Factor and Actor Networks: Alignment of Collective Action Groups for Water Sustainability in Ethiopia

Conference Paper (PDF Available) · June 2018with 216 Reads
Conference: Engineering Projects and Organizations Conference, At Brijuni, Croatia
Abstract
As the international water, sanitation, and hygiene (WASH) sector moves towards approaches that strengthen the wider political, social, technical, institutional, and environmental sub-systems that keep WASH infrastructure functioning, there is a growing need to understand the organizations, governments, and people that manage and rely on these sub-systems. As no single actor has the ability to manage all elements of these complex systems on their own, many approaches are turning to principles of “collective action” that aim to enable efficient, collaborative, stakeholder-driven action. One of the key steps in a “collective action” approach is converging or aligning the diverse agendas, mindsets, and priorities of the actors in the collaborative partnerships toward a single common goal or shared vision. As perspectives are diverse, dynamic, and potentially contradicting, there is a need to elucidate these perspectives and understand how to design collaborative partnerships so they operate effectively. We partner with the Sustainable WASH Systems Learning Partnership to explore how to assess alignment of priorities by analyzing network relationships and prioritized actions of actors engaged in a collaborative partnership. We conducted semi-structured interviews with all partners (n = 22) engaged in a collective action group in a single district in Ethiopia, then qualitatively analyzed each actor’s priority and assigned these as actor attributes in a network analysis. We then analyzed the network and the actors that prioritized similar actions based upon sub-group densities and external-internal indices. This work contributes to theory by exploring perspectives and alignment within a network; it potentially aids practice by showing the extent of alignment of actors within the network toward a common goal.
Working Paper Proceedings
FACTOR AND ACTOR NETWORKS: ALIGNMENT OF
COLLECTIVE ACTION GROUPS FOR WATER
SUSTAINABILITY IN ETHIOPIA
Kimberly Pugel, University of Colorado Boulder, USA
Amy Javernick-Will, University of Colorado Boulder, USA
Jeffrey Walters, University of Diego Portales, Chile
Karl Linden, University of Colorado Boulder, USA
Proceedings Editors
Bryan Franz, University of Florida and Iva Kovacic, TU Wien
© Copyright belongs to the authors. All rights reserved. Please contact authors for citation details.
16th Engineering Project Organization Conference
Brijuni, Croatia
June 25-27, 2018
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FACTOR AND ACTOR NETWORKS:
ALIGNMENT OF COLLECTIVE ACTION
GROUPS FOR WATER SUSTAINABILITY IN
ETHIOPIA
Kimberly Pugel1, Amy Javernick-Will2, Jeffrey Walters3, and Karl Linden4
ABSTRACT
As the international water, sanitation, and hygiene (WASH) sector moves towards
approaches that strengthen the wider political, social, technical, institutional, and
environmental sub-systems that keep WASH infrastructure functioning, there is a
growing need to understand the organizations, governments, and people that manage
and rely on these sub-systems. As no single actor has the ability to manage all elements
of these complex systems on their own, many approaches are turning to principles of
“collective actionthat aim to enable efficient, collaborative, stakeholder-driven action.
One of the key steps in a “collective action” approach is converging or aligning the
diverse agendas, mindsets, and priorities of the actors in the collaborative partnerships
toward a single common goal or shared vision. As perspectives are diverse, dynamic,
and potentially contradicting, there is a need to elucidate these perspectives and
understand how to design collaborative partnerships so they operate effectively. We
partner with the Sustainable WASH Systems Learning Partnership to explore how to
assess alignment of priorities by analyzing network relationships and prioritized actions
of actors engaged in a collaborative partnership. We conducted semi-structured
interviews with all partners (n = 22) engaged in a collective action group in a single
district in Ethiopia, then qualitatively analyzed each actor’s priority and assigned these
as actor attributes in a network analysis. We then analyzed the network and the actors
that prioritized similar actions based upon sub-group densities and external-internal
indices. This work contributes to theory by exploring perspectives and alignment
within a network; it potentially aids practice by showing the extent of alignment of
actors within the network toward a common goal.
