1 Is Collective Impact the Destination? A Typology of

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Is Collective Impact the Destination? A Typology of Interorganizational Collaboration

Rong Wang, University of Kentucky, [email protected] Katherine R. Cooper, DePaul University, [email protected] Anne-Marie Boyer, Northwestern University, [email protected] Shaun M. Dougherty, Vanderbilt University, [email protected] Michelle Shumate, Northwestern University, [email protected]

Paper draft prepared for ARNOVA 2018. Please do not cite without authors’ permission.

2 Abstract “Collective impact” has gained prominence as a particular means for organizations to respond to social problems in their community, though there is some concern that the term is over-used or improperly applied. In this study, we draw from research on collective impact, collaborative initiatives and network governance to suggest that what constitutes “collective impact” varies widely by community. We introduce a 2 by 2 matrix to describe a variety ways of organizing partners along two dimensions: a). the degree to which program planning and implementation are enacted by centralized leadership, and b). the degree to which cross-sector partners engage in collaboration. With interview and archival data collected from 28 communities across the United States, this study suggests that these networks may be classified according to one of four approaches: holistic coalitions, low-overhead coalitions, community-led coalitions, and multistakeholder coalitions.

Keywords: Collective impact, coalitions, network governance, cross-sector, education reform

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Is collective impact the destination? A typology of interorganizational collaboration Interorganizational collaboration is a long-used approach for responding to complex social problems within communities, and though various models and approaches to collaboration exist, recent academic and practitioner conversation has focused on one model in particular. Since its 2011 introduction in Stanford Social Innovation Review, collective impact has been touted as a model for improving educational outcomes and health disparities, among other concerns, and has been adopted in hundreds of communities across the United States and the world (Kania, Hanleybrown, & Splansky Juster, 2014; Kania & Kramer, 2011). Despite the popularity of this model, it remains somewhat controversial with respect to its effectiveness (Walzer, Weaver, & McGuire, 2016; Wolff, 2016) and distinction from other collaborative approaches (Christens & Inzeo, 2015). In particular, we question whether the term “collective impact” is overutilized, and whether communities would benefit from the availability of other terms or approaches to describe their efforts. Advocates for collective impact have argued for reserving this term for a specific type of collaborative initiative (see Edmondson & Hecht, 2014; Kania et al., 2014), and previous research suggests that communities that use this term may adopt it differently, with different implications for organizational participation (Cooper, 2017). In this paper, we further explore the nuances among self-described collective impact initiatives and other models of collaboration in the hopes of better understanding why communities adopt different approaches and how they structure their initiatives as a result. This paper is structured as follows. First, we review the definition of collective impact and a growing body of related academic literature. Second, we situate our understanding of

4 collective impact in the broader discourse concerning cross-sector collaboration and communitybased coalitions. Third, we review network literature, and in particular network governance. We then introduce our methodology, a grounded approach that relies on interview data and archival documents from 28 communities across the United States. This paper makes two contributions. First, we introduce what we believe to be one of the largest and most diverse samples of community-based, interorganizational collaboration. Second, we introduce a typology that draws upon elements previously discussed in nonprofit and collaboration literature, such as network governance (Provan & Kenis, 2008), and cross-sector collaboration (Bryson, Crosby, & Stone, 2006). Ultimately, our analysis suggests that communities may be organized according to two factors: the degree to which partners from different sectors engage in collaboration, and the degree to which program planning and implementation are enacted by centralized leadership. We then suggest a 2 by 2 matrix that encompasses four different approaches to local networks: holistic coalitions, low-overhead coalitions, community-led coalitions, and multi-stakeholder coalitions. Examples for each collaboration type explain why a community is located in that quadrant and its key characteristics. Implications of this typology for both researchers and practitioners are discussed. Literature Review Collaboration among nonprofits as well as between sectors are commonly discussed in nonprofit literature (see Gazley & Guo, 2015). Although related concepts surface in both the collaboration and network literatures, each area tends to rely on its own terminology and theories. Some models favored by practitioners, such as collective impact, come with their own sets of terms. In this section, we provide a brief overview of collective impact, placing it in the context of cross-sector collaboration and network governance literatures.

5 Collective impact Collective impact was introduced by consultants in a Stanford Social Innovation Review article as a way for nonprofits to avoid isolated impacts by leveraging their efforts with other cross-sector partners in a measured, managed way (Kania & Kramer, 2011). Specifically, Kania and Kramer (2011) describe collective impact as “the commitment of a group of important actors from different sectors to a common agenda for solving a specific social problem” (p. 36). Kania and Kramer - and many additional consultants or practitioners that utilize the term - have been clear that for an initiative to actually be considered collective impact, it must meet several conditions. Collective impact networks rely upon a “backbone” organization to manage the partnership and requires that partners agree upon a common agenda; partners pursue their shared goal by engaging in shared measurement, mutually reinforcing activities to meet that goal, and continuous communication. In the time since the term first appeared, hundreds of collective impact initiatives have been adopted by communities (Kania, Hanleybrown, & Splansky Juster, 2014) and by such varied groups as school districts, local governments, state governments, philanthropies and foundations, and the White House (Christens & Inzeo, 2015). Initially put forth by consultants, collective impact was met with some skepticism from researchers, who have long focused on related issues such as collaboration and networks (see Christens & Inzeo, 2015). But in recent years, researchers have begun to study collective impact initiatives in addressing social issues such as health disparity, and education reform (Cooper, 2017; Lawlor & Neal, 2016). Their approaches vary. In a reflection of philanthropic interest in collective impact, many of the existing large-scale studies of collective impact are reports commissioned by foundations (e.g., Henig, Riehl, Houston, Rebell, & Wolff, 2016). Some researchers have focused on particular stakeholders in collective impact, such as the role of the

