Social entrepreneurial ventures continue to receive attention from scholars that come from a variety of disciplines. While initially, much of the research on social ventures began in the nonprofit and public policy areas, it has now grown to include the for-profit sector (Moss, Lumpkin, and Short 2010).
Due to the relative newness of the field, scholars have yet to reach a consensus definition on what a social entrepreneurial venture is. For our study, we adopt a definition presented by Nicholls (2006) “innovative and effective activities that focus strategically on resolving social market failures and creating opportunities to add social value systematically by using a range of organizational formats to maximize social impact and bring about change”. Smith and Stevens (2010) provided their support for this definition as it shares similarities with other definitions presented by researchers in the field (i.e. Dees and Economy 2001; Austin, Stevenson, and Wei-Skillern 2006; Zahra et al. 2009) due to its focus on the creation of social value and innovative practices.
In this paper the authors hypothesize that social entrepreneurial ventures (SEVs) function as a system of systems (SoS). This hypothesis is evaluated by considering various characteristics of a SoS and then comparing the functioning of a SEV to a SoS. This is because the context of a SoS has increasingly been proposed as a solution to the engineering and management of complex systems problems because of the dynamic and flexible environment that it is believed these systems operate under. SEVs often operate from a partnership-based approach involving more stakeholders than a purely economically driven venture. These stakeholders can include government entities, funders, clients served, other organizations, and community members (Goldstein, Hazy, and Silberstang 2008). Additionally, the social entrepreneur engages with “social and community values to achieve social outcomes,” which indicates a slight difference from their economically driven counterparts (Tapsell and Woods 2010). Tapsell and Woods (2010) also purport that in terms of social entrepreneurship, any theoretical development must include the socio-economic context in which these firms operate. Due to some of these distinctions of social entrepreneurship, we hypothesize SEVs to be complex systems that operate in a dynamic and flexible environment and have to be engineered and managed in such an environment as well; thus making them behave like a SoS. This suggests that the SoS risk management approach may be a more appropriate strategy for managing risk in these organizations.
In this paper, we provide an example of an SEV, in which we evaluate the SEV from the perspectives of characteristics of a SoS and show how the SEV functions as a SoS. We evaluate MedShare International, a social entrepreneurial venture that recovers usable medical supplies and equipment from United States hospitals and manufacturers in order to redistribute it to medically-underserved areas of the world. MedShare was chosen as an illustrative example due to its strong reputation, partnership-based approach to creating social value, and the nature of its operations. Further explanation is provided in Section Three of this paper.
In the following sections, we first provide an overview of the characteristics of systems of systems and the flexibility dynamics that work within them. Second, we provide an overview of MedShare and how those characteristics relate to MedShare’s operations. Third, we provide an overview of systemic risk management practices in systems of systems and show how MedShare may benefit from systemic, rather than traditional, risk management practices. Finally, we suggest that SEVs operate as systems of systems and demonstrate how leaders of SEVs may benefit from operating from this perspective.
2 Complexity, Systems of Systems, and Social Entrepreneurship
Researchers have only recently begun to explore possible applications of complexity science to social entrepreneurship. While significant research exists in the application of complexity theory to traditional entrepreneurship, a robust application of complexity theory to social entrepreneurship has yet to emerge (Goldstein, Hazy, and Silberstang 2008). One may argue that a separate application or theory in the realm of social value creation may not be warranted. Others argue, “complexity science cannot be readily applied to organizations … its chief value lies in its use as a new metaphor for considering the dynamics of organizations (McMillan and Carlisle 2007). However, in McMillan and Carlisle’s case study, they show how complexity science was applied effectively to an existing social entrepreneurial venture. Additionally, several researchers show that the application of for-profit management theories is not appropriate without modification (Spear 2006; Robinson 2006; Austin, Stevenson, and Wei-Skillern 2006; Kaplan 2001; Moore 2001).
In the framework of complexity, the view that social entrepreneurship is a more complex process considers interactions, several levels of causation and analysis, and also includes concepts of cultural dynamics and social capital (Schwandt, Holliday, and Pandit 2009). SEVs are “the products of the social, cultural, commercial, and political expectations of the innovation’s range of stakeholders, not solely the vision of the social entrepreneur” (Newth 2015). SEVs are also described as collective organizations utilizing several stakeholders to address existing social problems while perhaps also creating new organizations or dismantling outdated institutional arrangements (Montgomery, Dacin, and Dacin 2012). Furthermore, SEVs become products of the competing logics of institutions that comprise their societal and economic environments (Newth in press). The exploration of complexity theory within the realm of social entrepreneurship takes these differences into consideration as social enterprises are considered to be complex structures with dynamic networks of interaction.
