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Mapping (mis)alignment within a collaborative network using homophily metrics [1]

['Kimberly Pugel', 'Department Of Civil', 'Environmental', 'Architectural Engineering', 'University Of Colorado Boulder', 'Boulder', 'Co', 'United States Of America', 'Amy Javernick-Will', 'Cliff Nyaga']

Date: 2022-09 

Abstract Collaborative approaches can overcome fragmentation by fostering consensus and connecting stakeholders who prioritize similar activities. This makes them a promising approach for complex, systemic problems such as lack of reliable, safe water, sanitation, and hygiene (WASH) services in low-income countries. Despite the touted ability of collaborative approaches to align priorities, there remains no comprehensive way to measure and map alignment within a network of actors. Methodological limitations have led to inconsistent guidance on if, and how much, alignment is needed around a common vision (e.g., universal, reliable access to WASH) and/or around an agreed set of activities (e.g. passing a bill to promote water scheme maintenance models). In this work, we first define alignment as the extent to which actors work with others who share priorities. We then develop and test a method that uses social network analysis and qualitative interview data to quantify and visualize alignment within a network. By investigating how alignment of two strong, well-functioning WASH collaborative approaches evolved over three years, we showed that while alignment on a common vision may be a defining aspect of collaborative approaches, some alignment around specific activities is also required. Collaborative approaches that had sub-groups of members that all prioritized the same activities and worked together were able to make significant progress on those activities, such as drafting and passing a county-wide water bill or constructing a controversial fecal sludge disposal site. Despite strong sub-group formation, networks still had an overall tendency for actors to work with actors with different prioritized activities. While this reinforces some existing knowledge about collaborative work, it also clarifies inconsistencies in theory on collaborative approaches, calls into question key aspects of network literature, and expands methodological capabilities.

Citation: Pugel K, Javernick-Will A, Nyaga C, Mussa ME, Dimtse D, Henry L, et al. (2022) Mapping (mis)alignment within a collaborative network using homophily metrics. PLOS Water 1(9): e0000044. https://doi.org/10.1371/journal.pwat.0000044 Editor: Matthew C. Freeman, Emory University, UNITED STATES Received: October 29, 2021; Accepted: August 15, 2022; Published: September 21, 2022 Copyright: © 2022 Pugel et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: All relevant data has been uploaded alongside the submission. Funding: This work was completed with financial support from the Sustainable WASH Systems Learning Partnership through USAID under the terms of the Cooperative Agreement AID-OAA-A-16-00075 to the University of Colorado Boulder (USAID: AJW, KL). The contents are the responsibility of the University of Colorado Boulder Sustainable WASH Systems Learning Partnership and do not necessarily reflect the views of USAID or the United States Government. The funders reviewed study design, data collection and analysis, but were not involved in the preparation of the manuscript. Competing interests: The authors have declared that no competing interests exist.

1. Introduction In a collaborative approach, all relevant stakeholders discuss, plan, and carry out a set of agreed activities to reach a shared vision [1]. When that shared vision requires broader systems change, such as providing universal access to water, sanitation, and hygiene (WASH) services, stakeholder perspectives are diverse and often conflict with one another [2, 3]. Fragmentation remains a significant barrier in the rural WASH sector, where, for example, some stakeholders prioritize government-led management of services [e.g. 4, 5], others prioritized community-based management [e.g. 6], while even others prioritize professionalized or privatized management [e.g. 7]. This fragmentation is twofold, meaning that stakeholders are prioritizing different activities, and that stakeholders that do have similar priorities are not working together. A collaborative approach overcomes fragmentation by (a) fostering consensus and (b) connecting stakeholders with similar prioritized activities. Consensus-building has been extensively studied in the literature on collaborative approaches (see for example, [8, 9]), thus, in this work, we focus on the latter: the process of connecting stakeholders with similar priorities, hereafter referred to as “alignment”. Understanding alignment requires an understanding of stakeholder priorities and stakeholder relationships. Yet, existing methods and theory have yet to comprehensively measure and map alignment. At most, past work has quantified or mapped either priorities or relationships, but not priorities and relationships together. Specifically, past work has mapped the extent to which stakeholders identify with the collaborative approach’s vision [10, 11], mapped stakeholders based on their priorities [12], analyzed whether similar types of stakeholders are more likely to work together in a collaborative network [10], and identified whether stakeholders perceive alignment in the network [13]. These comprehensive efforts have made great strides in understanding stakeholder alignment and, in doing so, have pushed current methods and analytical techniques to their limits. To date, no research has mapped priorities and relationships together as a way of quantitatively measuring alignment. These comprehensive efforts have made great strides in understanding stakeholder alignment and, in doing so, have continued to push current methods and analytical techniques forward. However, a well-tested metric for alignment could be used to quantify and investigate the processes of alignment within collaborative approaches. In this work, we first define alignment, then develop a method to assess alignment, and then use the method to investigate alignment within two collaborative approaches as they evolve over three years.