6 facilitators (Gillam, Counts, & Garstka, 2016), or the presence and participation of nonprofit stakeholders in collective impact initiatives (Cooper, 2017). Others have focused on the internal or external contexts of collective impact, finding the internal dynamics a unique context for exploring issues of dialogue and power (see Page & Stone, 2017) or focusing on the community characteristics that lead to the creation of a collective impact initiative (Boyer, Cooper, Dougherty, Wang, & Shumate, 2018). Many have been critical of collective impact, with much of the criticism focusing on two concerns. The first is that from researchers and practitioners is that collective impact relies too heavily on the involvement of formalized power, in particular organizations, the business sector, or leaders (Wolff, 2016; Wolff, Minkler, Wolfe, et al., 2017) and may omit local or grassroots involvement (Christens & Inzeo, 2015); Cooper’s (2017) study suggested that collective impact’s reliance on data and the financial resources required to maintain these initiatives present challenges for nonprofit participation. The second commonly discussed criticism of collective impact is that advocates for this approach do not necessarily place collective impact in a broader context of collaborative and community-based models (Christens & Inzeo, 2015). In the next section, we revisit the broader literature on cross-sector collaboration. Cross-sector collaboration The definition of collective impact specifies that actors from various sectors are needed to solve complex social problems (Kania & Kramer, 2011). However, the concept of different sectors working together predates the collective impact movement. Cross-sector collaboration has been described as “the linking or sharing of information, resources, activities, and capabilities by organizations in two or more sectors to achieve jointly an outcome that could not be achieved by organizations in one sector separately” (Bryson, Crosby, & Stone, 2006: 44).

7 These partnerships may include alliances among government, business, nonprofits and philanthropies, communities, and the public. Bryson et al. (2006) cite several propositions that sound similar to those concepts later stressed by Kania and Kramer (2011) in their description of collective impact. For example, Bryson et al. also emphasize the importance of sponsors, a similar understanding of the problem, continuous trust-building activities, and the gathering and interpretation of data. Additionally, many other scholars have focused on cross-sector partnerships in varying forms, such as the relationship between nonprofits and business (Austin, 2000; Austin & Seitanidi, 2012; Rondinelli & London, 2003) and nonprofits and government (Gazley & Brudney, 2007), or across all three sectors (Selsky & Parker, 2005); many of these researchers include typologies to describe the ways these sectors interact with one another and to what end. Other researchers have focused on typologies that classify interorganizational partnerships depending on activity (Snavely & Tracy, 2000) or the degree to which the partners are integrated (Kagan, 1991; Keast, Brown, & Mandell, 2007) or the formality of the partnership (Guo & Acar, 2005). Despite the existence of so many typologies, however, there is little research to depict the extent to which different sectors are involved, and whether the presence or absence of various sectors influence the way partnerships are structured. Although Cooper (2017) suggested that self-identified collective impact initiatives may vary in terms of the extent to which sectors are represented or play a role within the initiative, previous research has been limited to small samples and has not explored cross-sector engagement across a large sample of networks. Thus, we ask:

8 RQ1: How do different sectors engage in collaborative initiatives in response to community social issues? Though we suspect that the presence or absence of various organizational partners and sectors is important in shaping a network, the structure of networks likely also matters. In particular, we explore the role of network governance in the next section. Network governance mechanism Education collaboratives, such as collective impact, are often depicted as networks. Following Provan and colleagues, we defined these networks as “whole networks” which consist of “multiple organizations linked through multilateral ties” to facilitate achievement of a common goal (Provan, Fish, & Sydow, 2007, p. 482). Examining the whole network helps to uncover how collective impact initiatives evolve and how they are governed within their specific community contexts. Studying the whole network also provides implications for how to better structure multilateral collaboration to accomplish collective goals. Networks are thought to be more flexible and adaptive compared to other forms of collaboration (Powell, 1990; Powell, White, Koput, & Owen‐Smith, 2005). However, as pointed out in Provan et al. (2007: 482) whole networks "are often formally established and governed, and goal-directed, rather than occurring serendipitously" and that "relationships among network members are primarily non-hierarchical and participants often have substantial operating autonomy". This line of research leads to the conceptualization of the network governance model, which focuses on analyzing the structure and process of whole networks (Provan & Kenis, 2008). Network governance framework is built upon the assumption that network coordination could lead to positive network outcomes such as enhanced learning, more efficient use of