Goldstein, Hazy, and Silberstang (2008) propose ways complexity theory can be used to develop a practical theory within the field of social entrepreneurship in order to better understand and address fundamental issues related to social value creation. Their call to action to further explore the complexity of social entrepreneurship is one we hope to answer with this initial exploratory case study. We concur that social entrepreneurial ventures are complex and we also propose these ventures are systems of systems (SoSs).
Several researchers attempt to succinctly define SoSs, but the field has not come to an agreement on one standard definition for SoS (Gorod et al. 2008) (Boardman and Sauser 2006) and (Jamshidi 2005). Kotov’s (1997) frequently cited definition of SoS defines SoS as large-scale concurrent and distributed systems, the components of which are complex systems themselves. Alternatively, other researchers define SoS by virtue of their characteristics. Boardman and Sauser (2006) is the most commonly cited definition. They define SoS by virtue of five characteristics: autonomy, belonging, connectivity, diversity and emergence.
- –Autonomy is the ability of a system to make independent choices. This includes managerial and operational independence while accomplishing the purpose of the SoS.
- –Belonging refers to the ability of constituent systems to choose to belong to the SoS. The choice is based on their own needs/beliefs and/or fulfillment.
- –Connectivity refers to the ability to stay connected to other constituent systems.
- –Diversity refers to the level of heterogeneity of the constituent systems.
- –Emergence refers to the formation of new properties as a result of development or evolutionary process
In Figure 1, Sauser et al. (2008), also propose that there are opposing forces or paradoxes within each characteristic. Each opposing force then results in the characteristic under consideration attaining a level of equilibrium along the spectrum of that characteristic.
In Figure 1, the left side of the figure depicts qualities of an assembly (Shenhar and Dvir 2007) and the right side depicts the qualities of a SoS. According to Gorod et al. (2008), this means that for a particular organization, if the characteristics of that organization are plotted more towards the left end of the spectrum, the organization is rigid and does not have a lot of flexibility; thus resulting in low adaptability as well. As the organizational characteristic plotting move towards the right, it would portray a planned organization, which has more flexibility than a rigid organization. However, the organization is still primarily governed by rules and detailed procedures, which could be complex and require an extensive information processing capacity. The rigidity of this form is not because of technology or basic organizational structure, but due to strong process regulations, detailed planning, and control. Extreme movement towards the right on the characteristics scale would lead to a flexible organization which then ultimately turns into a chaotic organization. The flexible organization is dominated by strategic and structural flexibility. This results in a reasonably high ability to change its organizational conditions and also in being able to adapt to change disturbances without the organization losing its distinctiveness. Conversely, a chaotic organization has extensive flexibility but it is totally uncontrollable.
Gorod et al. (2008), further develop a Flexibility of SoS model, which is shown in Figure 2. This model shows the extent to which the flexibility of a SoS can fluctuate, depending on whether it can shift from a system or assembly or a chaotic form of SoS. This means that in order to maintain a flexible SoS (the ideal state), the extensiveness of the Flexibility Dynamic, shown in Figure 2, must lean towards independence, decentralization, network-centricity, heterogeneity and indeterminability. However, the key point to remember is that the flexibility dynamic should not be overwhelmingly toward the states described in the prior sentence, otherwise it would lead to a state of chaos. The ideal goal is to stay within the Optimization Space for SoS, as shown in Figure 2.
These characteristics are important to consider when evaluating SEVs as today’s SEVs can be extremely complex and are likely to function as SoS, which the authors evaluate and analyze in a later section of this paper. As mentioned in the paragraph above, in order to be able to manage the SEV effectively, there should be a certain level flexibility so that it can remain agile. However, too much flexibility would lead to complete chaos. Hence, an understanding of the SoS characteristics is important for personnel managing SEVs.
