University Press.

Vargo, S., & Lusch, R. (2004). Evolving to a new dominant logic for marketing. Journal of Marketing, 68(1), 1-17.

Vavra, P., & Goodwin, B.K. (2005). Analysis of price transmission along the food chain [Working Paper No 3], Paris: OECD Publishing.

Wilson, T.D. (1999). Models in information behaviour research. Journal of Documentation, 55(3), 249-270.

World Bank. (2010). Negotiation.Communication for Gover-nance and Accountability Program (CommGAP). Washing-ton, DC: The World Bank.

World Development Report. (2015). Mind, society, and behavior. World Bank Group. Washington, DC: The World Bank.

Yin, R.K. (2013). Case study research, design and method (5th ed.). Thousand Oaks, California: Sage.

Young, H.P. (2007). Social norms [Economics Series Working Pa-per No 307]. Oxford: University of Oxford, Department of Economics.

Submitted 19.01.2016. Approved 20.06.2016 Evaluated by double blind review process.

Integrating Supply Chain and Production Chain: a Genesis in the Ethanol Industry

Eder Moreno Ferragi Professor at Universidade Paulista - Santana de Parnaíba - SP, Brasil

[email protected]

ABSTRACT: Although frequently used interchangeably, the concepts of supply chain and produc-tion chain are considered in this study as two distinct and complementary theoretical frameworks for the conception of a data-mapping model titled SCMap – Supply Chain Map. The model’s objective is to identify and assess real chains of companies and products. An application in the agribusiness sec-tor, starting with a specific sugarcane processing plant and the biofuel ethanol is performed. The SC-Map establishes a structured and integrated manner of linking products and companies, considering three different categories of relationships: (1) between companies – supply chain approach; (2) between products – production chain approach, and; (3) between companies and products – regarding commer-cial practices in the corporate environment. Grounded on Graph Theory and Social Network Analysis – SNA, the software UCINET and NetDraw application are used to draw the maps and quantitatively assess the centrality of each company and product relative to the whole chain.

Keywords: Supply chain maps, product maps, production chain, chain visibility, agribusiness.


129

Ferragi, E. M.: Integrating Supply Chain and Production Chain: a Genesis in the Ethanol IndustryISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 1 p 129 – 146130

1. INTRODUCTION

Though often applied with similar meaning, sup-ply chain (SC) and production chain (PC) are two terms that define distinct approaches widely used by academics and professionals for analysis of inter-business activities and relationships. The SC ap-proach, linking companies, is associated to business management from the 1980s in order to address the integration of the internal company functions of purchasing, production, sales and distribution (Oli-ver & Webber, 1992); but as the concept applications evolve, the analysis begins to incorporate the exter-nal integration among companies with their suppli-ers and their customers at all levels, from producers of raw materials to final consumers (Lambert, Coo-per, & Pagh, 1998). As a result of the expansion of the method’s range, the concept of the network, in-stead of a chain, starts to reflect more accurately the complex relationships of the business environment in the scenario of global production and trade (Bra-ziotis, Baurlakis, Rogers, & Tannock, 2013; Carter, Rogers, & Choi, 2015). However, for the purposes of this analysis, the term supply chain – SC is used both in the restricted sense of a string and in the ex-panded sense of a network.

On the other hand, the PC approach, linking prod-ucts, is developed as an instrument of systemic vi-sion (Castro, Lima, & Cristo, 2002), related to the concepts of filière, commodity chains and systems of provision, which attempt to describe the complex relationships between production and consumption (Bair, 2009) and contributes to the understanding and performance of comprehensive production sys-tems with special application in agribusiness (Bat-alha, 1995). The term is widely used in Brazil to refer to a specific industry or sector for instance: the meat chain or the orange chain.

Individually, both SC and PC conceptual frameworks posit consistent contributions when dealing with the issues they respectively address. However, when considered in the real business environment, where it is usual for a particular company to produce and/or sell a varied and large number of products, often belonging to different chains (Braziotis et al., 2013; Mentzer et al., 2001), either the SC or PC approach, singly, demonstrate limitations to dealing with the relationships linking companies and their products.

The purpose of this paper is to provide an instrument which is able to identify and dispose companies and products as well as their relationships relative to the

chains and networks. The specific objectives of the study are: (1) to create a scalable and structured way of showing companies, products and their relation-ships in real corporate environments, based on the application of the complementary approaches of supply chain and production chain; and (2) to as-sess the positioning of products and companies in relation to real chains and networks in which they are embedded, using the quantitative indicators that measure the centrality: degree, closeness and be-tweenness, from Social Network Analysis (Borgatti, Mehra, Brass, & Labianca, 2009).

Three types of relationships are considered and represented in an integrated and complementary manner: (1) relationships of companies, among customers and suppliers from the SC approach; (2) relationships of products, among components and derivatives, from the PC approach, and (3) relation-ships of companies and products, among firms and the products which they produce and/or sell in the market, typical of a corporate trading environment.

A real application of the SCMap was implemented, based on a focal firm related to a focal product in the agribusiness sector: a sugarcane processing plant lo-cated in the region of Ribeirão Preto, in the state of São Paulo, here designated as EP01 (ethanol plant number one); and the biofuel ethanol. The choice of ethanol, or ethyl alcohol, is due to the promi-nent role that biofuel occupies in the global scenario since it is considered an alternative source of renew-able energy in the face of exhaustion of fossil fuel sources, especially oil and natural gas (Pimentel et al., 2008); also due to the role of Brazil as the sec-ond largest producer, which along with the United States, accounts for approximately 90% of all etha-nol produced in the world (Renewable Fuels Asso-ciation [RFA], 2013).

Considering the focal company (EP01) and the fo-cal product (ethanol), the SCMap’s model imple-mentation is initiated by three basic procedures: (1) products’ association, through a product tree structure following the PC approach; (2) companies’ association, through a customer supplier structure following the SC approach; and (3) association of the products to the companies which supply them, through a relational structure following the SC and PC approaches together. The use of the NetDraw app (Borgatti, 2002) version 4.14, together with the UCINET ® software for Windows, allows the rep-resentation and assessment of companies, products and their respective relationships.



The idea of ties and links forming chains, and later networks, has been the subject of numerous analy-ses and results in different conceptual frameworks under no less varied terminology, in order to con-tribute to the understanding of the ways in which people, processes, goods and places are connected to each other, and the consequent influence that such configurations impacts in production systems at local, regional and global levels.

This literature review addresses two distinct chains and networks approaches applied in the study: Sup-ply Chain – SC and Productive Chain – PC. It also presents a review of the concepts of Social Network Analysis – SNA as the foundation of the conceptual framework of the SCMap model.

2.1 Supply Chain

The term Supply Chain Management – SCM was in-troduced in 1982 by Keith Oliver, vice president of the London office of international consultancy, Booz Allen Hamilton (Bair, 2009; Frankel, Bolumole, Elt-antawy, Paulraj, & Gundlach, 2008; Houlihan, 1984; Oliver & Webber, 1992; Stock, 2009).

Mentzer et al. (2001) define the term supply chain as “a set of three or more entities (organizations or indi-viduals) directly involved in the upstream and down-stream flows of products, services, finances, and/or information from a source to a customer”. Similar concepts are mentioned in the literature, such as “set of firms” passing material forward (Londe & Mas-ters, 1994), “alignment of firms” that bring products or services to the market (Lambert, Stock, & Ellram, 1998), and “network of organizations” linked in both directions, upstream or downstream, providing sev-eral processes and value-adding activities to the final consumer (Christopher, 1992).

Albeit with slight variations, it is evident that the fo-cus of this approach is organizational entities and not products. It emphasizes, therefore, the firms’ relationships common among customers and sup-pliers; and not products’ relationships between components and derivatives, inherent to the trans-formation processes of manufacturing.

According to Mentzer et al. (2001) three levels of SC are identified considering its complexity: 1) the “direct supply chain”, comprising a company and its immediate suppliers and customers, 2) the “ex-

tended supply chain”, comprising a company, its immediate suppliers and customers, the suppliers of the immediate suppliers and customers of the im-mediate customers and so on, and 3) the “ultimate supply chain” involving a company, its immediate suppliers and customers, all suppliers and also all involved in the upstream and/or downstream flows of products, services, finances and/or information.