9 resources, and increased capacity in addressing complex problems, and understanding the functioning of networks helps to explain why networks produce certain outcomes (Provan & Kenis, 2008). The network governance approach views networks as the unit of analysis, which moves beyond the sum of organizational actors and their links to investigate the variation of networks regarding their structural characteristics. To solve collective problems, the right amount of integration across and among network members is critical, as too little coordination and collaboration may limit the value of adopting a network form; yet too much integration may be too costly and not necessarily beneficial (Provan & Lemaire, 2012). Locating the right amount of integration is related to different roles a network facilitator could play. As laid out in Provan and Kenis (2008), network governance mechanisms could range from self-organizing, to lead-organization governed, and to network administrative organization governed. In self-organizing model, there is no existence of a distinct or formal administrative entity and the network acts collectively. Organizational members are responsible for managing both internal and external management relations with stakeholder groups. In leadorganization governed networks, one organizational member functions as the coordinating body for the network and facilitates the activities of other partners to achieve the collective goal. The lead organization may play the role of fiscal agency to seek and control access to external funding. In network administrative organization governed models, a separate entity is set up to manage and sustain partnership activities. It differs from the lead-organization governance model that the administrative body is not a member of the network providing its own services. The network is externally governed. Another related characteristic is the degree to which an entity has central control over the funding within the network. When a network is more centrally governed, funding management

10 takes on a top-down approach where a government body defines how the funding will be allocated and monitored (Provan & Huang, 2012). In a less centrally governed network, no organizational members dominate in the flow of funding and members have flexibility in how to seek and manage their funding sources. The degree of centralized funding captures the extent to which funds are viewed as a property of a network, thus affording a broad pattern of interaction among members (Provan & Huang, 2012). The other characteristic of network governance is the problem or agenda that brings together an interorganizational network. In more centrally governed networks, there is a focused agenda all the members collectively tackle. It is possible that there is considerable variance across network members regarding their expertise areas and focal issue areas. Centralized governance helps in this situation to narrow down the issue area and strategically coordinate operational decisions. It ensures all members are in agreement with central issues and are committed to align all their effort (Lawlor & Neal, 2016). In less centrally governed networks, members are given enough flexibility to work on multiple agenda. Given the implication of network conveners for questions of a common agenda and program planning, we examine how these networks are organized: RQ2: How do collaborative initiatives self-organize in response to community social issues? The focus on the two elements in organizing partners to solve social issues helps us to uncover the variations that might emerge from diverse communities. Therefore, we examine the following research question to address an overarching inquiry: RQ3: How do cross-sector engagement and network governance shape the dynamics of collaborative initiatives?

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Methods Participants & procedure This study draws from archival data and interview data from 28 collaborative initiatives aiming to improve educational outcomes. See Table 1 for the list of communities sampled. These initiatives were sampled through the following procedures. First, we compiled a database of collective impact networks by identifying member affiliations with national networks such as StriveTogether, Collective Impact Forum, Ready by 21, and the Campaign for Grade-Level Reading. Second, we received participation consent from 14 communities that were on our recruitment list who also self-identifies as collective impact sites. Third, we used the following matching criteria to locate another 14 communities to be added in our sample: geographic (e.g., population density, coverage area, and number of school districts or municipalities), demographic (e.g., race, and poverty rate), and labor market (e.g., unemployment rate, and median income). The second half of our sample tend to be less structured though some of them self-identified as early-stage collective impact sites. Interviews were conducted with all the 28 communities. Questions included their history, mission statement, funding sources, strategies used to align partners, community engagement activities, and data collected. In addition, we also requested for archival data from all these communities, including their founding documents such as Memorandum of Understanding (MOU), meeting notes, and partner roster. Coding procedures Because previous research has hinted at the role of the convener and the representation of the different sectors (Cooper, 2017), and both cross-sector engagement e.g., Austin, 2000; Gazley & Brudney 2007; Selsky & Parker, 2005) and network governance (e.g., Provan &