3 Introduction to MedShare International: An Evaluation as a System of Systems
For purposes of illustration, we examine a U.S. based nonprofit organization, MedShare International. MedShare, headquartered in Atlanta, Georgia, U.S.A., operates as a recycling and distribution center for U.S. hospital surplus medical supplies and equipment. MedShare collects, sorts, and distributes products to health care facilities in medically under-served communities all across the globe. The U.S. healthcare system, due to regulations, discards billions of dollars of usable medical supplies and equipment each year (Denizel, Ferguson, and Toktay 2013). MedShare also receives donated medical supplies and equipment from medical manufacturers and distributors. MedShare’s mission, along with similar kinds of organizations called Medical Supply Recovery Organizations (MSROs), recover the waste of U.S. hospitals in order to redistribute it to medically underserved communities.
MedShare was chosen due to its highly collective approach to creating social value. MedShare works with a plethora of stakeholders, all operating independently. As a result, the authors consider MedShare to be a complex structure with dynamic networks of interaction. Furthermore, MedShare also displays the five characteristics that are used to define a SoS. Hence, the authors make the correlation that MedShare could be considered as a complex social enterprise as well as an SoS. The SoS characteristics have are applied to MedShare later in this paper.
While many medical surplus recovery organizations exist, MedShare is considered a leader in the industry holding both a “Best in America” certification by the Independent Charities of America and the best rating possible (four stars) by Charity Navigator for eleven years in a row. An additional reason MedShare was chosen is one of the authors of this paper was contracted by MedShare for a year to explore expansion possibilities and therefore learned the intricacies of MedShare’s operations working closely with the then chief executive officer, chief operating officer, and chief development officer. Secondary data was drawn from a wide range of sources including other journal articles, annual reports, publications and MedShare’s website in order to ensure updated information.
MedShare’s business model primarily focuses on shipping forty-foot containers of medical supplies and equipment from their three warehouses in Atlanta, GA, San Leandro, CA, and Secaucus, NJ. The average container fits 1,000 boxes or 12,000 pounds, and is valued at $150,000-$200,000 (Denizel, Ferguson, and Toktay 2013). MedShare manages several types of constituents in order to accomplish their mission. They manage partnerships with hospitals and manufacturers to provide the medical product and utilize volunteers to sort and stock the product into an online inventory system. The business model operates almost entirely by a volunteer-driven system that utilizes students, area health workers, and individuals with mental or physical disabilities (Brandt 2008). MedShare must also manage their international partners, which include governments and in-country non-governmental organizations (NGOs). Lastly, MedShare ships supplies via containers to different countries and has to manage a complex supply chain. MedShare is funded by individuals, foundations, corporations, government grants, and earned income. Figure 3 provides a visual depiction of the process flow model for MedShare.
To provide a size and scope of MedShare as an organization, in 2014 its annual revenue was $28M with assets worth over $26M. They utilize a volunteer force of over 18,000 each year and employ 45 individuals. In 2014, MedShare shipped 1,738,000 pounds of surplus medical product ($18.8M valuation) via 128 forty-foot containers to health care facilities in 26 countries and equipped over 370 medical mission teams. Since their founding in 1998, MedShare has shipped over 1,000 of these forty-foot containers to those in need. An additional social impact of MedShare’s organization is environmental; the surplus collected by MedShare is diverted from U.S. landfills (MedShare 2014).
4 Five Characteristics of Systems of Systems and Flexibility Dynamic: MedShare
4.1 Autonomy: Conformance vs. Independence
Autonomy in this case takes into account the ability of a system to make choices independently (i.e. managerial, operational), while also making progress towards the SoS’s goal or mission. In the case of MedShare, they work with two types of hospitals in two distinct ways.
The first type of hospital that MedShare works with is a donating hospital within the vicinity of a MedShare collection and distribution center. These hospitals donate their excess medical supplies and equipment to MedShare. MedShare provides the hospital with collection bins, training for hospital staff, and standards on what is recoverable. However, MedShare does not involve themselves in the day-to-day operations of the donating hospitals. These hospitals act as their own complex adaptive systems constantly adapting to both the internal and external environmental circumstances they encounter. While these hospitals conform in some aspects (i.e. they cannot donate expired supplies), they are given the freedom to deem what they wish to donate, particularly in terms of medical equipment (x-ray machines, incubators, etc.) versus medical supplies (sutures, gauzes, etc.). MedShare’s framework allows each hospital to operate independently from MedShare and one another.