2. Literature review In the international development sector, collaborative approaches are seen as a “highly effective means to scale and sustain impact through increased alignment and coordination between stakeholders”[14] (p. 18). This makes them a promising approach for complex, systemic problems such as lack of reliable, safe WASH services in low-income countries, and, as a result, they are increasingly implemented in the WASH sector. Yet, despite a wealth of practical knowledge and a growing body of literature on collaborative approaches in WASH (see Pugel et al. [2]), existing guidance has not investigated alignment. Our study starts to address this gap by drawing from the literature on collaborative approaches and network theory, and then applying it to cases in the WASH sector. When it comes to alignment, the literature on collaborative approaches and network theory are limited in four important ways. First, literature on collaborative approaches cite the importance of alignment but cannot agree on its granularity, i.e. whether alignment is needed on visions and/or activities. Second, network analysis techniques have sought to map alignment but have only mapped priorities and relationships separately, not together. Third, network literature seeks to measure network strength but does not consider alignment, in part due to the aforementioned methodological limitations. Fourth, homophily analysis has been widely used to map the extent to which actors with similar attributes are working together, but has only been used to assess quantitative, objective attributes, rather than qualitative, subjective attributes such as priorities. These limitations are described in more detail below. 2.1. Review of collaborative approaches and alignment Collaborative approaches convene a group of diverse stakeholders to collectively accomplish shared, complex visions that cannot be solved by any single entity working alone. The process they follow largely entails the facilitation of a collaborative group to agree on a vision (e.g. universal access to water services in a district) and activities to implement to achieve the vision (e.g. establishing and regulating a private maintenance service provider). Collaborative approaches then encourage stakeholders to coordinate and work together to implement agreed activities, sometimes requiring a change to their own organizational agendas. Members must dedicate some of their own time, and sometimes resources, to implement these activities. Literature studying collaborative approaches have noted the importance of alignment as an outcome [15, 16] but has not clearly defined what it means. For instance, the collective impact framework claim a key outcome is that actors are “implementing aligned action” [3]. Collaborative governance literature has cited an outcome as being actors “carry[ing] out actions… that align with the intentions of the [collaborative group]” [16] or the extent to which there is a “mutual alignment of agendas” [17]. Communication scholars define the idea of “co-orientation” as “a process whereby people align their actions in relation to common objectives through an ongoing dialectic of conversations and texts” [18, as cited in 19]. Loosely, literature on collaborative approaches uses the term to mean the process of actors working together with the same intentions. We build off these key concepts for this work, defining alignment as the extent to which actors work with others who share a common objective. Objectives of members have largely been broken down into two key areas: a vision and activities that could reach that vision. Different activities are different “means to an end”, with the “end” being the common vision. Vision alignment would therefore mean that actors who agree on the common vision are working with one another, while activity alignment would mean actors work with others who agree on a common set of activities. Yet, existing theory on inter-organizational collaboration, ironically, disagrees on the extent of alignment that is needed. Some guidance [1, 20] cites the importance of vision alignment, while other sources [3, 21] argue for activity alignment. In practice, it is more challenging to achieve activity alignment as it requires more time, resources, and occasionally, conflict resolution and facilitation skills. This is especially true in the WASH sector, in which almost all stakeholders are in some way working towards the vision of improved access, reliability, and sustainability of services, but many actors seek to reach this vision through different activities. Thus it is important to have guidance on how much alignment is necessary for collaborative approaches, as well as how the process of alignment occurs. 2.2. Review of networks and alignment Mapping alignment requires an understanding of both the relationships between stakeholders as well as each stakeholder’s priorities. Network analysis is a popular technique used to systematically map relationships of stakeholders, typically referred to as “actors”[22], making it well-suited for understanding alignment. In the WASH sector, network analysis is increasingly used by organizations and by facilitators of collaborative approaches to better understand stakeholders and relationships [23–25]. A few network researchers have made significant strides in trying to study alignment using network analysis, but all are limited in significant ways. Largely, existing work has analyzed actor perceptions of alignment rather than collecting data on actual priorities and real relationships. For instance, Nowell [13] used network analysis to analyze ties of “shared philosophy”, signifying which actors are perceived by others to “share a similar philosophy about the collaborative’s targeted issue and how it should be addressed” (p. 199). The study found that perceptions of alignment, meaning shared “beliefs and understandings about the issue”, was important for meaningful change [13]. However, modeling peer’s perceptions of alignment, rather than whether the actor’s priorities are aligned or not, limits the study. Ogada et al. [10] looked at perceived alignment as an attribute of actors in a network analysis study and found that actors with higher interest in the group’s vision had higher perceived influence in the network, finding that stakeholders “who know and astutely exploit their interests and sphere of influence are more effective participants” in a collaborative approach (p. 287). This is supported by findings from Kolleck