12 Kenis, 2008) are well covered in collaboration research, we opted to take a deductive approach to coding that relied largely upon procedural methods, or more “standardized” ways of coding data (Saldaña, 2013, p. 67). As a result, we drew upon these previous studies to develop provisional codes that fit our study’s “conceptual framework, paradigm, or research” (Saldaña, 2013: 65), and created codes pertaining to the presence and engagement of different sectors and different forms of network governance. We then relied upon magnitude coding, and pilot-tested our coding choices as recommended by Saldana (2013). Magnitude coding typically builds upon existing codes by adding a category to indicate its presence or intensity (Saldana, 2013). For each community, we coded two dimensions: network governance and cross-sector engagement, on a scale ranging from -2 to 2. Along the dimension of network governance, a community is coded as 2 if it has at least 3 of the following characteristics: a strong presence of a lead agency, a clear top down structure of how the lead agency works with the partner organizations, a structured way of how funding will be distributed, and a clearly defined common agenda. It is considered being most centrally governed. If a community has 1 or 2 of the above mentioned characteristics, it is coded as 1 indicating the network is to some degree centrally governed. If a community has at least 3 of the following characteristics, it is coded as -2 indicating it is most decentrally governed: lack of coordination in project planning or implementation among partners, tackling multiple agendas, no strong presence of a lead agency, and loose structure of how funding will be distributed. If a community has 1 or 2 of the above mentioned characteristics, it is coded as -1 indicating it is to some degree decentrally governed. Along the dimension of cross-sector engagement, a community is coded as 2 if it has the following characteristics: a strong presence of cross-sector partnerships (eg: presence of

13 nonprofits working closely with government or the private sector), open and sustained communication among partners. If a community has some presence of cross-sector partnerships (eg: presence of nonprofits working closely with government or the private sector), but communication among partners is not well sustained (eg: less frequently meeting), it is coded as 1. If a community has a single sector dominating, but still maintains some level of communication among partners, it is coded as -1. If a community has a strong presence of a single sector dominating and no sustained communication among partners, it is coded as -2. Results Our magnitude coding led us to create a continuum along two dimensions: the degree of cross-sector collaboration, and the extent to which the network is centrally governed. Each of these is further described below. Mode of Partnership Engagement To answer RQ1, we found that private, public and nonprofit sectors decide how to engage in collaborative initiatives in response to community social issues by considering the following factors: the availability of shared resources, continuous communication among partners, and the existence of a dominating sector in leading the effort. To further explain the different patterns of stakeholder collaboration, we propose the model of partnership engagement which moves along the continuum of high to low cross-sector engagement. In high cross-sector engagement, there is a strong presence of cross-sector partnerships. For example, nonprofits work with businesses, or government agencies form partnerships with nonprofits. In some cases more than two sectors are engaged in the collaboration. Partners tend to have open and continuous communication which is conducive to the mutually reinforcing activities emphasized by proponents of collective impact.

14 At the other end of the continuum is low cross-sector engagement, where there is a single sector dominating the implementation. The collaboration network could be led by a nonprofit or a government agency. Such limited cross-sector engagement may pertain to the lack of interest, involvement, or resources from cross-sector partners, or may reflect that the size of the community simply does not enable cross-sector engagement. Consequently, there is no sustained communication among partners. Mode of Network Governance To answer RQ2, we propose another model to describe the degree of centralized leadership in planning and implementing programs, ranging from centralized to decentralized governance. In centralized governance, there is a strong presence of a backbone organization, a structured way of distributing funding, and a commonly shared agenda - characteristics originally described in the collective impact model. The role of a backbone organization is to ensure all partners work toward a collective goal through “ongoing facilitation, technology and communications support, data collection and reporting, and handling the myriad logistical and administrative details needed for the initiative to function smoothly” (Kania & Kramer, 2011: 40). In decentralized governance, partners tend to be self-organizing and lack coordination in program planning or implementation. Partner organizations may work together without completely agreeing on the problem to be solved, and thus they tend to work on multiple agendas under distributed leadership. In addition, there is no or no strong presence of a backbone organization and generally the initiative has a loose structure in how to distribute the funding to different programs. As is the case with partner engagement, the tendency to be less organized

15 may be a result of resources available in the community or a conscious choice to work together in this fashion. Based on the coding results of 28 communities, we propose a Community System Solutions framework to capture how organizations collaborate to solve social issues without assuming whether a particular form of collaboration is likely to be more effective than others. The framework is depicted in Figure 1, which is composed of a 2X2 matrix and illustrates four quadrants of community system solutions derived from the intersection of the axes. These quadrants help to explain a diverse set of ways that move beyond the collective impact model. Mapping the Community System Solutions Four quadrants are derived from the intersection of the axes to depict different ways of organizing organizational partners for solving community-based problems. In this section, we draw examples from our sampled communities to showcase the key characteristics of each quadrant; in response to RQ3, we found that depending on community issues at hand, history of partnership in the community, and the level of sufficient resources, the dynamics of collaborative initiatives can be depicted in the following 4 models of coalition. See the mapping of all the sampled 28 communities in Figure 2. Holistic Coalition This quadrant captures networks that are highly centralized in program design and implementation, and also are characterized by high cross-sector engagement. Communities located in this quadrant are often collective impact initiatives that have been operating for a while. Our coding identified 14 of the sampled communities within Quadrant 1. Though these communities share some common characteristics, they also differ in their collaboration patterns.