The second type of hospital that MedShare works with is a hospital working within a medically underserved area of the world. These hospitals frequently operate with limited resources and income. The surplus that MedShare recovers from the previously mentioned hospitals is then donated to these hospitals. While the hospitals receiving aid must meet certain criteria to be eligible, they are able to use the medical surplus they receive in any way they see fit. They are able to make independent choices about the storage, inventory management, use and distribution of the aid that they receive.
When considering MedShare, a strong conformance approach would be difficult and costly to manage. For donating hospitals, if MedShare managed the entire donating process for hospitals, the process would be too resource consuming for MedShare and too intrusive for the hospitals themselves in terms of the management of resources. For the receiving hospitals, these hospitals are often located in areas of the world that have little infrastructure. The resources expended to oversee the management of donated medical surplus would overwhelm MedShare. Additionally, these hospitals may not be as effective as they are the knowledge experts of their own patients and the needs of those patients.
However, if MedShare allows hospitals to donate in any way they choose and allow these hospitals to donate whatever they wanted, MedShare would be overwhelmed with largely unusable surplus (expired, opened, unsanitary, broken, etc.). Additionally, if MedShare donated this surplus to any hospital (without conducted due diligence on the hospital’s true need), corruption and reselling of the surplus might occur and not benefit those truly in need.
Each constituent operates independently from MedShare and each is recognized as a complex adaptive system. For example, hospitals in MedShare’s network of partners are self-organizing, learning, and adapting to its own internal and external changes, a primary differentiator of a complex adaptive system (McMillan and Carlisle 2007). MedShare also allows receiving hospitals of aid to function in this manner. However, MedShare does not operate in a chaotic manner when engaging these partners, so they are not completely to the right in Figure 4. If MedShare did not have some controls in place to manage in flow and out flow of resources from and to these partners, they would lose effectiveness as an organization. MedShare may, at times, operate towards the edge of chaos, but they seek to remain within the lines of the optimization of space. The conclusion is that MedShare operates slightly more towards an independence approach when considering their autonomy as a SoS.
4.2 Belonging: Centralization vs. Decentralization
Belonging is a characteristic of a SoS that considers the right and ability the constituent systems have to belong to the SoS (Boardman and Sauser 2006). MedShare acts as the hub of all receiving, sorting and distribution of medical surplus supplies and equipment. Their primary means of shipping aid is via forty-foot shipping containers. In fact, all medical surplus sent internationally is sent in these containers. Single-batch items or smaller amounts of surplus are only distributed through medical mission groups.
Within this process, several constituents are involved from the volunteers who sort the supplies, the hospitals that donate the supplies, the funders choosing to fund certain containers, and the hospitals receiving the aid. In considering the characteristic of belonging for Medshare, the organization provides each constituent with the freedom to choose whether or not they are part of the SoS, therefore they are plotted closer to decentralization, rather than centralization. Systems choose to belong to on a cost/benefit basis, “but also to cause greater fulfillment of their own purposes” (Sauser et al. 2008). Systems may choose to contribute and be a part of MedShare’s processes and choose to leave at any time once they have been through the initial screening.
As such, MedShare is not completely chaotic in their belonging characteristic. All hospital donation programs must follow strict MedShare guidelines in terms of what they are able to donate. The same approach is applied to MedShare’s volunteer force, receiving hospitals, donating manufacturers, and funders of the containers. MedShare operates closer to a decentralized organization, as each constituent may choose to participate. However, they are not completely decentralized as each constituent must meet certain standards to be included.
4.3 Connectivity: Platform-centric vs. Network-centric
Connectivity is a characteristic that describes the ability of the constituents to stay connected to other constituents. MedShare’s business model is primarily built on the networks it manages between its constituents (hospitals, government officials, NGOs, donors, volunteers, etc.). A strict form of connectivity between the constituents would result in a platform-centric structure. This would cause a hierarchal type of structure where certain hospitals have better access to receiving aid than other hospitals. MedShare’s approach, while it does exclude some countries due to import regulations, does not dictate which hospitals receive aid. MedShare works with donors, government officials, NGOs and the hospitals to explore aid options and dedicates the same resources to each process and constituent.