From the mechanical concept of a chain consisting of elements that are connected to each of its two imme-diate neighbors and which together form a strong flexible connection, the term supply chain conveys the idea of linearity when in reality it is observed that real SCs are more like expandable networks (La-zzarini, Chaddad, & Cook, 2001) that integrate mul-tiple business and relationships (Lambert, Cooper et al., 1998), or supply networks composed of sets of SCs (Lamming, Johnsen, Zheng, & Harland, 2000). As a result of the expansion of the borders of this approach, the concept of network instead of chain starts to reflect more broadly the complex relation-ships of the business environment in the setting of production and global trade (Braziotis et al., 2013; Carter et al., 2015).

Even recognizing that the term supply network is better suiting than the term supply chain – SC, the latter will be used in the present work just for the purpose of maintaining the original terminology; but it is accepted that the resulting SCMap is most similar to a network instead a chain configuration with linear connections, or in other words, it is more similar to a “supply chain network structure”, as coined by Lambert, Cooper et al. (1998).

In order to build theory and develop normative tools and methods for a successful SC management, Lambert, Cooper and co-author (1998) present a conceptual framework covering the combination of three strongly interrelated elements: 1) the structure of SCs, 2) the SC business processes, and 3) the man-agement of SC components.

Placing the focus of this analysis just on the chain structure, which is understood as the network chain members and their connections and relationships, the present study considers just the two primary aspects of the network structure proposed by the authors: a) identification of members of SC, and b) structural network size. Assuming that a well-devel-oped system of metrics (that is, it is able to evaluate the performance of SCs as a whole) can increase the chances of success of the entire chain with regard to


aligning processes across multiple companies focus-ing on market sectors more profitably and achieving competitive advantage, Lambert and Pohlen (2001) present a new conceptual framework for the devel-opment of metrics to CSs analysis which consists of seven steps.

They are: 1) map the SC, 2) analyze every relation-ship, 3) develop statements of profit and loss, 4) realign processes in SC, 5) align non-financial mea-sures with P & L, 6) make comparisons between companies and, 7) replicate.

Without losing sight of the integrated role that each one of those elements and steps play in their respec-tive conceptual frameworks (Lambert, Cooper et al., 1998; Lambert & Pohlen, 2001), the SCMap model specifically focuses on the structure of the SC by iden-tifying the members who comprise it and mapping them as a chain or network. Unlike other mapping approaches and methods found in academic works whose intent to incorporate various intra company levels of processes in a single consolidated instru-ment of analysis (Miyake, Torres, & Favaro, 2010), the SCMap model seeks just to contribute to the de-velopment of a structured and scalable model, able to identify and situate companies and their relation-ships in the real business environment with the aim of highlighting development opportunities in dyadic relationships of firms and in inter-organizational net-works (Harland, Lamming, & Cousins, 1999).

2.2 Production Chain

According to Jennifer Bair (2009), the concept of production chain – PC is originally based on the filière approach, introduced in the 1960s by French researchers at the Institut National de La Recherche Agronomique and the Centre de Cooperatión Inter-nationale en Recherche Agronomique pour le Dével-oppement. Frequently used interchangeably with the terms “commodity systems analysis” (Friedland, 1984) and “systems of provision” (Fine & Leopold, 1993), the researchers seek to describe “the sequence of processes by which goods and services are con-ceived, produced and brought to market”.

In Brazil, the term filière, used as a synonym for production chain and special application in the ag-ricultural sector, also describes a system that is the “chain of technical operations (downstream and up-stream) reflecting the sequence of processing raw materials into finished products” (Batalha, 1995). Based on Jean Parent’s study (1979), which defines

the term as the sum of production and business processes needed to pass one or more “raw materi-als” to a “final product” until the product reaches the final consumer; Castro et al. (2002) summarize the concept as a “system, in which the various ac-tors are interconnected by material flows of capital and information, in order to supply a consumer end market with system products”.

It should be emphasized that, although based on the same chain design that connects actors, activi-ties and products, it is clear that the focus of the PC approach encompases the products, whether raw materials, intermediate products or even products ready for consumption. In this approach, the firm’s position within the system can be identified by ob-serving the manufacturing process for which it is responsible in preparing the final product (Batalha & Silva, 2008).

Interestingly, instead of companies as depicted in the SC illustrations, the PC illustrations describe the transformation processes performed by these com-panies; this fact actually makes more sense, taking into account that the focus of analysis in this case are products.

The systemic character of the approach is another as-pect worth mentioning in this study. While the con-cept of SC evolves from a linear supply chain to a net-work chain comprising interconnected organizations, the concept of PC also growsthrives from the idea of a chain to a systemic approach, able to contribute to the analysis of complex production systems.

The systemic vision of agriculture, coined as agribusi-ness, by John Davis and Ray Goldberg (1957) from Harvard University, was introduced in Brazil by Dé-cio Zylbersztajn (1994) from the São Paulo University under the term “agroindustrial complex” and later “agribusiness system”. The broader aspect of the ap-proach enables the identification of other subsystems that compose it. Castro et al. (2002) argues that the agribusiness system consists of several production chains, or subsystems of the agricultural business.

Batalha and Silva (2008) term an agro-industrial sys-tem – SAI as a set of activities carried out in agri-cultural production from the production of inputs until the arrival of the final product to the consumer, but is not associated with any raw material or spe-cific product. However, when the focus of analysis is a specific raw material, the authors use the term “agro-industrial complex”; on the other hand, when


the starting point of analysis focus on a specific fin-ished product, the term used is “agro-industrial pro-duction chain”.

While acknowledging the application of the ap-proach as originating from the agricultural sector, by virtue of its systemic character, Castro et al. (2002) argue that the model of PCs can “be applied to pro-ductive activities other than agriculture, such as the production of industrial products”. The term is also used as one of its institutional action tools in Brazil, by the Ministry of Development, Industry and For-eign Trade, responsible for implementing the eco-nomic and administrative policy related to industry and commerce. The PC approach emphasizes the systemic view, since it allows comparison of a chain made up of links, each link in the chain in turn is equated with a particular sector of the production, which occurs in different industries responsible for independent operations and technologically sepa-rated (Ministério do Desenvolvimento, Indústria e Comércio Exterior [MDIC], 2015).

Particularly, in the food sector, where the issue of health is key, the focus on products justifies the PC approach, as it can contribute to traceability from components to the final product (Dekker, Verkeerk, & Jonjen, 2000; Miraglia et al., 2004). The above examples underscore the relevance of the SCMap model for the analysis of connected and sequential operations in different industries.

The analytical meso feature of the PC approach should also be highlighted. The approach is situated between the two great bodies of economic theory: microeconomic analysis, which considers the basic elements of the economic system; and macroeco-nomic analysis, which considers the major economic aggregates (Batalha, 1995).

2.3 Integrating Supply Chain (SC) and Production Chain (PC) Theory

As mentioned earlier in this literature review, two facts become evident when SC and PC theory are considered in an integrated way: 1) the micro ana-lytical feature of the SC approach, focusing on the microeconomic environment composed of a com-pany, its customers and suppliers and their respec-tive relationships; and 2) the meso analytical feature of the PC approach, where the products are viewed in an aggregate way and not as individual atomic products; when referring to, for example, the beef chain or even the automobile chain, the idea is to ex-

plain a wide variety of meats or even different kinds of automobiles and their components, which in turn are produced by a set of aggregate companies in the meat industry or automotive industry, according to the case.

If, as stated by Batalha and Silva (2008), on the one hand the PC approach is shown useful as a tool for the development of public and private sector poli-cies as a result of its meso analytical feature; on the other hand it is less efficient as a tool in the manage-ment of individual companies and their positions in their respective chains.

In order to move towards enabling the micro ana-lytical feature of the SC approach to communicate with the meso analytical approach of the PC, it is necessary that the subsystem, called the productive chain, be also decomposed into another micro ana-lytical subsystem at the product level, allowing spe-cific products to be linked with specific companies. For this purpose, the SCMap model employs two different and complementary concepts widely used to standardize the description of materials and pro-duction processes: 1) the Harmonized Commodity Description and Coding – HS, which in Brazil led to the Mercosul Common Nomenclature – NCM/HS; and 2) the “product structure tree” or bill of materi-als – BOM.