16 In this section we draw from several communities to explain why they chose to work in this particular structured way. Within the Quadrant 1, the following eight communities were coded as both highest in network governance and diverse partnerships: Summit Education Initiative (SEI) in Akron Ohio, Higher Expectations of Racine County in Wisconsin, ROC the Future in Rochester NY, Achieve the Brown County in Wisconsin, Learn to Earn Dayton in Ohio, Family Success Alliance in Orange County North Carolina, Impact Tulsa in Oklahoma, Flint and Genesee Literacy Network in Michigan. All of these initiatives are affiliated with national networks such as StriveTogether and Collective Impact Forum, and they also identified themselves as Collective Impact. In each community, the backbone organization plays a significant role in aligning partners’ effort toward a collective agenda, and functions as the fiscal agency. All the partners in these networks meet regularly and have a strong focus on data collection and data use. To explain these characters in more detail, we will discuss two communities specifically. The first one is SEI which was established in 1994 to improve reading scores in the Akron Public Schools. With sufficient funding to support programming, SEI has a strong backbone organization that helps manage over 300 partners, including school districts, higher education institutes, investors, and communitybased organizations. All partners collaborate on the principle of “acting on education data.” SEI maintains a solid database. Another example to highlight is ROC the Future, which was founded in 2011 in a community of a rich history of cross-sector collaboration. Its strong backbone agency organizes the coalition around different task forces that are defined by academic outcomes such as school readiness network, attendance network, and college access network. The diverse partnerships are built among local nonprofits, research institutes, local foundations, businesses, and government agencies.

17 Another set of examples are communities that are slightly high on centralized governance and highest on cross-sector engagement, indicating that the backbone organizations tend to play a coordinating role and provide some degree of autonomy for its diverse network members. These include: Coalition for New Britain’s Youth in Connecticut, Westbrook Children's Project in Maine, Youth Thrive in Wake County North Carolina, and Hartford Partnership for Student Success in Connecticut. In these communities, the backbone agency facilitates efforts from diverse sectors but sometimes the initiative as a whole tends to tackle more than one collective agenda. All of these communities are Collective Impact sites except Hartford Partnership for Student Success led by a United Way in the community. The other two communities that are also located in Quadrant 1 but coded as lower on centralized governance and lower on cross-sector engagement: Berkshire United Way in Massachusetts, and Communities that Care in Franklin Massachusetts. These are both Collective Impact sites too. The Berkshire initiative has multiple collective impact initiatives which are set to tackle different social issues such as reducing teen pregnancy, improving early childhood academic performance, building a drug-free community, positive youth development, and parent engagement. Though there is a strong presence of a backbone organization, the network itself advocates the model that heavily relies on volunteers from local communities to implement programs. Communities that Care in Franklin County as a network brings youth, parents, schools, community agencies and local governments to promote the health and well-being of youth people, and thus education is only a part of their bigger picture. The backbone organization views itself as a facilitator of the partners. Holistic coalition requires sufficient financial and human resources to sustain. The advantage of this model is that partnerships’ effort toward a common agenda can be align

18 efficiently. However, the downside is that the network lacks direct community engagement. Most of the communities located in the Quadrant 1 from our sample heavily rely on their local partners to gain access to local communities to raise the awareness of education reform. Low-Overhead Coalition This quadrant is characterized by networks that feature low-cross-sector engagement and emphasize centralized governance. As suggested in the network governance literature, networks that are centrally governed typically have a strong lead agency that takes the initiative to coordinate and manage other partners; however, networks that fall in this category typically have fewer partners to manage and, given the low cross-sector engagement, little representation from business and government sectors. This approach is commonly seen among newer initiatives, managed by an interested or funding party with limited resources that typically rely on existing partnerships in the community. Examples of low-overhead coalitions within our sample include the Impact Committee for Education in Davidson County North Carolina, and the United Way of Saginaw Michigan. These networks founded in 2015 and 2014, respectively, were, at the time of data collection, represent fairly young initiatives that are still building key partnerships and amassing resources. Both networks are centrally governed, in this case by local United Way agencies that also act as funders. Within Davidson County’s Impact Committee for Education, the United Way acts as the lead agency by coordinating meetings and managing communication among partners. That said, their partnerships at time of data collection were few and relied typically among local nonprofits, (e.g.,the Boys and Girls Club of Davidson County, YMCA, and other local nonprofits that offer education and community outreach programs). The partnership includes the local school districts

19 but only one local business; and at the time of data collection, leadership was seeking to recruit local superintendents and policymakers. Likewise, in Saginaw, the United Way coordinates activities among partners, which are similarly limited to local nonprofits and school districts with minimal involvement from business or local government. Two more communities also fall in Quadrant 2: Campaign for Grade Level Reading Marshalltown Iowa, United Way of Miami-Dade Homestead City Florida. We refer to coalitions within this quadrant as “low-overhead” because the convening agency typically has limited human and financial resources. This may be a good model for those collaborative initiatives with limited finances and a limited number of partners to coordinate. With that said, however, coalitions are typically small and rely on participation from the nonprofit sector as opposed to a diverse representation of sectors. Although many early-stage education initiatives we spoke to operated as Low-Overhead Coalition, several of them expressed that there limited in what they could accomplish through this format and ultimately aspired to be more Holistic Coalition. Community-Led Coalition Community-led coalitions in the third quadrant are characterized by decentralized network governance and low cross-sector engagement. The networks are typically dominated by community-based organizations from a single sector (nonprofits or civil society organizations), have informal or irregular communication among partners with no particular organization in the lead, may pursue multiple agendas or have fluid and evolving network goals, and may even have a continually evolving roster of partner organizations. From our sample of 28, the networks that fall within this quadrant are United Way of York County in Biddeford, Maine; My Brother’s Keeper in Mt. Vernon, New York, Campaign for Grade Level Reading in Delray Beach, Florida; and the Howard Local Management Board in Howard County, Maryland.