4.4 Diversity: Homogeneous vs. Heterogeneous
Diversity is a characteristic that describes the heterogeneity within the SoS. Boardman and Sauser (2006) specifically define increased diversity in a SoS as achieved by “released autonomy, committed belonging, and open connectivity”. Much of the above discussion shows each of these to be the case in MedShare’s SoS. Here, we describe the evidence that diversity exists by a discussion of their constituent systems. There are four aspects to consider when evaluating MedShare’s diversity. The first of which is with the hospitals they donate surplus to. Each hospital must meet certain criteria to be eligible for aid, but they may vary in their purpose (children’s hospitals, maternity hospitals, etc.). However, they all must demonstrate significant need. The second is the medical surplus itself. MedShare ships containers with approximately a third of equipment and two thirds of supplies. Every container is specific to each hospital, as the hospital representative is able to view MedShare’s current inventory and place an order based on their available inventory. The locations that MedShare ships to are also heterogeneous, over 96 countries since their founding in 1998. However, there are countries that MedShare is unable to ship to due to import regulations or unstable governments. Finally, MedShare works with a heterogeneous base of donors, from individuals to NGOs to governments to corporations. MedShare, by and large, does not dictate their funding base. Frequently, organizations come to MedShare with specific hospital requests and if the hospital meets the criteria and the funding is secured, MedShare will aid that hospital. Therefore, the characteristic of diversity, as it relates to diversity, is positioned closer to heterogeneous, as can be seen in Figure 4.
4.5 Emergence: Foreseen vs. Indeterminable
Emergence describes the ability of a SoS to form new properties as a result of a developmental process (Gorod et al. 2008). The two opposing forces in this characteristic are foreseen and indeterminable outcomes. The foreseen emergence of Medshare’s SoS is a container with safe, usable, and unexpired medical surplus. As opposed to the foreseen factor, the delivery of that container is not always a smooth process. When MedShare ships to a country for the first time, a learning process occurs in regards to regulations, government laws/processes, and logistical issues. MedShare often creates new and unique relationships and processes as the SoS includes new countries and new hospitals. However, MedShare’s effective operational practices have systems in place to capture this new learning and the new properties are somewhat anticipated. Therefore, we position the characteristic of emergence closer to the center of the spectrum than other characteristics. An overview of MedShare’s flexibility dynamic can be viewed in Figure 4.
5 Risk Management of Systems of Systems
Due to the highly complex nature of a SoS, risk management should be thought of differently as there are unique challenges when managing risks associated with a SoS. According to Gorod et al. (2008), an assembly represents zero options that results in extremely low adaptability. In a flexible SoS, there is a sufficiently large optimization space, but it is important to stay within this optimization space to keep the risk at an acceptable level. This is shown in Figure 5 below.
Due to process regulations, as well as changes in planning and control, if the flexibility dynamic starts moving towards the left of the spectrum, the organization would not behave as a flexible SoS anymore, but would be functioning either as a system or assembly. This would thereby, reduce the overall flexibility associated with the organization, which would not be ideal for an SEV. Conversely, if the flexibility dynamic swung to the extreme right resulting in excessive flexibility, or towards a chaotic form, the operational risk starts to increase, which is not ideal either.
Figure 5 identifies two regions markedly different from each other in regards to the amount of risk versus the amount of adaptability. For Region A, as the risk decreases, the adaptability increases until the SoS reaches the “Optimization Space of Adaptability.” Once the SoS reaches this area of optimization, the flexibility dynamic should be maintained within the optimization space. If this is not done and it continues moving to the right of the flexibility dynamic spectrum, i.e, heading into Region B, the amount of risk increases but simultaneously the adaptability decreases, which leads the SoS into a chaotic state. As mentioned earlier, it is key for the managing personnel of SEVs to keep this in mind to prevent chaos from occurring in their organizations.
Considering risk management in the context of SoS, a traditional risk management approach may not be the most effective. Since individual complex systems are acquired separately with the goal of delivering a greater capability, a manager’s task of engineering and managing a SoS must also deal with inherent coordination and integration related issues (Katina, Keating, and Ra’ed 2014). The traditional approach to risk management tends to examine and analyze risks separately without a concern for the impact that risk may have on other constituents (Pinto, McShane, and Bozkurt 2012).