et al. [11], who investigated predictors of ‘common vision identification’ within Collective Impact networks and found that members more central to the network had higher identification with the groups’ visions. While these two studies made strides to investigate alignment, they focused on the extent to which individuals identified with the network’s vision, without dissecting the different individual priorities of actors. Walters et al. [12, 26] overcame these limitations by cross-tabulating each individual actor’s values and perceived influence to investigate conflict and power within a network, making them the first to map alignment of actors’ prioritized values, rather than perceived alignment. Yet, they did not investigate actor relationships in addition to their objectives, though this was suggested in the study as a needed area of future work. Thus, as current methods stand, there remains no way to measure or quantify the extent to which actors work with others that share their priorities. 2.3. Review of network strength and alignment By mapping all relationships between actors, network analysis can investigate where relationships occur, where they do not, and “the implications of both for achieving outcomes” [27]. Network researchers have sought to measure networks by their ‘strength’ to find metrics that can predict what influences the ability of the network to accomplish those outcomes, including their “ability of a group to coordinate” [28] or their ability to be “connected in ways that facilitate achievement of a common goal” [27]. Largely, these studies focus on strength metrics that investigate the structure of the network, such as number of connections [28], strength of connections [13], reciprocation of connections [29], and knowledge about the network structure [30, 31]. However, the literature on collaborative approaches has recommended investigating process-oriented aspects of network strength [32, 33], which could include relationships amongst those with shared priorities. Investigating alignment as a strength metric would also respond to calls from communication scholars who argue that structure-oriented research largely misses much of the important processes that are central to collaborative work [32, 34]. A well-tested metric for alignment could be used to measure and, in turn, comprehensively investigate the process of alignment. 2.4. Review of homophily metrics and alignment One promising network analysis technique has high potential as a way to map alignment: homophily, meaning the tendency for similar actors to be connected [33]. This method has been around for many years, allowing network researchers to investigate “both the attributes of actors and relations among them…. [to explain] of how and why they decide to cooperate with each other” [35]. Homophily analysis investigates the tendency of actors to connect to other actors with similar attributes/ characteristics, including socioeconomic characteristics [33], sub-group membership [36], geographical locations and culture [37], political affiliation [38], and demographics such as race, gender, or culture [39]. Homophily analyses typically focus on categorical attributes. Those that focus on quantitative network parameters, for example the node degree or total number of connections a node has to others in the network [40], are referred to as assortativity analyses [41]. However, some studies have inconsistently also used assortativity calculations for categorical attributes [42, 43], meaning the terms are somewhat interchangeable. Despite the overlap, we will refer to it as homophily analysis. A central focus of homophily analyses is understanding whether, and how much, actors in a network tend to connect to similar actors, i.e. those with the same attribute, compared to different actors, i.e. those with different attributes. Largely, many have found that actors with similar attributes are more likely to be tied together. Shrestha and Feiock [39] explain this phenomenon through transaction costs, where creating ties with similar actors has lower transaction costs than creating ties with dissimilar actors. They illuminate this further: “It is easier for a local jurisdiction to bargain and negotiate with other jurisdictions when they have a similar demographic composition, because their preferences and motives are likely to be similar…. [and] increases the likelihood of collaborative relationships” (p. 14, emphasis added). They assume that similarities in demographics and socioeconomic characteristics translates directly to also mean similar ideas and priorities for actions or activities. Arguably, this “birds of a feather flock together” theory makes sense logically, where actors with similar priorities, objectives, and motives face lower transaction costs of implementing activities if they work together. However, these assumptions may not hold when looking at collaborative networks that convene actors with diverse priorities [44]. For instance, Ogada et al. [10] conducted a homophily analysis within a collaborative network to investigate if actors tend to connect to similar institution types (e.g. government agencies with government agencies). They found that actors tended to collaborate with different types of institutions (e.g. government ministries with local water user associations) because each type of actor brought different strengths to the table. While Ogada et al.’s study [10] laid important groundwork for investigating alignment, they only looked at homophily by actor type, not by actor priorities. This reveals a gap, where no homophily studies have comprehensively investigated the extent to which a strong, collaborative network is comprised of actors working with those with similar priorities. Greater research is thus needed to understand the extent to which like-minded actors work together in collaborative approaches, and homophily analysis is a promising technique to do so. 2.5. Research questions As described in the literature review, clear gaps remain in the literature on collaborative approaches and networks. These gaps stem from two highly related limitations: first, there is no comprehensive way to measure alignment, and second, due to the lack of methods, alignment has not been comprehensively investigated over time in strong, well-functioning collaborative approaches. To address these gaps, this study focused on two research questions: (a) How can alignment be measured? (b) How do actors in strong collaborative networks align over time?