20 Situated in rural Maine, United Way of York County in Biddeford is a larger coalition of 58 loosely-connected community-led and nonprofit organizations (for e.g. YMCAs and Boy Scouts). The partners in the coalition are voluntarily associated and do not require formal membership. Lead by a group of six staff members at the local United Way, the coalition faced severe funding and capacity issues during their initiation in 2007 with an early childhood coalition, and later in 2014 with a youth-focused coalition. These funding challenges restricted their work to an event-based approach with communication limited to sporadic meetings like annual breakfast events. A lack of buy-in from the local government has added to its list of capacity challenges. Due to these external challenges, the coalition relies heavily on alliances with local nonprofits to assist with events. In certain cases, community-led coalitions are born from the coordinated efforts of a few individual community champions. My Brother’s Keeper in Mt. Vernon, New York, is one such example. Led by two community leaders, the coalition was created in 2016 by an advocate who reached out the school district, mayor’s office, and other community individuals to rally support. Funding from school district grants, and an additional grant from New York City, supported the resulting coalition of 18 organizations. Despite the presence of a lead agency in the coalition, fiscal powers are controlled by the school district leading to decentralization of authority in the network. The coalition has multiple agendas around education ranging from reading to mental health, and coordinating among various partners on these agendas has been a challenge Additionally, similar to United Way of York County, My Brother’s Keeper lacks support from local government agencies. However, we identified two networks in the Quadrant 3 that are led by government agencies: the Campaign for Grade Level Reading (CGLR) in Delray Beach, Florida, and the

21 Howard Local Management Board in Howard County, Maryland. In contrast with previously discussed networks in this quadrant, these two initiatives follow a structured collective impact model which intends to promote centralized governance and cross-sector collaborations. In CGLR Delray Beach, the main players are the city and the school district, but their main agendas are less defined as to include multiple broader goals to achieve. The City of Delray Beach functions as a backbone organization yet leadership and succession in the network are a challenge which impacts goal alignment among partners. That said, the network works hard to maintain accountability to its community by releasing regular reports on grade-level proficiency. Its efforts to improve education in the community have earned it an All-America City Award. Howard County, lead by the Howard Local Management Board, has structured itself as a centralized governance network in writing; but in practice the lead agency is struggling with gaining buy-in from all its partners. Founded in 2017, the initiative has 29 members, with most of them from the government sector like juvenile services, police, social services, and family and children services. The network’s goals comprise ensuring physical and mental well-being for children and young adults, and equitable opportunities to succeed for all youth in the county. The lead agency works mainly as a coordinator and communicator, yet communication appears to be scattered and irregular in the network. That said, they engage closely with the community and focus on racial equity in their outreach efforts. The communities that fall in the community-led coalition quadrant have struggles with partner alignment and goal direction of the network. Funding and capacity issues compound these challenges which is why the sustenance of the network falls upon a few key actors in the community. The term “community-led” is meant to highlight the efforts of these individual and

22 organizational actors in ensuring that education initiatives and motivations are supported in the community even in the face of capacity and partnership alignment struggles. Multi-Stakeholder Coalition The fourth quadrant is Multi-Stakeholder Coalition as it captures the effort of aligning diverse partners to work toward multiple agenda without a backbone organization. Partners in these initiatives self-organize themselves into program design and implementation. Our coding identified six communities that are located in Quadrant 4: Anne Arundel Local Management Board in Maryland, Blue Ribbon Commission on the Prevention of Youth Violence in North Carolina, Building Our Future Kenosha, Success by 6/Smart Start (United Way of Southwest Oklahoma Lawton City), Sparks! La Crosse in Wisconsin, Campaign for Grade Level Reading Grinnell Iowa. Among these communities, the Anne Arundel network, Building Our Future Kenosha and Sparks! La Crosse are Collective Impact. However they are located in Quadrant 4 as the backbone organization does not organize regular meetings for partners and there is no structured collaboration among partners. It is often due to the fact that the initiative is still at its early stage. For example, Sparks! La Crosse and Building our Future are both less than two years ago as of when the data were collected. The Anne Arundel network is managed by a local government agency which focuses on multiple social issues and thus lacks a clear agenda in what needs to achieve at the education level. Partners of the initiative do not engage in continuous conversation. Blue Ribbon Commission aims to tackle multi-generational poverty in a local community by increasing self-sufficiency, social cohesion, collective efficacy and economic stability. With the coordination of the backbone organization, the initiative did significant amount of community engagement to reduce youth violence by working with diverse local paters. The