Systemic risk is defined as a risk that originates from multiple sources, affects multiple agents, and propagates quickly among individual parts or components of the network (Kaufman and Scott 2003). It could also be thought of as the risk or probability of breakdowns affecting an entire system and not just a breakdown in individual parts or components and is evidenced by correlations among most or all of the parts. This is particularly true when thinking of SEVs where there is the probability of most of the individual parts or components to be interconnected. As a result of this, in the case of SEVs, if they were to consider an individual risk without considering the impact on their constituents, the overall effectiveness of trying to mitigate risks would be lost to a great extent.
Figure 6, adapted from Gandhi et al (2015), provides a general framework for Systemic Risk Management of SoS. This risk management framework is applied to the SEV taken into consideration for the purpose of this study and is explained in the following section.
As shown in Figure 6, there are several constituents of risks. However, for a SoS, there is not much point in evaluating and then trying to mitigate these risks individually. Hence, all risks should be considered together and the interconnectedness as well as the external factors that could affect them should also be understood. (Gandhi et al. 2015; Letens et al. 2008). Every SoS should have a governing body, stakeholders with positions of authority to establish and oversee the SoS’s opportunity and risk mitigation process. Additionally, the constraints, cognitive biases, time critical issues, knowledge management and budget that could affect how the risks and related decision making processes are implemented.
6 General Systemic Risk Management: MedShare
When dealing with risks associated with SoS, not only must the individual risk constituents be taken into consideration, but also their interconnectedness must be considered. Furthermore, external factors that could affect the overall risks associated with the SoS should also be considered.
Consider the example of MedShare analyzing the risk of adding an additional donating/supplying hospital constituent. A traditional risk management approach would only examine the hospital itself. MedShare may consider the size of the hospital, the number of hospital staff members that need training, the quality of the supplies the hospital would donate, etc. However, as MedShare operates as a SoS, they would ideally take a more systemic approach to risk. MedShare would consider the effect of that additional hospital on the volunteer constituents for example. The additional supplies could cause a change in the sorting process or the hospital may add new types of supplies that would require volunteers to become trained on these new supplies. An additional risk is the volunteers’ reaction to these changes. The volunteers could consider the change positively or negatively, which may affect the supply of volunteers or even volunteer morale. MedShare would also consider donor constituents (additional funding may be required), receiving hospital constituents (needs of hospitals), etc.
Table 1 examines the five distinguishing characteristics of a SoS and compares the traditional versus systemic approach in the specific case of MedShare. Table 1 shows that for each characteristic the way the risks would be handled from a traditional approach versus a systemic approach are considerably different. The systemic approach does not just consider a single entity, characteristic or risk but would instead, understand the holistic effects by considering other constituents such as other hospitals, suppliers, donors, vendors, etc.
Traditional versus systemic approach to SoS risk management: Medshare example.
|SoS characteristic||Traditional approach||Systemic approach|
|Autonomy||Would only consider the effect of the risk of providing aid to a hospital based on the autonomy characteristic of that hospital.||Would attempt to understand the effect of all known opportunities and risks when delivering aid to a hospital affecting the autonomies of all systems within the SoS (i.e. donating hospitals, funders, volunteers)|
|Belonging||Would focus on understanding the effect of a including a single hospital on a single system (i.e. inventory system)||Would attempt to understand the holistic effects on all of the systems by bringing on an additional hospital (i.e. donating hospitals/supply, volunteers, logistics).|
|Connectivity||Would view risks associated with each constituent from a stand-alone point of view (i.e. governments)||Would attempt to understand what the effects are of all known opportunities and risks on all systems (i.e. receiving hospitals, funders).|
|Diversity||Would consider risks one at a time and not take into account the diverse nature of the constituents (i.e. logistics shipping to various countries).||Would take diversity associated with opportunity and risk into account and the varying effects they have on systems (i.e. funders, donating hospitals/supply, mission teams)|
|Emergence||Properties are not well understood or anticipated (no learning)||Examines how all systems relate and the multiple opportunities, risks, external factors occurring and affecting those systems (i.e. political situation, technology, liability)|
In continuing with the examination of MedShare, here we apply a visual risk management framework utilizing MedShare. The primary goal of MedShare’s SoS is the “efficient recovery and redistribution of the surplus of medical supplies and equipment to those most in need” (MedShare 2014). MedShare manages the process of shipping to hospitals in over 96 countries, supplies over 30 U.S. safety net clinics, manages a workforce of over 18,000, equips approximately 350 medical mission teams a year, ships approximately 125 forty-foot containers a year, and manages a donor support base equaling over $25M a year (MedShare 2014). All of the constituents listed above are considered key stakeholders in the MedShare SoS.