5. Discussion With collaborative approaches increasingly applied in the WASH sector, and with alignment cited as a key outcome metric, it is important to understand alignment within collaborative approaches in WASH. Further, with increasing use of network analysis as a tool in the WASH sector [23–25], using network analysis to understand alignment is a natural fit for the WASH sector. This study starts to fill this gap. Despite the two well-functioning collaborative networks having an agreed common vision of safe and reliable sanitation or water services for the area, the networks were largely misaligned with an overall tendency of stakeholders to be connected to others with different priorities (i.e., with negative homophily scores). When looking across the five unique snapshots of the networks at different points in time, four snapshots had negative homophily scores, meaning that stakeholders had an overall tendency to connect to others with different priorities. This means that these successful networks, as a whole, were misaligned. Yet, existing literature would have suggested that a ‘strong’ collaborative network would be aligned, having a positive homophily score between 0 and +0.5. We saw great variation in homophily scores across networks that were able to collaboratively implement actions, for example, how the Kitui WASH Forum support network in 2018 had a homophily score of +0.07 and was able to collectively develop and pass a County-wide Water Bill to standardize water scheme management models and support professionalized maintenance. However, the Debre Birhan Learning Alliance, despite their ability to construct a temporary Fecal Sludge Dumping site in 2020, had a negative homophily score of -0.05. Analyzing alignment over time may provide additional insights and a starting point for future work, for example the Debre Birhan Learning Alliance showing an overall positive trend of +0.08 in its homophily score from 2019 to 2020. These results call into question the assumption that alignment is an indicator of a successful network, supporting previous work from Ogada et al. [10] which found that actors in collaborative networks tended to collaborate with different types of institutions. This would be expected in collaborative approaches that bring together diverse actors with very different priorities [21, 44]. Conflict and disagreement, which may cause the misalignment, may help spur innovation and help solve complex, systemic problems. Investigating network strength through a process-focused lens, in an otherwise structure-focused body of literature on network strength [32, 33], allowed for a more nuanced understanding of network strength. Ultimately, it is the integration of this method with in-depth case knowledge that allowed for a more complete understanding of alignment. Despite misalignment in the network as a whole, sub-groups of actors with the same priorities were working together closely (not just sharing information). The sub-groups still had many intra-group connections (i.e. ties connecting actors with different priorities) which also helps to stymie “group think”, where the introduction of new ideas is limited, and creates space for new ideas to flow into the subgroup [9, 51, 52]. Critical to understanding alignment in a collaborative group is understanding what the priorities are, where sub-groups form, and who is in those sub-groups. A few key recommendations for future users of this method have emerged—whether used in practice to aid facilitation of collaborative approaches, or for research. First, homophily analyses should be supplemented by clear visualizations for interpretation (for example through NodeXL software [45]), if taken as standalone calculations they do not allow for full interpretation of network structures. Homophily analyses should also look at relationships that indicate working together (such as via support, training, coordination, sharing resources, or joint implementation) rather than just the sharing of information. Network analysts and implementers of collaborative approaches should both place greater emphasis on subgroups within networks, as they are an important structural aspect of how work gets done and may also help capture influence points within a network. Second, homophily analyses can help map how alignment evolves over the course of the establishment of a collaborative network, with early years creating some misalignment as new ties are formed often for reasons other than priorities, then greater alignment as the group starts to move toward action. Thus we recommend that studies of collaborative networks should look at changes over time, rather than at single snap-shots in time. Iteration at an annual frequency provided a nuanced view into network evolution, especially in a newly-formed network (Debre Birhan). We also advise researchers assessing homophily to directly interview actors about their prioritized activities rather than use other attributes as proxies, as is the current norm in network research. We found a key limitation of using the homophily analysis to be that the calculation alone cannot tell the entire story of alignment within a network and should be supplemented by visualizations to adequately investigate sub-group alignment. A valuable addition to this method would be if a network analysis software package such as igraph could develop a metric that could reflect connectivity within a sub-group based on an attribute, accounting for random tie formation.