Able to establish a common language, to refer to products between two or more agents, the Harmo-nized Commodity Description and Coding System, also known as the Harmonized System HS, consists of a standard set of names and numbers for classify-ing traded products, developed and maintained by the World Customs Organization – WCO and ac-cepted by more than 200 countries and economies. “It comprises about 5,000 commodity groups; each identified by a six digit code, arranged in a legal and logical structure and is supported by well-defined rules to achieve uniform classification” (World Cus-toms Organization [WCO], 2015). In the South Com-mon Market – MERCOSUL, comprised of Brazil, Argentina, Paraguay, Uruguay and Venezuela, the HS is used with the addition of two digits for the purpose of a more detailed characterization of the products, resulting in the Mercosul Common No-menclature – NCM/SH. But for scope delineation, this study only considers the classification of prod-ucts with the first four digits of the HS, two for the chapter and two related to the position the product occupies in relation to everything else.


The BOM is a formally structured list for an ob-ject, semi-finished or finished product, which lists all the component parts of the object with the name, reference number, quantity, and unit of measure of each component, which captures the end products, its assemblies, their quantities and relationships. The academic contribution and both the opportunities and challenges related to these lists is significant (Choi, Dooley, & Rungtu-sanatham, 2001; Giménez, Vegetti, Leone, & Hen-ning, 2008; Li, Yang, Sun, Ji, & Feng, 2010; Pathak, Dilts, & Mahadevan, 2009; Vegetti, Leone, & Hen-ning, 2011; Vidal & Goetschalckx, 1997; Yan, Yu, & Cheng, 2003). Despite the discussion about the use of the BOM in inter companies relationships, in the SCMap model the list is considered only as a means of determining relationships between two classifications of products: 1) the products which companies provide to the market, or “top prod-ucts” (Hegge & Wortmann, 1991) and, 2) the prod-ucts which these companies acquire from the mar-ket in order to produce the former, or “primary products” (Hegge & Wortmann, 1991). Processes and production methods as well as intermediate products resulting from different stages of the in-side company manufacturing process, also called

“subassemblies” (Hegge & Wortmann, 1991) are not part of the scope.

Among the difficulties in the application of the SC theoretical concepts in the corporate environment, there is the fact that a company belongs to different chains. Although Lamming et al. (2000) attempt to classify supply networks according to the type and characteristics of the product that is being considered, their respective representations fail to show real situ-ations where a company can produce more than one product and therefore belong to more than one SC.

The difficulties encountered in implementing the concepts and definitions of conceptual frameworks of SCs in real environments are mentioned in Faw-cett and Magnan’s (2002) study, where “few compa-nies have adopted and disseminated the formal defi-nition of SCM. And they’ve even less meticulously mapped their supply chains, so that they can know who the suppliers of their suppliers and customers of their customers are” (Fawcett & Magnan, 2002, p. 340). The authors conclude that integration to the ex-tent proposed by academic concepts was perceived as very rare – constituting more a theoretical ideal than a practical reality, as shown in Figure 1.

Figure 1. Different views of supply chain integration

Source: Fawcett and Magnan (2002).


The same view is shared in the work of Kim, Choi, Yan and Dooley (2011) where the authors point out the low quantity of actual supply networks studies, given the difficulty of obtaining data and also the lack of effective tools for mapping and treating such data. According to Lamming et al. (2000), a review of the supply networks literature, reveal that none of the existing approaches correctly addresses the practicalities of day-to-day life lived by the profes-sionals working in the area.

However, this situation gets new contours in the first decade of the 21st century with the development of modern methods and collection technologies, pro-cessing and analysis of large amounts of data via computer systems and the internet (Barabási, 2012). Based on the study and analysis of social networks – SNA (Social Network Analysis) in the social sci-ences, a stream of SC researchers recognize the ac-ceptance of the fundamentals and instruments of the SNA approach as particularly adjusted to the study of the inter-relationships in a SC (Borgatti & Li, 2009; Carter et al., 2015; Kim et al., 2011; Mueller, Buergelt, & Seidel-Lass, 2008; Talamini & Ferreira, 2010).

2.4 Social Network Analysis

Social Network Analysis – SNA, belongs to a field of sociology that studies sets of individuals and the links between them, based on graph theory, alge-bra and statistics. Starting from sociometric studies prepared by psychiatrists Jacob Moreno and Helen Jennings, who in 1932 tried to understand the drop-out students in a school in New York, it was pos-sible to associate the theme Physics and Sociology and conclude that such behavior was more related to the position of students in the network that they were part of, than to their individual characteristics (Borgatti & Li, 2009).

In the following years, the expansion of the use of the SNA approach to other fields occurred explo-sively, the analysis was used to study the behav-ior of genes and other cell components, counter-terrorism, prediction and analysis of epidemics, mapping neural networks, and the administration of intra and inter-organizational structures, among others (Barabási, 2012).

Early studies of graph theory, a subarea of mathe-matics, that studies the combinatorial relationships between objects of a given set, focused on network analysis, dating from 1735 (Barabási, 2012). Sig-nificant contributions were the work of Erdős and

Rényi (1959), who introduced the study of random networks in graph theory and Granovetter (1973), who addressed the influence of the social network in which individuals are involved. But only at the end of the 1990s, with the development of instruments for collecting and processing data and the advent of information and internet technology, were the prac-tical applications of these concepts made possible, as then it became feasible to visualize, study and de-scribe the behavior of systems compounded of hun-dreds to billions of interacting components, such as the list of friends, friends of friends and so on; de-tailed list of interactions and reactions of genes, pro-teins and metabolites in a cell; or even the behavior of hundreds of billions of interconnected neurons in the brain (Easley & Kleinberg, 2010).

Despite the obvious differences between the indi-vidual characteristics of each network found in na-ture or in society as well as between the diversity of processes that shape the relationship of its agents, the fundamental fact is that the architecture and evolution of these networks are very similar to each other, allowing the use of a set of mathematical tools in common to exploit these systems and understand the behavior of each of its components as well as the network as a whole (Barabási, 2012).

In the academic literature, networks consist of a number (N) of actors, commonly called nodes or vertices and a relationship (L) between them, usual-ly called links or edges. The real networks are com-posed of a widely varied number of nodes (N) and links (L) that can be analyzed from a vast amount of mathematical assessment tools. This requires that a complete list of nodes and links is represented by an adjacent matrix composed of a square matrix with the same number of rows and columns as the num-ber of network agents, and Aij elements of this ma-trix to represent the links between agents (Mueller et al., 2008).

The standardized representation of agents (nodes) and their relationships (links) through charts also facilitates visualization and understanding as it allows recognition and can suggest new perspec-tives and inferences about a set of data, based on the assumption that it is possible to get more in-formation by sight than by all other senses com-bined (Ware, 2004).

Thus, the fundamental functionality of the SNA is the application of mathematical models based on the properties of graph theory for the study and evalua-


tion of a network formed by a number of agents and links that represent the relationships between them, according to the position occupied and the frame that compose the network. The metrics used in SNA are designed in two levels – at the level of individual agents (nodes) and at the network level.

From the point of view of an agent, the concept wide-ly used is the centrality, which measure as a single agent is associated with all the remainder agents that comprise the network and thus reflects the rela-tive importance of this agent in the network (Free-man, 1979). Three metrics are most commonly used to evaluate the centrality index: degree, closeness and betweenness. The centrality degree indicates the amount of other agents with which a particular agent is connected (indegree – outdegree), thereby measuring the visibility of an agent on a network. The centrality of closeness indicates how close an agent is to the other agents in the network, besides those to which it is directly connected; this makes it possible to measure how easily the agent can be con-nected to any other agent of the network. The cen-trality indicator, which measures the betweenness, indicates the importance of an agent in relation to other agents, through which it could be connected to the rest of the network, so it is able to measure the agent’s ability to allow interaction between other agents in the network (Freeman, 1979).