KEYWORDS
Collaboration, water governance, sustainable systems, network analysis, organizational
communication
1 PhD Student, Civil and Environmental Engineering Department, University of Colorado Boulder,
USA, Phone +1.530.615.9319, email kimberly.pugel@colorado.edu
2 Associate Professor, Civil and Environmental Engineering Department, University of Colorado
Boulder, USA, Phone +1.720.220.7220, email amy.javernick@colorado.edu
3 Assistant Professor, Civil and Environmental Engineering Department, University of Diego Portales,
Chile, Phone +56-9569-2532, Email: jeffrey.walters@mail.udp.cl
4 Professor, Civil and Environmental Engineering Department, University of Colorado Boulder, USA,
Phone +1.303.502.0188, email karl.linden@colorado.edu
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INTRODUCTION
Sustaining water supply and sanitation services in low-income and developing contexts
is a pressing challenge. Against daunting failure rates following decades of efforts to
build infrastructure, sustaining long-term service functionality is increasingly
recognized as a complex problem that cannot be solved using traditional problem-
solving methods (Meadows 2008; Zhang et al. 2016). Instead, studies show that
sustaining water services requires a healthy system of interconnected financial,
political, institutional, economic and social sub-systems that can support the proper use,
maintenance, and monitoring of infrastructure (Carter and Ross 2016; Loorbach 2010;
Welle et al. 2015; Westley et al. 2011). These sub-systems are managed and relied on
by a diverse range of actors (people, government offices, and organizations), and often
no single actor has the knowledge, resources, or authority to fully comprehend and
manage the system. Progress towards sustainable services thus requires innovative
approaches that not only identify which factors to target, but also how to align actors
toward common, joint actions to sustain services. Involving a diverse web-like network
of actors with unique priorities, values, and perspectives introduces another level of
complexity to this already-complex problem (Berg 2016; Ogada et al. 2017; Ricart et
al. 2018). Thus, the upcoming movement of systems-approaches (Liddle and Fenner
2017) and network-approaches for sustaining water, sanitation, and hygiene (WASH)
services must also consider the tradeoffs of strengthening collaboration while
navigating diverse perspectives across sectors, organizations, and administrative levels.
Guided by principles of collective action, collaborative partnerships bring together
relevant actors and generate joint action (Kania et al. 2011; Ostrom 2000). The process
for establishing these partnerships starts with learning about the problem and their
relationships with the other actors involved (Gray 2000; Kania et al. 2011), discussing
potential solutions (Hardy et al. 1998), agreeing on a solution, and then collectively
taking action. Doing these activities with a diverse range of relevant actors allows the
complex problem to be negotiated based upon differing experiences and expertise.
However, convening diverse perspectives, assumptions, interpretations of the problem,
beliefs, and priorities poses a challenge of reaching consensus and alignment on a
single vision and action (Gray 1989, 2000). Organizations and government offices with
different roles may be constrained by external structures or preconceived notions that
limit their willingness to collaborate (Ostrom 2000). Understanding these differences
is a core goal of typical “collective actionwork, which aims to bring a group of
important actors together to establish a common agenda, problem definition, and
solution to a complex social problem (Kania et al. 2011). Through this lens, we narrow
the focus of this study to alignment, specifically, we analyze how actors (organizations
and government offices) align, or do not align, regarding the actions they prioritize to
strengthen their local WASH system.
Achieving the “right” amount of alignment of priorities is a primary and well-
documented challenge of collaborative work (Bruns 2013; Gray 2000; Hardy et al.
2005; Koschmann 2016). Collaborative partnerships consist of people and
organizations that are connected in various ways (communicating, sharing information,
sharing resources, joint planning, etc.) to achieve a common, “aligned” goal. However,
context-specific and dynamic relationships between actors are unique to each
partnership, and it is difficult to implement a standard process to get all actors “on the
same page” and align their priorities. While many scholars acknowledge the need for
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alignment (Murphy et al. 2015) and the presence and challenges of misalignment (Gray
1989; Ulibarri and Scott 2017), the need for concrete methodologies for explicitly
assessing the extent to which a group aligns has escaped scholar’s attention. One stream
of academic work has established techniques for “stakeholder analysis” which can
evaluate how collaborative partnerships are structured and function (Reed et al. 2009).
However, while techniques for stakeholder analysis are increasingly used, no feasible
methods exist for understanding where misalignment occurs and how alignment
emerges (Reed et al. 2009; Starkl et al. 2013).