Figure 7 is a visual depiction of the general systemic risk management framework for MedShare International. The MedShare SoS includes a network of over 55 health care partners (donating hospital). Each constituent (donating hospital) operates as it’s own independent entity, but serves as a key part of the SoS network. All hospitals, although independent, are all interconnected with equal access to the infrastructure in which they donate their surplus. Additionally, the behavior of one, can affect other constituents.
The nine constituents interact with each other and several external factors can play a role in this interaction. External factors are defined as a potential influence that may flow across the SoS’s boundary (Gandhi et al. 2015). The first of these external factors are identified as technology, fuel prices, federal regulations, competition, economic conditions, and the political situation both domestically as well as internationally. These factors pose both opportunities and risk for the MedShare SoS. Each of these factors would have an impact on the entire SoS. The nine constituents of system risk is explained in relation to the MedShare SoS in Table 2.
A systemic risk evaluation of the MedShare system of systems.
|Constituents of systemic risk in relation to MedShare SoS||Operational description|
|Schedule||The (ability or inability) of the MedShare SoS to deliver containers in a timely manner, which is acceptable to stakeholders.|
|Operational||The (ability or inability) of the surplus collection, sorting and stocking operations as well as the overall infrastructure of the SoS to be efficient, as expected by the stakeholders.|
|Financial||The (ability or inability) of the MedShare SoS to raise funds to support both container projects and the overall SoS.|
|Vendor||The possibility of choosing a wrong supplying hospital for the needs of MedShare’s receiving hospitals.|
|Culture||The culture of MedShare’s volunteer program as well as corporate culture could affect the overall operation of the SoS.|
|Reputation||The possibility of MedShare earning a negative image in the eyes of any of its constituents if it fails to live up to their expectations.|
|Flexibility||The (ability or inability) of the MedShare SoS to respond to changing hospital requirements or regulations as well as changing international dynamics.|
|Compliance||The risk of the MedShare SoS not being able to comply with federal or international regulations. This could include the accidently shipment of expired product.|
|Quality||The (ability or inability) to provide safe, usable, and sterile medical surplus supplies and equipment.|
The visual depiction in Figure 7 helps to show how these multiple sources of risk may affect the overall systemic risk of the SoS. For example, if economic conditions were to go down, either due to unemployment, interest rates, etc., this could affect the SoS financial, vendor, and culture constituents of the SoS. Donating hospitals may be less able to donate, financial support may decrease, and the overall morale of volunteers, staff members, and donors may be negatively affected.
This leads to the second type of external factors that are identified as resource, time criticality, stakeholder and funding constraints. A resource constraint could be the size of the warehouse in each of MedShare’s three locations. MedShare must manage all of its systems to be in line with the capacity of the warehouse. Stakeholder constraints would include donating hospitals, receiving hospitals as well as volunteers.
One constraint that is of particular importance to the SoS is time criticality. The reason for this importance is two-fold. MedShare frequently responds with medical aid following natural disasters. Following the 2010 earthquake in Haiti, MedShare responded with 27 forty-foot containers of medical supplies and equipment (medshare.org). The ability to deliver these supplies within a timely matter in situations such as these is crucial. The second reason for this importance is the international regulation of expired products. If any of the supplies in the containers are expired, the importing country officials will reject the entire container.
The ability of the MedShare SoS to manage risk in a systemic fashion, rather than assessing each risk one at a time is critical to the overall success of mission accomplishment. As a SoS can be defined as multiple complex systems operating together to achieve a common goal, MedShare exemplifies a SoS.
7 Discussion and Conclusions
The purpose of this article was to explore what theoretical insights and practical implications can emerge from exploring complexity science within the realm of social entrepreneurship. Specifically, this article purports that SEVs operate as systems of systems and as such, a systemic risk management approach may be more appropriate in managing these ventures. While MedShare is a single example of a SoS operating with a social mission, it is also a starting point for exploring whether most social entrepreneurial ventures (SEVs) tend to operate as SoSs. Referring back to Nicholls (2006) who defines SEVs as “innovative and effective activities that focus strategically on resolving social market failures and creating opportunities to add social value systematically by using a range of organizational formats to maximize social impact and bring about change,” we propose that SEVs can be more effective when they are managed and considered as SoSs. This implies that SEVs should operate on the edge of chaos or in the optimization of space which focuses on flexibility, adaptability, emergence, and learning.