At the network structure level as a whole, three met-rics are noteworthy: density, centralization and com-plexity. The density refers to the actual number of links of the network, compared to the total of all the possible links assuming that all agents were connect-ed to each other, when the network density would be equal to 1 (Scott, 2000). The network centraliza-tion indicator seeks to assess the degree of the central agents in a network. A central agent is one through which pass most of the network connections; in this case a network with a greater degree of centralization is the one that shows the structure of a star where a single agent is connected to all others that, in turn, are not connected to each other; on the other hand, the lowest degree of centralization network occurs when all agents have the same number of connections to each other (Freeman, 1979). Thus, it can be said that the level of centralization of a network is related to the distribution of power or control over all network agents, whereas the density reflects the cohesion be-tween its agents. The degree of complexity, in turn, is defined as the number of dependency relations in a network and considers both the number of agents

as well as the degree to which they are connected to each other. This fact indicates that more complex net-works require more operational responsibility and coordination (Kim et al., 2011).

It is not uncommon in the literature the existence of studies where SNA and SC are used concomitantly. In these studies, companies are linked to other com-panies as notes linked to other nodes assuming the configuration of networks (Borgatti & Li, 2009; Kim et al., 2011; Mueller et al., 2008; Pahri, 2005). The SC-Map model makes advances in this regard, includ-ing in the analysis a structured way to connect prod-ucts and their relationships to both its components and to companies that provide them; or even to con-nect companies and their relationships to both their customers and suppliers, as well as to the products they manufacture and/or sell.

3. METHODOLOGY

Given that in an extended sense all products, and agents that produce and make them available on the real market, are in some way related, being part of a single, interconnected global network, this paper defines a limited scope to a company and a product, and thus seeks to establish a starting point for analy-sis chains and more complex networks.

Each one of the four steps of the methodology, de-tailed in the following subsections, is supported by real data provided by the studied Ethanol Plant. Excel spreadsheets listing all the products (compo-nents) acquired for the Ethanol production, as well as its suppliers, were made available by the Com-pany under a non-disclosure agreement.

Although the quantitative methods of the SNA anal-ysis can be carried out concerning the flow of ma-terials, tangible and intangible goods, for example: products, money and information, as pointed by Talamini and Ferreira (2010), the present analysis considers products and firms as nodes linked by ties following the method of use SNA in a SC context proposed by Borgatti and Li (2009). The paper is also intended to follow the methodology proposed by Kim et al. (2011), where the authors applied the SNA approach to analyze in terms of both the flow of ma-terial and the contractual relationships of three dif-ferent automotive supply networks.

To this end, the following procedures are followed in this study: (1) definition of a focal company and con-sequently a specific focal product manufactured and


supplied by it, connecting them through a company/product link or relationship; (2) identification of the acquired components used for the focal product man-ufacturing, and consequently linking each compo-nent to the focal product by a product/product con-nection; (3) identification of components’ suppliers and the product’s buyers and consequently associa-tion of the focal company with both, on the one hand, its suppliers and on the other hand, its customers, through company/company connections; and finally, (4) association of each component to the companies which provide them via company/ product links.

3.1 To Define a Firm and Associate It to a Product

The concept of a focal company – FC is considered as the starting point for the definition of a supply or network chain as shown by Lambert, Cooper, and Pagh (1998) and by Fawcett and Magnan (2002). But once a company can produce a varied and wide range of products, the purpose of this work is to as-sociate it with a specific focal product – FP.

In this paper the choice of ethanol as FP takes into account the importance of agribusiness in the global and domestic scenario in Brazil, previous work of the authors, and the relevance of renewable fuel within this context in Brazil and worldwide. The FC (ethanol plant), here designated EP01, properly identified in the Brazilian national register of legal entities CNPJ, Ministry of Finance of Brazil, under a specific record, located in the state of São Paulo, and therefore entitled to produce and supply the FP (ethanol) to other companies or consumers through commercial activity (Rosenbloom, 2008), is one of the units belonging to one of the largest sugar and ethanol groups in Brazil with milling capacity of over 20 million tons of sugarcane per year.

The concept of FP is connected to the chapter and position 2207 of the international code of the Har-monized System – HS, identified as Ethyl Alcohol. In this case the FC produces two distinct products under the same 2207 HS Code:

1. Anhydrous alcohol (undenatured ethyl alcohol with water content = <1% vol), NCM 22071010, and

2. Hydrated alcohol (other ethyl alcohol undena-tured with alcohol content => 80 vol%), NCM 22071090.

For illustration purposes, FC represented by a square named EP01, and FP (ethanol) represented by a tri-

angle identified by the code 2207 are connected via a company/product link represented by the dashed arrow (Figure 2).

Figure 2. Focal company – EP01, associated to the focal product (Ethanol) – 2207

3.2 To Identify the Components Acquired by the Focal Company and Associate Them to the Focal Product

The identification of ethanol components is performed based on information provided by EP01, which con-sists of the description and NCM code of each product that it acquires for the production of ethanol.

For scope delimiting effect, 19 products are consid-ered, representing more than 90% of the inputs used for the production of the FP. The inputs are classi-fied as eight agricultural inputs (AI) used in the pro-duction of cane sugar (codes 1207, 2521 , 2710, 2833, 2921, 3103, 3105 and 3808); ten chemical inputs (CI) used in the production of ethanol (codes 2102, 2801, 2807, 2815, 2828, 2902, 2941, 3821, 3822 and 3907), and the sugar-cane (SC) (code 1212).

While EP01 provides commercial names and the eight digits of the NCM of each input, the SCMap model allows visualization of only the first four dig-its of the harmonized system corresponding to the chapter and the product. The complete coding NCM is only maintained for indexing purposes, allowing consistency for future comparisons. For the pur-poses of graphic representation, each input has been identified by the first four digits of the HS.

Based on the product tree structure or bill of material, each of the agricultural raw materials are associated with sugarcane; consequently, in the same way sug-arcane and the other chemical inputs have all been associated to the focal product, ethanol, through the solid arrows, as shown in Figure 3, which represent the product/product links.


Figure 3. Relationships among the focal product ethanol (2207) and its components

Data about quantities and prices as well as the manu-facturing and transformation processes exercised in-ternally in the company are not part of the study scope.

3.3 To identify the Direct Customers and Suppliers As-sociated to the Focal Product and Link them to the Focal Company

Along with the identification of the components, EP01 provided the commercial name and the CNPJ number of each of the 40 active suppliers of chemical inputs (CI) and agricultural inputs (AI) acquired for the ethanol manufacturing; the focal company also provided the commercial name and CNPJ code of its nine customers responsible for the acquisition of more than 80% of its biofuel production.

According to the company, the sugarcane is pro-duced by their own farms, and complementarily is also acquired from a set of 729 external producers:

500 of them independent producers that negotiate based on market conditions, and 229 partners under contractual relations (Neves, Waak, & Marino, 1998).

Each of the 40 suppliers are represented by circles identified as C001 (company one) to C040 (company forty), and the nine customers are represented by the circles from C041 to C049. The 500 independent producers, suppliers of sugarcane, are represented by a single diamond identified as IP500.

Producer “partners” are not represented in this work, as the output of their production is con-sidered as part of the EP01 production itself. All input suppliers, independent producers that supply sugarcane and customers which acquire ethanol, are associated with EP01 via company/company links represented in Figure 4 by the dotted arrows.


Figure 4. Part of the focal company direct supply chain associated to the ethanol

Only the active suppliers and customers with whom EP01 maintains or has maintained trade relations (Choi & Krause, 2006), involving the purchase and sale relating to ethanol operations within the last 12 months are considered. Strategic information, such as price, quantities, production processes and any other kind of commercial relationships, are not part of the scope. Data such as corporate name and CNPJ code of each company are not disclosed, but will have the important function of acting as indexers able to allow future comparison and visibility between companies that allow mutual information-sharing. Sugarcane suppliers are not identified individually, and may not be indexed, so will not allow further comparisons with data from other companies.