23 backbone organization views itself as a connector and emphasises the role its partners play in getting the work done. The initiative in Grinnell is led by a local community college and a community foundation. The shared leadership allows the partners to collaborate in their own way. It serves a small community of just over 9000 population, and the partnerships are relatively diverse to attract multiple sectors. The initiative in Lawton city is led by the local United Way, yet the partnerships are loosely organized. There is no attendance requirement for involvement, so attendance and engagement from partners varies. The initiative focuses on multiple education programs which rely on informal connections across partners. Multi-stakeholder coalition is unique in that partners tend to self-organize their effort. Even with sufficient resources to sustain a backbone organization, the lead agency often plays a connector or convener’s role to leave the partners enough autonomy in how to engage in collaboration. This leads to the existence of multiple agendas partners focus on, which could create challenges to how efficiently utilize and coordinate resources. The advantage of this model is that partners engage in direct community engagement. Discussion and Conclusion In this study, we aimed to move beyond communities’ trend towards self-identified collective impact models and to further explore nuances in interorganizational partnerships. With the three research questions proposed, we focus on understanding who the partners are, how they engage in program planning and implementation, and how the degree of cross-sector engagement and centralized governance affect the dynamics of collaborative initiatives. With qualitative data collected from 28 communities across the U.S., we conducted content coding and proposed a community system solution framework to benchmark where each community is located and why.

24 With the coding results, we found that collective impact as defined by Kania and Kramer (2011) and discussed in recent research is not necessarily the intended final destination for all communities. In some cases, we see communities label themselves as collective impact to meet funder expectations and to connect to a broader community of advocates of the model across the country. However, they tweak the model for their circumstances due to capacity concerns or different roles the backbone organization plays. The community system solution framework in this study captures the variations of collaboration models and showcases that collective impact is only one of the pathways to generate system change at the community level. We acknowledge that communities vary by the community size, tenure, population served, problems at hand, goals to achieve, existing social capital in the community, and resources available for mobilization. By no means these communities discussed here represent all education reform initiatives in the U.S. But they provide a set of cases to capture diverse needs and solutions at the community level. The framework could also serve as a guide for communities to configure what is the best way to align partners and implement programs given their specific community context. In the result section, we highlighted the advantages of some models over others and reasons why certain communities might adopt a model other than collective impact. In addition, the framework provides an alternative language for the communities to describe the varieties of ways that they could use to organize their partners for social impact, other than the collective impact model. We suggest that some communities may see changes over the time - for some communities that aspire to collective impact, they may indeed begin in one quadrant and ultimately move to others. However, we suggest that such a progression may not be the goal for

25 all communities, especially those with limited cross-sector engagement, who do not attract significant funding, or those communities that have found success with less centralized, more grassroots approach to organizing. Regardless, we hope that communities will see collective impact as one approach that is available to them and that an awareness of other approaches might lead them to the best path forward for their community. Similarly, we hope that funders may also be open to other approaches of community system solutions as they seek to fund initiatives with the best chance of success in their respective communities.



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27 Gazley, B., & Brudney, J. L. (2007). The purpose (and perils) of government-nonprofit partnership. Nonprofit and Voluntary Sector Quarterly, 36(3), 389–415. https://doi.org/10.1177/0899764006295997 Gazley, B., & Guo, C. (2015). What do we know about nonprofit collaboration? A comprehensive systematic review of the literature. Academy of Management Proceedings, 2015(1), 15409. https://doi.org/10.5465/AMBPP.2015.303 Gillam, R. J., Counts, J. M., & Garstka, T. A. (2016). Collective impact facilitators: How contextual and procedural factors influence collaboration. Community Development, 47(2), 209–224. https://doi.org/10.1080/15575330.2015.1133684 Guo, C., & Acar, M. (2005). Understanding collaboration among nonprofit organizations: Combining resource dependency, institutional, and network perspectives. Nonprofit and Voluntary Sector Quarterly, 34(3), 340–361. https://doi.org/10.1177/0899764005275411 Henig, J. R., Riehl, C. J., Houston, D., Rebell, M., & Wolff, J. (2016). Collective impact and the new generation of cross-sector collaborations for education. Teachers College, Columbia University. Retrieved from https://www.tc.columbia.edu/education-policy-and-socialanalysis/department-news/cross-sector-collaboration/CI-corrected-digital-version-3-1116.pdf Kagan, S. L. (1991). United we stand: Collaboration for child care and early education services. New York: Teachers College Press. Kania, J., Hanleybrown, F., & Splansky Juster, J. (2014). Essential mindset shifts for collective impact. Stanford Social Innovation Review.(Collective Insights on Collective Impact). Retrieved from http://www.ssireview.org/articles/entry/essential_mindset_shifts_for_collective_impact