In Weerawardena and Mort’s (2006) study of social entrepreneurial nonprofit organizations, they find that the majority of cases they examine appear to adopt a “highly cautious approach in dealing with risk.” Additionally, they find that key decision-makers will not undertake any project without assessing the costs, regardless of the potential social value that could be created. The system risk management approach described in this study provides a framework for SEV managers to assess risk in way that considers the unique environment that SEVs operate in. It is a framework that allows managers to consider all constituents involved. Carlisle and McMillan (2006) suggest that managers need to keep attuned to where all parts of their organization are operating with regards to the ‘edge of chaos’ zone and ensure that it is not pulled into either extreme (stability or chaos). The case study presented here supports their suggestion and further, provides an example as to how this framework may be applied practically in an SEV.
Research shows that the social venture environment is more tumultuous than their economically driven counterparts (Dorado 2006; Helmig, Jegers, and Lapsley 2004; Waddock and Post 1991). SEVs are both responsive to and constrained by external factors (Weerawardena and Mort 2006). The management of risk becomes even more critical to the SEV success as they appear to operate as SoS’s, which also means they operate on the edge of a chaotic form. We concur with Goldstein, Hazy, and Silberstang (2008) that complexity informing social entrepreneurship and vise versa will lead to a robust theory of social dynamics that can better inform researchers and practitioners about the issues related to social value creation.
In referring to MedShare again, if MedShare were to allow it’s constituents’ complete independence (far right spectrum of autonomy), their reputation, schedule, quality, compliance, and operational risks would be detrimentally affected. MedShare would lose overall effectiveness in regards to their mission. The same can be said for their belonging characteristic. If MedShare allowed any constituent type to join the SoS, the risks involved would cause chaos and the ultimate failure of the SoS. We suggest that SEVs do operate on the edge of chaos, but when a systematic risk management framework is applied, the SoS can operate in an optimal way (i.e. optimization of space).
This systematic risk management approach can also aid SEVs in their transparency to stakeholders. In a social entrepreneurial context, value creation is not as easily reducible to the enrichment of one or a few stakeholders, as it is in traditional entrepreneurship (Murphy and Coombes 2009). SEVs can utilize a holistic approach to both their social value creation and risk management of that creation by applying a systemic risk management framework to include all stakeholders involved. The model presented in this study may be used as a communication tool that enables the leaders of SEVs to justify their activities and decisions.
Funding agencies may also benefit from utilizing a systemic risk management approach. A recent report on family foundation giving trends calls for more neutral and appropriate test beds for exploring ideas and testing options as well as a need for charities to share experiences and learning (Pharaoh, Keidan, and Gordon 2011). Our systemic risk management framework may answer this call to action. It provides a neutral way to compare the affect of one action on all constituents as well as the overall SoS. Funders may also benefit by requiring requests for funding be accompanied by a systemic risk analysis of the project or activity seeking funding.
In conclusion the authors would like to remark that due to the above observations, the SEV selected for the purpose of this paper, does indeed function as an SoS.
8 Future Research
A future research stream would examine a set of SEVs, including those that utilize a systemic risk management approach and those that do not. It would also involve approaching SEVs and asking them to implement this type of approach and tracking results against SEVs not implementing this approach. Future research would also help to verify the authors’ theory that SEVs, such as MedShare, operate as a SoS as well as if there are some SEVs that do and some SEVs that do not. If it is true that some SEVs do not operate as a SoS and some do not, is an SEV that does operate as a SoS utilizing a systemic risk management approach more effective than their non-SoS counterparts that may utilize a traditional risk management approach?
We hope this preliminary explorative study motivates further exploration in the relationship between complexity theory and social entrepreneurship. The complexity of the dual goals of social and economic value creation for SEVs is one we believe could benefit from the application of the framework presented here. Further work in this area can only help those social entrepreneurs working to solve the world’s greatest social problems.
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