As defined by Mentzer et al. (2001), the representa-tion of a company, its suppliers and direct customers are called the “direct supply chain”. Thus, if EP01 produces just one product, or ethanol, it could be said that Figure 4 represents its direct supply chain. But in reality, besides ethanol, EP01 also produces sugar, and even taking into account that, in this par-ticular case, almost all of the inputs used to produce ethanol are the same as those used in the production of sugar (Tokgoz & Elobeid, 2006), the customers are different when taking into consideration the ethanol and sugar separately. So the complete direct supply chain representation of EP01 should also consider all customers who acquire the sugar from the com-pany, which is out of the scope of the present work.

Taking these factors into account, it is prudent to note that Figure 4 represents only a part of the direct

supply chain of EP01; in other words, the part of its direct supply chain referent to the product ethanol.

3.4 To Associate Each Component to the Company which Supplies It

After have each component identified and associ-ated to the ethanol, according to the PC approach (Figure 3); as well as each supplier and customer as-sociated to EP01, according to the SC approach (Fig-ure 4); all considered companies are then associated to the product or products that they produce and/or provide to the market integrating the approaches of PC and SC in a complementary way. These compa-ny/product links are represented by dashed arrows.

3.5 Social Network Analysis Methodological Tools

Considering companies and products as agents or nodes, and the relationships or associations among such agents as connections that connect the nodes which configure the network, it is possible to calcu-late indicators that measure the centrality degree, closeness and betweenness, or in other words, the importance of an agent, company or product, in re-lation to the others being considered (Carter et al., 2015, Freeman, 1979). Quantitative analysis of the importance of each product and company, based on their respective positions in relation to all the others, is reached using the UCINET® software, grounded on graph theory and social network analysis – SNA (Borgatti et al., 2009).


While the study’s scope covers a total of delimited agents in 50 companies, 500 producers of sugar-cane and 20 products, the constant concern is with the search for instruments capable of supporting analyses that consider the exponential increase of

agents which act in the real environments of chains and networks of companies and products. Thus, by using NetDraw software, part of UCINET, it is pos-sible to portray the products and companies related to EP01 and ethanol as shown in Figure 5.

Figure 5. A NetDraw view of the products and companies related to EP01 and ethanol (2207)

4. RESULTS

Although the resulting network diagram (Figure 5) cannot be considered absolutely illuminating, visually speaking, through the software UCINET, and its NetDraw application, following the SCMap model, it is possible to create a more user-friendly graphical image of real companies, products and their relationships based on both SC and PC ap-proaches. See Figure 6.

It is also possible to quantitatively evaluate the posi-tion of each company and each product related to the whole network, calculating the centrality degree indicators as described in Table 1.

4.1 Graphic Design

The map resulting from this study allows the visualiza-tion of the EP01’s chain of companies and products as-sociated with respect to ethanol. Three different kinds of relationships are introduced, as shown in Figure 6.


Figure 6. SCMap of EP01 relative to ethanol (2207)

At the bottom side, the products are represented by triangles. According to the PC approach (Castro et al., 2002), the FP (ethanol) is associated with sugar-cane as well as with agricultural and chemical inputs which EP01 uses to manufacture it. Product/product links are represented by solid arrows.

Displaying products and their components, the map contributes to some relevant issues such as transparency analysis, traceability, sustainability, life cycle, environmental footprint and reverse lo-gistics, as well as other metrics concerning the ori-gin of the products used and consumed in global markets as well as the destination of their wastes and discharges, a growing concern among large business groups and organizations representing society (Hoffman, 2013).

In the upper portion of the SCMap, the direct supply chain (Mentzer et al., 2001) of EP01 related to etha-nol (2207) is shown. Companies are represented by squares and the sugarcane producers represented by a single diamond; EP01 is associated on one side with its suppliers and on the other side with its cus-tomers by company/company linkages according to the SC approach (Braziotis et al., 2013; Fawcett & Magnan, 2002; Lambert et al., 1998; Mentzer et al., 2001). Such links indicate trade relations between

customers and suppliers (Choi & Krause, 2006) and are represented by dotted lines.

The upper portion (SC) and the lower portion (PC) are then associated, allowing the visualization of the company/product links represented by dashed ar-rows according to the SCMap model, in order to al-low each company to be associated to the product or products which it provides. It becomes an important tool, able to identify companies and the products they supply in the commercial market environment.

Figure 6 is thus the graph results of this work, in compliance with the proposed scope and objective of the study. Even illustrating just part of the direct supply chain, it can be considered the starting point of an unlimited network map.

The differential, and therefore the contribution of the SCMap structured model, is to allow the expan-sion of the representation of companies and prod-ucts indefinitely by the replication of the model.

By changing the focus of analysis, considering any other product and company associated with each other, as focal product and company, the model al-lows replication of the procedures described in the methodology, thus expanding the representation ex-


ponentially, but in a structured way, once products and companies are indexed through their respective standard codes.

Besides the above-mentioned contributions to the supply chain management area, the SCMap model additionally offers the possibility to identify and display individual classes of products associated to the companies which produce and/or trade them, al-lowing the identification of products, components as well as the companies that produce and/or supply them to the market.

4.2 Quantitative Analysis of the Chain Companies and Products

Using the UCINET software, it is possible to calcu-late the centrality degree indicators: in/outdegree, closeness and betweenness for each one of 550 com-panies (Table 1) and 20 products (Table 2) comprised in this analysis. Considering the limited scope of this starting analysis, the density, centralization and complexity’s measures of the network as a whole are not considered.

As shown in Table 1, with respect to EP01’s sup-pliers (from C001 to C040), the highest outdegree value (5.000) for the supplier C025 shows that it en-joys greater importance as it is connected to a high-er number of nodes than any other supplier (four products and one company); this fact means it is the company which provides the largest number of products on the network. By the same criterion, the supplier C005 with a value 4,000 of outdegree, and suppliers C006, C023 and C037 with outdegree of 3,000 each, are next in order of importance. Taking into account just the quantity of products provided to the network, all other suppliers have the same relative importance (outdegree = 2,000).

While this result does not indicate a significant impor-tance, due to the small size of the sample considered, as more companies begin using the model, this indi-cator becomes more relevant, since it is able to mea-sure among others the relationship of a company and a product with other companies and products, and thereby contribute effectively to the identification of bottlenecks, concentration of products and suppliers, and dependence risks of suppliers and materials.

Table 1. Centrality indicators of each company COMPANIES

DEGREE CLOSENESS BETWEENNESS

CODE OUT IN IN OUT

EP01 10.000 540.000 3.333 0.179 5.142.950

IP500 2.000 0.000 0.175 0.179 0.000

C001 2.000 0.000 0.175 0.179 0.000C002 2.000 0.000 0.175 0.180 0.000C003 2.000 0.000 0.175 0.180 0.000C004 2.000 0.000 0.175 0.180 0.000C005 4.000 0.000 0.175 0.180 0.000C006 3.000 0.000 0.175 0.180 0.000C007 2.000 0.000 0.175 0.180 0.000C008 2.000 0.000 0.175 0.179 0.000C009 2.000 0.000 0.175 0.179 0.000C010 2.000 0.000 0.175 0.180 0.000C011 2.000 0.000 0.175 0.179 0.000C012 2.000 0.000 0.175 0.179 0.000