As the international WASH sector moves away from infrastructure-only approaches
and towards systems- and network- strengthening, there is an increasing need for
context-specific tools to visualize, assess, and measure alignment within collaborative
partnerships. Without a way to measure and evaluate alignment, they may not be able
to build an evidence base of how their approaches lead to positive outcomes and report
on performance (Emerson and Nabatchi 2015). Without a method to adequately
visualize and quantitatively assess alignment, actors establishing collaborative
partnerships are often unable to understand the nuanced differences in partner priorities
and how these differences may be associated with an actor’s position in the network or
role. These limitations affect their ability to determine how to strengthen a network and
may reduce their ability to attract and hold funding from large donors.
Filling this gap in the WASH sector requires adopting perspectives and frameworks
from outside the discipline. To build an understanding of alignment, this work draws
from both network theory and communication theory to employ network analysis
techniques while also prioritizing context-specific negotiation of perspectives. Recent
work has found that the alignment of missions and strategies is more valuable than
alignment of values (Murphy et al. 2015), thus we focus our study on the alignment
specifically of actions that the actors prioritize. By “alignment” we mean the extent to
which actors’ prioritized actions converge toward a common, collective goal or action
to strengthen their local system, including understanding how smaller groups emerge
within the greater network of actors.
In this paper, we first establish a point of departure by reviewing relevant academic
and practical directions in the WASH sector, network literature, and communication
literature. Our intent is to provide the rationale for why alignment should be measured
in collective action approaches, why network and communication perspectives provide
an important lens, and how this methodology can be applied. As demonstrated by
empirical and theoretical works in communication, we propose that complete
alignment of priorities is not ideal; rather a collaborative network must balance a shared
group vision with individual interpretations and ideas. Our approach for a methodology
recognizes that the structure of the collaborative network influences how it functions,
and we use a combination of network analysis and semi-structured interviews to
visualize, assess, and measure alignment and sub-groupings of alignment within the
network. By overlaying the priorities of actors and combining it with network structure,
our preliminary results display the network of “factors and actors”, or (f)actors,
combined to determine the extent of misalignment and alignment amongst the network.
This new approach provides a process for WASH practitioners to understand how actor
priorities align in a collaborative partnership while also contributing to communication
and network theories and applications. We conclude this paper by presenting the
direction of this research, including known limitations and foreseen next steps.
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POINT OF DEPARTURE
Here we briefly review the topic of stakeholder collaboration in the WASH sector to
set up the need for understanding the processes and structures that occur in
collaborative networks. Establishing a premise of past work on processes in
communication literature and on structures in network literature, in the following
section we develop a point of departure and objectives for this study.
COMMUNICATION
The communication field has provided a well-established lens to understand and
observe alignment in collaborative partnerships. Seminal work by Hardy et al. (2005)
defines collaboration as “a cooperative, interorganizational relationship in which
participants rely on neither market nor hierarchical mechanisms of control”. The goal
of a collaboration is to “create a richer, more comprehensive appreciation of the
problem among the stakeholders than any one of them could construct alone” (Gray
1989). Interorganizational collaborations are rife with complexities and tensions,
particularly in international contexts, and thus must be carefully planned to be
successful (Gray 1989; Heenan and Perlmutter 1979; Innes and Booher 1999;
Koschmann 2016). The inclusion of diverse organizations and governments, each with
their own vision, values, priorities, and resources, leads to “staggering” challenges that
“require a radically different approach to organizing and managing” (Gray 1989). Some
challenges include selecting who is included and excluded (i.e., boundaries) as
members in the collaborative partnership, coordinating laterally without a hierarchical
authority, achieving consensus on a shared vision, ambiguous authority and leadership
towards that vision, dual roles of individuals representing organizations, and unclear
measures of effectiveness (Crona and Bodin 2006; Koschmann 2016; Lewis 2006).
In addition to other challenges, achieving consensus on a shared vision is requires
trade-offs. Within the field of communication, the concept of alignment is investigated
within areas of consensus-building through discussions and negotiation, establishing
shared visions and directions for the group, and group versus individual constructions
of the problem and solutions. These investigations show that complete alignment is not
necessarily desirable: identical “aligned” perspectives minimize creativity and
innovation needed to solve complex problems, as demonstrated by the idea of “group
think” (Hardy et al. 2005; Innes and Booher 1999; Lawrence et al. 1999). Instead,
establishing common priorities and agendas requires trade-offs, such as balancing
involvement of diverse perspectives with time and capacity requirements (Ulibarri and
Scott 2017).