28 Kania, J., & Kramer, M. (2011). Collective impact. Stanford Social Innovation Review, 1, 36–41. Keast, R., Brown, K., & Mandell, M. (2007). Getting the right mix: Unpacking integration meanings and strategies. International Public Management Journal, 10(1), 9–33. https://doi.org/10.1080/10967490601185716 Lawlor, J. A., & Neal, Z. P. (2016). Networked community change: Understanding community systems change through the lens of social network analysis. American Journal of Community Psychology, 57(3–4), 426–436. https://doi.org/10.1002/ajcp.12052 Page, S., & Stone, M. M. (2017). Power, conflict, and collaborative decisions in partnerships to improve public education. Presented at the ARNOVA, Grand Rapids, MI. Powell, W. (1990). Neither market nor hierarchy: Network forms of organization. Research in Organizational Behaviour, 12, 295–336. Powell, W. W., White, D. R., Koput, K. W., & Owen‐Smith, J. (2005). Network dynamics and field evolution: The growth of interorganizational collaboration in the life sciences. American Journal of Sociology, 110(4), 1132–1205. https://doi.org/10.1086/421508 Provan, K. G., & Kenis, P. (2008). Modes of network governance: Structure, management, and effectiveness. Journal of Public Administration Research and Theory, 18(2), 229–252. https://doi.org/10.1093/jopart/mum015 Provan, Keith G., Fish, A., & Sydow, J. (2007). Interorganizational networks at the network level: A review of the empirical literature on whole networks. Journal of Management, 33(3), 479–516. https://doi.org/10.1177/0149206307302554 Provan, Keith G., & Huang, K. (2012). Resource tangibility and the evolution of a publicly funded health and human services network. Public Administration Review, 72(3), 366– 375.

29 Provan, Keith G., & Lemaire, R. H. (2012). Core concepts and key ideas for understanding public sector organizational networks: Using research to inform scholarship and practice. Public Administration Review, 72(5), 638–648. Rondinelli, D. A., & London, T. (2003). How corporations and environmental groups cooperate: Assessing cross-sector alliances and collaborations. The Academy of Management Executive (1993-2005), 17(1), 61–76. Saldaña, J. (2013). The coding manual for qualitative researchers (Second edition). Thousand Oaks, CA: SAGE Publications Ltd. Selsky, J. W., & Parker, B. (2005). Cross-sector partnerships to address social issues: Challenges to theory and practice. Journal of Management, 31(6), 849–873. https://doi.org/10.1177/0149206305279601 Snavely, K., & Tracy, M. B. (2000). Collaboration among rural nonprofit organizations. Nonprofit Management and Leadership, 11(2), 145–165. https://doi.org/10.1002/nml.11202 Walzer, N., Weaver, L., & McGuire, C. (2016). Collective impact approaches and community development issues. Community Development, 47(2), 156–166. https://doi.org/10.1080/15575330.2015.1133686 Wolff, T. (2016). Ten places where collective impact gets it wrong. Global Journal of Community Psychology Practice, 7(1). Retrieved from https://www.gjcpp.org/en/resource.php?issue=21&resource=200

30

Table 1. A list of sampled communities

State CT NC ME NC CT NC ME NC MA MA NY NY OH OH FL FL WI WI OK OK MI MI MD MD WI WI IA IA

Network Name Coalition for New Britain's Youth Family Success Alliance (Orange County) Westbrook Children's Project (Portland) Youth Thrive Hartford Partnership for Student Success CT United Way of Davidson County United Way of York County Blue Ribbon Commission Communities That Care Coalition (Franklin county) Berkshire United Way ROC the future (Rochester) My Brother's keeper alliance Mt Vernon Summit Education Initiative (Akron) Learn to Earn Dayton (Dayton) Delray Beach, Campaign for Grade Level Reading United Way of Miami-Dade (Homestead City) Higher expectation Racine Building our future Kenosha Impact Tulsa Lawton City Flint & Genesee Literacy Network Saginaw Howard Anne Arundel County Sparks! La Crosse Achieve Brown County CGLR Marshalltown CGLR Grinnell

ID CI1 CI2 CI3 CI4

Scope of work City level County level City level Couty level

Collective impact Or Not Collective impact Collective impact Collective impact Collective impact

EN1 EN2 EN3 EN4

City level Couty level City level City level

No No No No

CI6 EN6 CI8 EN8 CI5 EN5

Couty level Couty level City level City level Couty level Couty level

Collective impact Collective impact Collective impact No Collective impact Collective impact

CI9

City level

Collective impact

EN9 CI7 EN7 CI10 EN10 CI11 EN11 EN12 CI12 EN13 CI13 CI14 EN14

City level County level County level City level City level County level County level County level County level City level City level City level City level

Collective impact Collective impact Collective impact Collective impact No Collective impact Collective impact Collective impact Collective impact Collective impact Collective impact Collective impact Collective impact

31

Figure 1: Community System Solutions

32

Figure 2. Mapping of the sampled communities