C013 2.000 0.000 0.175 0.180 0.000C014 2.000 0.000 0.175 0.180 0.000C015 2.000 0.000 0.175 0.180 0.000C016 2.000 0.000 0.175 0.180 0.000C017 2.000 0.000 0.175 0.179 0.000C018 2.000 0.000 0.175 0.179 0.000C019 2.000 0.000 0.175 0.180 0.000C020 2.000 0.000 0.175 0.179 0.000C021 2.000 0.000 0.175 0.180 0.000C022 2.000 0.000 0.175 0.180 0.000C023 3.000 0.000 0.175 0.180 0.000C024 2.000 0.000 0.175 0.180 0.000C025 5.000 0.000 0.175 0.180 0.000C026 2.000 0.000 0.175 0.180 0.000C027 2.000 0.000 0.175 0.180 0.000C028 2.000 0.000 0.175 0.180 0.000C029 2.000 0.000 0.175 0.180 0.000C030 2.000 0.000 0.175 0.180 0.000C031 2.000 0.000 0.175 0.180 0.000C032 2.000 0.000 0.175 0.180 0.000C033 2.000 0.000 0.175 0.180 0.000C034 2.000 0.000 0.175 0.179 0.000C035 2.000 0.000 0.175 0.180 0.000C036 2.000 0.000 0.175 0.179 0.000C037 3.000 0.000 0.175 0.180 0.000C038 2.000 0.000 0.175 0.180 0.000C039 2.000 0.000 0.175 0.179 0.000C040 2.000 0.000 0.175 0.180 0.000C041 0.000 1.000 3.339 0.175 0.000C042 0.000 1.000 3.339 0.175 0.000C043 0.000 1.000 3.339 0.175 0.000C044 0.000 1.000 3.339 0.175 0.000C045 0.000 1.000 3.339 0.175 0.000C046 0.000 1.000 3.339 0.175 0.000C047 0.000 1.000 3.339 0.175 0.000C048 0.000 1.000 3.339 0.175 0.000C049 0.000 1.000 3.339 0.175 0.000

Regarding the products (Table 2), the indegree indi-cator shows that the agricultural input AI 3808 is the one with the highest value (17,000), indicating that it is less important from the point of view that there are more companies able to provide it; on the other hand, the agricultural and chemical inputs whose indegree has the lowest value (1.000) should receive greater care from the point of view of supply, since

they are supplied by only one company, indicating a high commercial dependence level.

Also deserving some note are the products’ between-ness indicators. As can be seen, the agricultural in-put AI3808 with the highest value (15,500) indicates it is the product most common to the entire chain or network in terms of access to all other products and


companies, which may indicate that its availability is more vulnerable to external impacts of other chains. On the other hand, the chemical input CI3822 with

the lowest value (0.250) is the most isolated product in terms of connection with other network agents, and therefore less sensitive to external impacts.

Table 2. Centrality indicators of each product

PRODUCTS

DEGREE CLOSENESS BETWEENNESSCODE OUT IN IN OUT

2207 0.000 12.000 9.122 0.175 0.000

SC/1212 1.000 508.000 2.853 0.176 258.000

AI/1207 1.000 1.000 0.176 0.176 1.000AI/2521 1.000 1.000 0.176 0.176 1.000AI/2710 1.000 1.000 0.176 0.176 1.000AI/2833 1.000 1.000 0.176 0.176 1.000AI/2921 1.000 1.000 0.176 0.176 0.500AI/3103 1.000 1.000 0.176 0.176 1.000AI/3105 1.000 7.000 0.178 0.176 6.000AI/3808 1.000 17.000 0.181 0.176 15.500CI/2102 1.000 1.000 0.176 0.176 0.500CI/2801 1.000 2.000 0.176 0.176 1.000CI/2807 1.000 3.000 0.176 0.176 1.500CI/2815 1.000 2.000 0.176 0.176 1.000CI/2828 1.000 2.000 0.176 0.176 0.700CI/2902 1.000 1.000 0.176 0.176 0.500CI/2941 1.000 2.000 0.176 0.176 0.700CI/3822 1.000 1.000 0.176 0.176 0.250CI/3824 1.000 2.000 0.176 0.176 0.450CI/3907 1.000 2.000 0.176 0.176 0.450

The in/out closeness indicators, as well as all indica-tors related to the purchasing companies (from C041 to C049) and EP01, are not relevant, given the size and scope of the sample. The sugarcane (1212) and ethanol (2207) indicators will also make more sense when it is possible to expand the network by consid-ering new products and businesses.

Among the limitations of the work, there is the re-striction of the scope for a focal company and a fo-cal product, as well as the limitation of analysis that considers only three central indicators among many others possibly arising from the SNA. This fact speaks to the wide possibilities of expansion of the

analysis under this model, which is presented only as an initial approach; we cannot see the full conse-quences of this model presently.

5. CONCLUSION

Using the SCMap model it is possible to visual-ize products, companies and their relationships, as well as to identify and analyze the positioning of products and companies that make up the real chains and networks of the corporate environment, thus contributing to the issue of visibility and ap-plication of supply chain management concepts in


real environments of companies and the products which they trade.

It is also possible to provide a quantitative analysis of their respective positions in relation to the other, obtaining the centrality indicators derived from the network analysis, specifically Social Network Anal-ysis. These indicators have proved able to measure and assess the importance, criticality and substitut-ability of companies and products when compared with each other on the same chain or network.

While recognizing the limitation of this study, con-sidering the modest number of companies and prod-ucts in face of real situations, the proposed structure should be viewed just as a starting point for linking products and companies properly indexed in a stan-dardized manner, allowing unlimited expansion through replication of the methodology.

The SCMap model enables the development of future work where real chains may be identified and repre-sented in expanded form, taking into account suppliers, customers, components and derivatives of each single product and/or company represented in this initial work, thus contributing to perform the theoretical ideal of the production chain and supply chain approaches.

6. REFERENCES

Bair, J. (2009). Global commodity chains: Genealogy and review. In J. Bair (Ed.), Frontiers of commodity chain research (pp. 1-34). Stanford, USA: Stanford University Press.

Barabási, A.-L. (2012, July). Network science. E-book version. Re-trieved from http://barabasi.com/networksciencebook/

Batalha, M. O. (1995). As cadeias de produção agro-industriais: Uma perspectiva para o estudo das inovações tecnológicas. Revista de Administração, 30(42), 43-50.

Batalha, M. O., & Silva, A. L. (2008). Gerenciamento de siste-mas agroindustriais: Definições, especialidades e correntes metodológicas. In M. O. Batalha (Coord.), Gestão agroindus-trial (pp. 1-62). São Paulo, SP: Atlas.

Borgatti, S. P. (2002). NetDraw software for network visualiza-tion. Lexington, USA: Analytic Technologies.

Borgatti, S. P., & Li, X. (2009). On social network analysis in a supply chain context. Journal of Supply Chain Management, 45(2), 5-22 .

Borgatti, S. P., Mehra, A., Brass, D. J., & Labianca, G. (2009). Net-work analysis in the social sciences. Science, 323, 892-895.

Braziotis, C., Baurlakis, M., Rogers, H., & Tannock, J. (2013). Supply chains and supply networks: Distinctions and over-laps. Supply Chain Management: An International Journal, 18(6), 644-652.

Carter, R. C., Rogers, D. S., & Choi, T. Y. (2015). Toward the the-ory of the supply chain. Journal of Supply Chain Manage-ment, 51(2). Retrieved from http://ssrn.com/abstract=2546173

Castro, A. M. G., Lima, S. M. V., & Cristo, C. M. P. N. (2002). Ca-deia produtiva: Marco conceitual para apoiar a prospecção tecnológica. Presented at XXII Simpósio de Gestão da Inova-ção Tecnológica, Salvador, BA.

Choi, T. Y., Dooley, K. J., & Rungtusanatham, M. (2001). Sup-ply networks and complex adaptive systems: Control versus emergence. Journal of Operations Management, 19, 351-366.

Choi, T. Y., & Krause, D. R. (2006). The supply base and its com-plexity: Implications for transaction costs, risks, responsive-ness, and innovation. Journal of Operations Management, 24, 637-652.

Christopher, M. L. (1992). Logistics and supply chain manage-ment. London, UK: Pitman.

Davis, J. H., & Goldberg, R. A. (1957). A concept of agribusiness. Division of Research, Graduate School of Business Adminis-tration, Harvard University, Boston, USA.

Dekker, M., Verkerk, R., & Jonjen, W. M. F. (2000). Predictive modeling of health aspects in the food production chain: A case study on glucosinolates in cabbage. Trends in Food Sci-ence & Technology, 11, 174-181.

Easley, D., & Kleinberg, J. (2010) Networks, crowds, and markets: Reasoning about a highly connected world [Adobe Digital Edi-tions version]. Cambridge University Press. Retrieved from http://www.cs.cornell.edu/home/kleinber/networks-book/

Erdös, P., & Rényi, A. (1959). On randon graphs. Publicationes Mathematicae, 6, 290-297.