However, there is a gap between the groundwork laid by these researchers and those
studying a specific area of collaborative work called “collective action”. Established
by Olson in The Logic of Collective Action in 1965, collective action work explores the
processes through which self-interested individuals in a group can work together to
achieve a common goal, as long as they all benefit from this goal. Empirical
investigations have taken this further to study the role of collective action in managing
common-pool resources and public service goals (Lynn et al. 2018; Ostrom 2000). As
it has developed, collective action work has claimed it is “distinctly different” from
typical collaborations, in that it “involve a centralized infrastructure, a dedicated staff,
and a structured process that leads to common agenda, shared measurement, continuous
communication, and mutually reinforcing activities among all participants” (Kania et
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al. 2011 p. 38). Despite this claim as an outlier, collective action work is based on many
of the same ideologies as collaborations and thus face the same complexities,
challenges, and tensions described in other work.
From this extensive work from communication researchers studying
interorganizational collaboration and collective action, we have a firm understanding
of the challenges associated with developing alignment and the tradeoffs that limit the
desirability of complete alignment. Yet, neither stream of research provides concrete
methodologies for measuring and assessing alignment and how it changes over the
course of collective action work. Needing an analysis that provides quantitative rigor
while acknowledging context-specific, qualitative meaning, we turn to network
analysis techniques.
NETWORK ANALYSIS
To better understand best practices for the formation, processes, and structures of
collaborative partnerships, practitioners and academics can use stakeholder analyses to
assess actors and reveal the nature of the web-like “network” of their interactions (Reed
et al. 2009). When forming a collaborative partnership, understanding the structure of
existing relationships is useful to design appropriate actions to strengthen them. One
such analysis, network analysis, is increasingly applied to these types of complex
systems.
An extensive review by Provan et al. (2007 p. 482) defined a network as “a group
of three or more organizations connected in ways that facilitate achievement of a
common goal”. Network research evaluates the presence or absence of relationships
between actors in a network and evaluates how the network structure facilitates
achievement of goals (Barley and Weickum 2017). The method employed for
collecting data for network analysis allows researchers to build a network based on
specific types of relationships for a pre-determined outcome. For example, we can see
organizations as tied to each other via information sharing related to water
sustainability, and then answer questions such as who needs to strengthen information
sharing with whom to ensure proper maintenance of water and sanitation
infrastructure? Once an outcome of interest is determined, the type of relationship,
analysis metrics, the level of analysis (individual actor or whole network), and
boundary of actors can be selected for that outcome.
Metrics such as centrality can help determine who spans boundaries and who is
particularly well-connected with the rest of the network (Doerfel and Taylor 2004;
Koschmann and Wanberg 2016). For our example in WASH, this assessment would be
valuable to visualize a bridge-like connection that a temporary organization might serve
between actors. From this, the collaborative partnership could identify which actors
would need to be connected if that bridging actor leaves, to ensure that information
sharing remains active. In addition, subgrouping algorithms can determine clusters and
silos (Provan et al. 2007), which for our example, could reveal fragmented and isolated
actors with unique local knowledge that leads to duplication of efforts.
As a single group of actors may have distinct types of relationships with many
actors, the type of relationship selected for the network analysis determines network
structure. For example, information-sharing relationships may look entirely different
than the resource-sharing relationships in the same network. Relationships (e.g., ties or
links) often used include quantitative and qualitative relationships. Quantitative
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relationships could include the amount of resources shared, frequency of
communication (Walker and Stohl 2012), information (Poleacovschi and Javernick-
Will 2016), knowledge (Wanberg et al. 2015), and referrals to clients (Cooper and
Shumate 2012; Koschmann and Wanberg 2016). Qualitative relationships could
include trust and perception of drawbacks to collaboration (Provan et al. 2003) and
provision of social support (Provan and Kenis 2007). These qualitative metrics can then
be converted to quantitative measures using Likert scales for analysis. Once quantified,
metrics can be based at a zoomed-in level looking at actors themselves or a zoomed-
out level looking at the entire network of actors. Yet, as many researchers investigate
network structures, there is also a need to investigate how the networks function as a
result of that structure (Provan et al. 2007; Provan and Kenis 2007).