6. Conclusions and contributions Implementers of collaborative approaches expect to build and strengthen networks of local stakeholders that can work together to improve water and sanitation services. These approaches can be designed to overcome fragmentation by fostering consensus and connecting stakeholders that share priorities. The latter goal, connecting stakeholders that share priorities, is referred to as “alignment”. Yet, to date there is no evidence for if, how, or why this alignment occurs. Developing a method to quantify alignment is a critical first step. By simultaneously mapping stakeholder priorities and relationships, we can measure and visualize alignment. Our novel approach contributes to practice by allowing organizations to use network analysis tools more effectively as an adaptive management tool to assess and monitor progress of collaborative approaches. We argue that mapping alignment and misalignment over time can help facilitators of collaborative approaches better manage their group to create sub-groups that both (a) ensure those with similar priorities are working together closely, and (b) allow some connections to others to allow them to solve complex problems and minimize group think. A comprehensive understanding of alignment can also add evidence to deepen conversations around alignment of incentives and financial flows. As these findings also lay the groundwork for quantifying and visualizing alignment, we believe that with further study and use, the metric can become a way to understand network strength by looking at alignment of subgroups. This work also contributes both to network theory and the various theories on collaborative approaches. We build on network theory by bringing qualitatively-analyzed interview data into a homophily analysis to directly assess alignment rather than using proxies. Existing literature has largely sought to understand alignment using proxies because current network analysis tools cannot directly map alignment. We thus build on theory by expanding the range of applications of the homophily analysis [33], developing a way to understand the process through which collaborative networks align and move beyond conventional, structure-focused methods [32, 35]. This study was also the first to map how alignment changes over time, documenting how actors in a collaborative network first forged new connections to many actors regardless of their priorities, followed by a gradual prioritization of an activity and the coalescing of ties as actors work together to implement the activity. Collaborative governance and other theories of collaboration have cited alignment as a key outcome of collaborative approaches, but prior to this study, there has been no effective way to map or quantify alignment. As a result, there also has been too little investigation into how aligned collaborative groups actually are, and the extent of alignment required. We showed that while consensus on visions may be required, as suggested by previous research [2], some alignment on priorities for how that vision will be accomplished may be required within sub-groups. In our cases, collaborative networks that were able to successfully implement activities together had one or more well-connected sub-groups of aligned actors amidst a larger network of diverse priorities for activities, but the overall network was misaligned. This reinforces the established idea a certain degree of ‘misalignment’ is pivotal to collaborative approaches as it allows for diverse perspectives to be brought into solutions and creates the conditions for innovation, rather than the conditions for ‘group think’ [10]. This shifts the conversation away from whole-network alignment and elevates the importance of the sub-group. As such, we encourage network- and systems- theorists to further investigate the role of the sub-group in applications beyond collaborative approaches, such as network strengthening and systems strengthening. The novel method developed for this work allows researchers and practitioners to quantify and visualize alignment using network analysis and case knowledge. With these tools and evidence, future work can map how collaborative approaches align activities and trigger collective action.

Acknowledgments This work would not have been possible without the organizations and government entities who are members of the two collaboratives featured as cases in this study. In addition, we appreciate the data collection support from LINC Local, IRC-WASH, and Oxford University. We also acknowledge the high-quality data collection efforts of Belay Mulat, Abebaw Zerfu, and Pauline Kiamba.

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