Fawcett, S. E., & Magnan, G. M. (2002). The rhetoric and reality of supply chain integration. International Journal of Physical Distribution & Logistics Management, 32(5), 339-361.

Fine, B., & Leopold, E. (1993). The world of consumption. Lon-don, UK: Routledge.

Frankel, R., Bolumole, Y., Eltantawy, R., Paulraj, A., & Gundlach, G. (2008). The domain and scope of SCM’s foundational dis-ciplines: Insights and issues to advance research. Journal of Business Logistics, 29(1), 1-30.

Freeman, L. C. (1979). Centrality in social networks: Conceptual clarification. Social Networks, 1, 215-239.

Friedland, W. H. (1984). Commodity systems analysis: An ap-proach to the sociology of agriculture. In H. K. Schwarz-weller (Ed.), Research in rural sociology and development (pp. 221-235). London, UK: JAI Press.

Giménez, D., Vegetti, M., Leone, H. P., & Henning, G. P. (2008). PRoduct ONTOlogy: Defining product-related concepts for lo-gistics planning activities. Computers in Industry, 59, 231-241.

Granovetter, M. S. (1973). The strength of weak ties. American Journal of Sociology, 78(6). 1360-1380.

Harland, C. M., Lamming, R. C., & Cousins, P. D. (1999). Devel-oping the concept of supply strategy. International Journal of Operations & Production Management, 19(7), 650-674.


Hegge, H. M. H., & Wortmann, J. C. (1991). Generic bill-of-mate-rial: A new product model. International Journal of Produc-tion Economics, 23, 117-128.

Hoffman, B. (2013). Behind the brands: Food justice and the ‘Big 10’ food and beverage companies [Oxfam Briefing Paper No. 166]. Oxford, UK: Oxfam GB.

Houlihan, J. B. (1984). Supply chain management. Proceedings of the 19th International Technical Conference, BPICS.

Kim, Y., Choi, T. Y., Yan, T., & Dooley, K. (2011). Structural inves-tigation of supply networks: A social network analysis ap-proach. Journal of Operations Management, 2, 194-211.

Lambert, D. M., Cooper, M. C., & Pagh, J. D. (1998). Supply chain management: Implementation issues and research opportuni-ties. The International Journal of Logistics Management, 9(2), 1-20Lambert, D. M., & Pohlen, T. L. (2001). Supply chain metrics. The International Journal of Logistics Management, 12(1), 1-19.

Lambert, D. M., Stock, J. R., & Ellram, L. M. (1998). Fundamen-tals of logistics management. Boston, USA: McGraw-Hill.

Lamming, R., Johnsen, T., Zheng, J., & Harland, C. (2000). An initial classification of supply networks. International Jour-nal of Operations & Production Management, 20(6), 675-691.

Lazzarini, S. G., Chaddad, F. R., & Cook, M. L. (2001). Integrating supply chain and network analyses: The study of netchains. Journal on Chain and Network Science, 1, 7-22.

Li, G., Yang, H., Sun, L., Ji, P., & Feng, L. (2010). The evolutionary complexity of complex adaptive supply networks: A simula-tion and case study. Int. J. Production Economics, 124, 310-330.

Londe, B. J. La, & Masters, J. M. (1994). Emerging logistics strate-gies: Blueprints for the next century. International Journal of Physical Distribution & Logistics Management, 24(7), 35-47.

Mentzer, J. T., DeWitt, W., Keebler, J. S., Min, S., Nix, N. W., Smith, C. D., & Zacharia, Z. G. (2001). Defining supply chain management. Journal of Business Logistics, 22(2), 1-25.

Ministério do Desenvolvimento, Indústria e Comércio Exterior. (2015). Cadeia produtiva: Conceituação. Brasília, DF. Re-trieved from http://www.mdic.gov.br/sitio/interna/interna.php?area=2&menu=3252

Miraglia, M., Berdal, K. G., Brera, C., Corbisier, P., Holst-Jensen, A., Kok, E. J., ... Zagon, J. (2004). Detection and traceability of genetically modified organisms in the food production chain. Food and Chemical Toxicology, 42(7), 1157-1180.

Miyake, D. I., Torres, A. S. T., Jr., & Favaro, C. (2010). Supply chain mapping initiatives in Brazilian automotive industry: Challenges and opportunities. Journal of Operations and Supply Chain Management, 3(1), 79-97.

Mueller, R. A. E., Buergelt, D., & Seidel-Lass, L. (2008). Supply chains and social network analysis. Paper prepared for pre-sentation at the 110th EAAE Seminar System Dynamics and Innovation in Food Networks, Innsbruck-Igls, Austria.

Neves, M. F., Waak, R. S., & Marino, M. K. (1998). Sistema agro-industrial da cana-de-açúcar: Caracterização das transações entre empresas de insumos, produtores de cana e usinas. Presented in SOBER, Congresso Brasileiro de Economia e So-ciologia Rural, Poços de Caldas, MG.

Oliver, R. K., & Webber, M. D. (1992). Supply chain management: Logistics catches up with strategy. In M. G. Christopher (Ed.), Logistics: The strategic issues (pp. 63-75). London, UK: Chapman & Hall.

Parent, J. (1979). Filières de produit, stades de production et branch-es d’activité. Revue d’Économie Industrielle, 7(1), 89-91.

Parhi, M. (2005). Dynamics of inter-firm linkages in Indian auto component industry: A social network analysis. Presented at Druid Winter Conference, Denmark.

Pathak, S. D., Dilts, D. M., & Mahadevan, S. (2009). Investigat-ing population and topological evolution in a complex adap-tive supply network. Journal of Supply Chain Management, 45(3), 54-67.

Pimentel, D., Marklein, A., Toth, M. A., Karpoff, M., Paul, G. S., McCormack, R., ... Krueger, T. (2008). Biofuel impacts on world food supply: Use of fossil fuel, land and water re-sources. Energies, 1, 41-78.

Renewable Fuels Association. (2013). Historic U.S. fuel ethanol production. Washington, USA: Renewable Fuels Association.

Rosenbloom, B. (2008). Canais de marketing: Uma visão geren-cial (1ª ed., 2ª reimpr.). São Paulo, SP: Atlas.

Scott, J. (2000). Social network analysis: A handbook. London, UK: Sage Publications.

Stock, J. R. (2009). A research view of supply chain management: Developments and topics for exploration. Orion, 25(2), 147-160.

Talamini, E., & Ferreira, G. M. V. (2010). Merging netchain and social network: Introducing the ‘social netchain’ concept as an analytical framework in the agribusiness sector. African Journal of Business Management, 4(13), 2981-2993.

Tokgoz, S., & Elobeid, A. (2006). An analysis of the link between ethanol, energy, and crop markets [Working Paper No 06-WP 435]. Center for Agricultural and Rural Development, Iowa State University.

Vegetti, M., Leone, H., & Henning, G. (2011). PRONTO: An on-tology for comprehensive and consistent representation of product information. Engineering Applications of Artificial Intelligence, 24, 1305-1327.

Vidal, C. J., & Goetschalckx, M. (1997). Strategic production-dis-tribution models: A critical review with emphasis on global supply chain models. European Journal of Operational Re-search, 98, 1-18.

Ware, C. (2004). Information visualization: Perception for design. San Francisco, USA: Morgan Kaufmann.

World Customs Organization. (2015). What is the Harmonized System (HS)? Brussels, Belgium: World Customs Organiza-tion. Retrieved from http://www.wcoomd.org/

Yan, H., Yu, Z., & Cheng, T. C. E. (2003). A strategic model for supply chain design with logical constraints: Formulation and solution. Computers & Operations Research, 30, 2135-2155.

Zylbersztajn, D. (1994). Políticas agrícolas e comércio mundial – “Agribusiness”: Conceito, dimensões e tendências. In H. H. Fagundes (Org.), Estudos de política agrícola (No 28). Brasí-lia, DF: Instituto de Pesquisas Econômicas Aplicadas – IPEA.