Network analysis has shifted from a technique used by empirical researchers
(Provan et al. 2007) to a tool for practitioners to inform action in collaborations (Bodin
et al. 2017; Starkl et al. 2013). These analyses allow decision makers to see what gaps
exist in the current network structure and to inform decisions about how it might be
strengthened. In other words, network analysis enables an abstraction of reality based
on data collection methods, analytic assumptions, and user interpretation that can be
used to sway decision makers.
While current network analysis techniques can be used to evaluate these context-
specific strengths and gaps in network structure, they do not allow visualization of
perspectives and priorities of the actors existing in a network. Current technique also
does not allow for in-depth analysis into the processes through which true collaboration
occurs, such as how diverse priorities are navigated and negotiated toward a set of
aligned priorities. Thus, existing tools and approaches in network analysis are
incapable alone in explicitly informing decision makers on how to enhance alignment
of collaborative partnerships for WASH governance.
COMMUNICATION AND NETWORKS IN THE WASH SECTOR
Collaborative partnerships are encouraged by donor agencies as they are assumed to be
a viable solution for addressing complex problems that are otherwise unsolvable by a
single entity (Huxham et al. 2000; Huxham and Vangen 2000; Koschmann et al. 2011).
As a result of an increasing awareness of WASH system complexity, the WASH sector
has seen an emergence of partnerships, coalitions, networks, collective action groups,
and collective impact groups that seek to jointly solve these complex problems (Bisung
et al. 2014; Boschet and Rambonilaza 2018; Dickin et al. 2017; Harrington 2017;
Lienert et al. 2013).
As the WASH development sector shifts away from pure “community
management”, the paradigm is moving toward providing more long-term external
support by external partners to increase sustainability (Hutchings et al. 2015;
Schweitzer and Mihelcic 2012). Often, these approaches focus on strengthening the
wider system needed to sustain services over a long period of time (Moriarty et al. 2013;
Schweitzer and Mihelcic 2012; Verhagen et al. 2008). This type of approach integrates
diverse, and inherently complex, ranges of organizational values, beliefs, and priorities
and therefore requires partnerships of a collaborative nature.
The first area of relevant work in the WASH sector is multi-criteria decision aids.
In water projects, decisions are often based on multiple criteria, which can be weighted
differently according to who weights the criteria. In the case of disagreement, Stark et
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al. (2013) explored consensus-finding for weights in multi-criteria decision aids,
though they did not investigate collaborative partnerships specifically. For cases where
stakeholders held conflicting preferences, they used social network analysis to
determine clusters of actors that agreed. This study advised that if actors were
misaligned, then top-down decision making should ensue. In collaborative partnerships,
there is often no hierarchical form of control (Hardy et al. 2005) and thus while the
study took an important step at using network analysis to understand alignment, the
top-down advice provided by Stark et al. (2013) does not apply to collaborative
arrangements. Furthermore, the study used information about extent of alignment to
determine the type of decision make, rather than use the information to inform the
establishment of a common vision.
A noteworthy first effort at assessing alignment in the WASH field was the work
done by Walters and Javernick-Will (2015) on rural water services in Nicaragua. Using
stakeholder theory and project management literature, they assessed how four
stakeholders valued an array of factors to provide insight into their associated actions
and therefore potential alignment. However, their study did not evaluate how these
actors interacted with one another and worked together as part of a larger structure
network of actors. Other studies have looked at the network structure, but do not
consider the priorities and perspectives of the actors in the network. Notably are work
by Walters and McNicholl. Walters (2016) applied social network analysis to inform
exit strategies for NGOs in Nicaragua based on communication relationships between
the NGOs and governments. Related work by McNicholl investigated ties of skills and
information between governments and relevant stakeholders using social network
analysis to characterize and identify potential gaps in relationships (McNicholl 2017;
McNicholl et al. 2017). While these studies have laid important groundwork in the
WASH field using network analysis, they do not consider the perspectives of actors
within that structure. Values and interests of actors in collaborative water governance
are two of the most important elements tied to infrastructure performance as they
provide “the basis for prioritizing water sector outcomes and determining the incentives
that promote the achievement of shared objectives” (Berg 2016 p. 13). Here, Berg
identifies the importance of balancing diverse interests and priorities between actor
groups and calls for application of stakeholder analysis and public participation to
better understand these differences, though to date, no studies have undertaken this
endeavor. We answer these two gaps by rooting our network analysis in collaborative
work and looking at the perspectives and priorities of the actors within that structure.
One notable study has employed both stakeholder analysis and network analysis
separately to assess a collaborative governance. Ogada et e. (2017) used stakeholder
analysis to qualitatively compare perceptions, finding that actors with higher interest
in the sector had higher perceived influence. In addition, they used social network
analysis to quantitatively compare centrality of actors and assess who was most
influential. Recognizing that a network of “like-minded stakeholders can also limit
diversity of knowledge”, they also used an external-internal index to quantify
homophily, the extent to which actors with similar characteristics related to each other
(such as those with the same function, sector, and type of resource used). Findings of
low homophily reinforced the idea that for water governance, actors were required to
reach outside of normal groups to solve complex problems. While these findings are a
notable next step in rigorously documenting misalignment within networks through
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qualitative stakeholder analysis and the quantitative network analysis, Ogada et al. did
not integrate both analyses in combination to further investigate how different interests
and perceived influence was distributed across the network structure. In this case, this
limited the study’s ability to explore how fragmentation affected the ability of the
partnership to sufficiently align these interests toward a common goal.
In sum, a growing movement is emerging in the WASH sector around not just what
systems are needed to support infrastructure, but which actors are necessary to support
each local system. Rigorous stakeholder analysis is required to understand these actors
and their relationships to each other, but application of these tools is limited in their
application and relevance to context-specific situations. Yet, even though this
management approach is considered “emerging” in the WASH and water governance
sectors, it is not a new approach to management and is relatively well-studied in other
contexts (Browning et al. 1995; Lubell et al. 2002). As the WASH sector begins
implementing more collective action approaches, organizations cannot ignore the
challenges and tradeoffs present, less they risk unplanned and unsuccessful
consequences inherent to collaborative work.
STUDY OBJECTIVES
WASH practitioners seek to employ collective action without proper tools for
analyzing alignment of priorities toward a collective goal. Previous studies have
documented the value of diversity of perspectives without providing guidance for how
to overcome challenges introduced by the added diversity of values and priorities. In
light of the gaps related to collective action in WASH, our research seeks to build upon
and improve knowledge and practice in the WASH sector through the application of
communication and network theory. Specifically, we are interested in applying these
theoretical paradigms to better understand ways that the priorities of network actors
align, both between individuals and in relation to the whole network. In doing so, we
respond to calls for more combination of stakeholder analysis techniques indicated by
Reed et al. (2009) and the need for quantitative and qualitative methods for
understanding network function and evolution indicated by Provan et al. (2007) and
(Hardy et al. 2003).
Specifically, through the exploratory research in this paper we start to assess
alignment of priorities of actors in a collaborative partnership using network analysis
technique and qualitative analysis of semi-structured interviews.
RESEARCH CONTEXT
The Sustainable WASH Systems Learning Partnership (SWS) aims to improve the
sustainability of future WASH programs and catalyze national and international uptake
of successful approaches to strengthen wider WASH systems. Funded by the United
States Agency for International Development (USAID), SWS seeks to better
understand collective action initiatives in Ethiopia, Uganda, Kenya, and Cambodia.
The SWS partnership provides a unique opportunity to understand how a collective
action group is initiated, developed, evolved, and either succeeds or fails. Our study
uses data from one collective action initiative in Ethiopia.
Ethiopia is listed as one of the high priority areas in the US Global Water Strategy
of 2017 due to low coverage of water services, rapidly growing modern economy, and
worsening drought conditions. The Strategy emphasizes the challenging political
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environment, with “limited private sector engagement, policy constraints, and the lack
of data for decision-making limit the sustainability of current service provision” (“U.S.
Government Global Water Strategy” 2017). To overcome these challenges at a district
level, SWS establishes collective action platforms called Learning Alliances to engage
all key actors and then support action toward a common goal (Verhagen et al. 2008).