Volume 9• Number 1 • January - June 2016

EDITORIAL INFORMATION 2015

23

13

8 21

36

Institutional diversity of authors (%)

FGV/EAESP

UFRGS

UFU

Other nationalinstitutions

Other internationalinstitutions

2014 2015

Authors geographic partnership

Only Brazilian authors 10 8

Only foreigners authors 4 7

International collaboration 1 0

Total of published articles 15 15

Editorial Flow

Submitted 34

Approved 15

Rejected 19

Published 15

33

21 10

36

Geographic diversity of authors (%)

SP

RS

Other BrazilianStates

Other Countries

Editorial Information 2015

ISSN: 1984-3046 • Journal of Operations and Supply Chain Management Volume 9 Number 1 2016

Source: Google Analytics

MOST ACCESSED ARTICLES IN 2015

RANKING ARTICLES NUMBER OF

ACCESSES

1st THE APPLICATION OF LEAN PRICIPIES IN THE FAST

MOVING CONSUMER GOODS (FMCG)

Alaa Aljunaidi and Samuel Ankrah. Journal of Operations and Supply

Chain Management, v. 7, n. 2, July-December 2014 106

2nd

SHARED RESPONSIBILITY AND REVERSE LOGISTICS

SYSTEMS FOR E-WASTE IN BRAAZIL

João Ernesto Brasil Migliano, Jacques Demajorovic and Lucia Helana

Xavier. Journal of Operations and Supply Chain Management, v. 7, n.

2, July-December 2014 66

3rd

SUPPLY CHAIN MANAGEMENT AND ITS ROUTE TO

NORMAL SCIENCE: A KUHNIAN ANALYSIS

Rogers Ascef, Geraldo Ferrer and Steve Mullins. Journal of Operations

and Supply Chain Management, v. 7, n. 2, July-December 2014 51

4th REDUCING INTERNAL INFORMATION TECHNOLOGY

RESOURCE ALLOCATION THROUGH GLOBAL UPSTREAM

ELETRONIC BUSINESS STANDARDS: A CASE STUDY IN

NOVOZYMES

Douglas Steven Hill, Juan Francisco Zurita Duque and Helle Skøtt.

Journal of Operations and Supply Chain Management, v. 4, n. 1,

January-June 2011 41

5th SUPPLY CHAIN MANAGEMENT MEASUREMENT AND ITS

INFLUENCE ON OPERATIONAL PERFORMANCE

Priscila Laczynski de Souza Miguel and Luiz Artur Ledur Brito.

Journal of Operations and Supply Chain Management, v. 4, n. 2, July-

December 2011 37

6th BENEFITS OF CPRF AND VMI COLLABORATION

STRATEGIES: A SIMULATION STUDY

Raj Kamalapur, David Lyth and Azim Houshyar. Journal of Operations

and Supply Chain Management, v. 6, n. 2, July-December 2013 27

7th RISK MANAGEMENT IN THE SUPLY CHAIN OF THE

BRAZILIAN AUTOMOTIVE INDUSTRY

Edson Júnior Gomes Guedes, Alexandre de Vicente Bittar, Luiz Carlos

Di Serio and Luciel Henrique de Oliveira. Journal of Operations and

Supply Chain Management, v. 8, n. 1, January-June 2015 21

8th CHANGES IN THE ROLE OF PRODUCTION AND

OPERATIONS MANAGEMENT IN THE NEW ECONOMY

Henrique Luiz Corrêa. Journal of Operations and Supply Chain

Management, v. 1, n. 1, January-June 2008 20

9th DOES THE INTITUTIONAL CONTEXT SHAPE

INTERNATIONAL OPERATIONS STRATEGY? COUNTRY-

LEVEL ANALYSIS

Vitor Fabian Brock and Iuri Gavronski. Journal of Operations and

Supply Chain Management, v. 6, n. 1, January-June 2013 19

10th INTERNAL LOGISTICS, EXTERNAL COMMUNICATION,

INFORMATION PROCESSING AND FINANCIAL CONTROL:

AN ANALYSIS WITH BRAZILIAN MICRO AND SMALL

ENTERPRISES

Claudinê Jordão de Carvalho and Rodrigo Fernandes Malaquias.

Journal of Operations and Supply Chain Management, v. 5, n. 1,

January-June 2012 15



REVIEWERS 2015

The Journal of Operations and Supply Chain Management would like to thank the following

reviewers for their valuable contribution:

Adriane Angélica Farias Santos Lopes de Queiroz

Universidade Federal de Mato Grosso do Sul, Departamento de Economia e Administração -Campo

Grande - MS, Brazil

Ana Cristina de Faria

Universidade Nove de Julho, Programa de Pós-Graduação em Cidades Inteligentes e Sustentáveis -

São Paulo, SP, Brazil

Angela Ruriko Sakamoto

Universidade Luterana do Brasil, Centro Universitário Luterano de Palmas - Palmas - TO, Brazil

Antônio André Cunha Callado

Universidade Federal Rural de Pernambuco, Departamento de Administração - Recife - PE, Brazil

Consuelo Patrícia Queiroz Morales

Pontificia Universidad Catolica del Peru, Departamento de Ingeniería - Lima, Peru

Cristiane Biazzin

Fundação Getulio Vargas, Escola de Administração de Empresas de São Paulo - São Paulo - SP,

Brazil

Dagoberto Helio Lorenzetti


Brazil

Deborah Moraes Zouain

Universidade do Grande Rio, Programa de Pós-Graduação em Administração - Rio de Janeiro -RJ,

Brazil

Fernando César Almada Santos

Universidade de São Paulo, Escola de Engenharia de São Carlos - São Carlos - SP, Brazil



Iuri Gavronski

Universidade do Vale do Rio dos Sinos, Centro de Ciências Econômicas - São Leopoldo - RS,

Brazil

Jorge Luiz de Biazzi

Universidade de São Paulo, Faculdade de Economia Administração e Contabilidade - São Paulo -

SP, Brazil

José Manuel Meireles de Sousa

Universidade Anhembi Morumbi - São Paulo -SP, Brazil

Juliana Bonomi Santos

Centro Universitário FEI, Programa de Pós-Graduação em Administração - São Paulo - SP, Brazil

Julio Eduardo da Silva Menezes

Universidade Federal do Tocantins, Palmas, TO, Brazil

Lamay Bin Sabir

Aligarh Muslim University, Aligarh, Uttar Pradesh, India

Luciana Gondim de Almeida Guimarães

Universidade Potiguar – Natal - RN, Brazil

Luis Miguel Domingues Fernandes Ferreira

Universidade de Aveiro, Departamento de Economia, Gestão, Engenharia Industrial e Turismo -

Aveiro, Portugal

Margareth Rodrigues de Carvalho Borella

Universidade de Caxias do Sul, Centro de Ciências da Administração - Caxias do Sul - RS, Brazil

Miguel Afonso Sellitto

Universidade do Vale do Rio dos Sinos, Programa de Pós-Graduação em Engenharia de Produção e

Sistemas - São Leopoldo - RS, Brazil

Orlando Cattini Junior

Fundação Getulio Vargas, Escola de Administração de Empresas de São Paulo - São Paulo - SP -

Brazil



Paulo Cesar Calabria

IBM Brasil, IBM Global Services - São Paulo – SP, Brazil

Priscila Laczynski de Souza Miguel


Brazil

Rameshwar Dubey

Symbiosis International University Pune – Maharashtra, India

Saurav Negi

University of Petroleum and Energy Studies - Dehradun, Uttarakhand

Ubiratã Tortato

Pontifícia Universidade Católica do Paraná, Centro de Ciências Sociais Aplicadas – Curitiba – PR,

Brazil

Zaida Cristiane dos Reis

Universidade de Caxias do Sul, Conselho de Ensino, Pesquisa e Extensão - Caxias do Sul - RS,

Brazil

15

8

8

50

19

Institutional diversity of authors (%)

FGV/EAESP

Universidade de Caxiasdo Sul

Universidade do Valedo Rio dos Sinos

Other NationalInstitutions

Other InternationalInstitutions

38

19

23

19

Geographic diversity of reviewers (%)

SP

RS

Other BrazilianStates

Other Countries

Date post:	03-Mar-2017
Category:	Education
Upload:	fgv-fundacao-getulio-vargas
View:	42 times
Download:	0 times