SWS partners with districts where key stakeholders show interest in supporting and
strengthening the broader system necessary to sustain water hardware. The selected
district in Ethiopia has some actors that share these interests, but there is no clear
priority and direction for how to support and strengthen their system. The Learning
Alliance includes about 20 actors (organizations and government offices) that were
selected through consultation with the district’s government water office. The selection
process resulted in roughly 40% district “woreda government offices, 40% zonal
government departments, and 20% NGOs. In Ethiopia, the hierarchical structure of
government flows from the national level to regional, then zonal, then district “woreda”,
then community kebele” level. In November 2017, SWS facilitated the first convening
of the Learning Alliance, where they presented results from a variety of planning tools
that assess the wider system, including network analysis, to help decision makers better
understand and plan for the complexities of the factors. Informed by planning tool
results, members met in March 2018 to start to collectively plan actions for sustaining
WASH services. This research builds on existing system-analysis tool techniques and
generates additional insights that can be used to (1) better understand the ways that
diverse actors come together to jointly address shared priorities and (2) track and
evaluate the process of alignment of these priorities over time.
METHODS
This research proposed to evaluate actor priorities in combination with the network
structure. Below we detail the methods for data collection and analysis used to
accomplish this.
DATA COLLECTION
The data for this work was collected in twenty-two interviews conducted with each
organization in the Learning Alliance during the summer of 2017. Interviews were
approximately an hour long, with the longest taking two and a half hours. Interviews
collected data on each actor’s perceptions of challenges, solutions, and prioritized
actions.
Priority Action
Semi-structured interviews lasting 15-25 minutes asked representatives a series of
questions about challenges and solutions to sustaining water services as well as which
solution is the most important. Interviews were conducted in the national language,
Amharic, and conducted by two experienced enumerators hired and trained by the
authors in June 2017. During training, we proposed interview questions and discussed
what we intended each question to mean once translated, then received feedback as to
the question appropriateness, and finally had each enumerator translate the questions
to Amharic then re-translate them back to English to ensure questions conveyed the
intended meaning.
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Network Structure
Following interviews, the remaining time was spent collecting network data about each
actor’s relationships with other actors in the network. Organization representatives
were first asked which other organizations in the network they interact with. For each
named organization, they were then asked to identify how often their organization
received or sent information, solved problems with, and/or actively implemented with
a given organization. Relationships were specified to be outside of formal structures.
For example, if an organization is required to send formal reports to a district finance
office, these reports were not counted as sharing information. This was done to target
informal relationships outside of normal hierarchies.
DATA ANALYSIS
The data analysis for this work occurred in two phases, described in the following
sections. The first phase analyzed the results of the twenty-two interviews, including
analyzing priorities using qualitative analysis and network structure using network
analysis. The second phase combined the two data types to build a network that
overlays actor priorities with their relationships to visualize, assess, and measure how
priorities differ amongst the network.
Qualitative Analysis of Priorities
For the first round of analysis, we summarized all themes that interviewees discussed
by assigning each incident to a theme or “code”. To draw theoretical insights from the
respondents’ perspectives themselves and to minimize bias from our outsider status,
we employed grounded theory techniques (Strauss and Corbin 1990). To do this, we
qualitatively coded the translated, transcribed interviews using inductive, emergent
coding to start to show themes based on their forcefulness, recurrence, and repetition
(Owen 1984). To organize the interviews and emergent codes, we used QSR NVivo
software (QSR n.d.). As groupings emerged organically from the interviews, it was
important to ensure the author’s coding was unbiased. Validity of the coding was
checked using an intercoder reliability NVivo feature that compared the author’s
coding with a second researcher who was given the same data (0.57, where kappa
values > 0.4 are considered acceptable agreement (Opdyke et al. 2015; Saldaña 2009).
Then, using the emergent list of codes, we conducted a second round of coding to
analyze answers to which solution do you think is the most important. Through this,
we identified a single priority for each actor.
Building Collaborative Network Structure
The first round of analysis consisted of typical network analysis techniques and
software packages UCINET and NodeXL, which tied actors together through
relationship data for information sharing, coordination, and problem solving. This
standard analysis was analyzed as a part of the USAID Sustainable WASH Systems
Learning Partnership, and it revealed the structure of each relational network. This
analysis, though not completed by the authors, provided a skeleton for the authors to
add qualitative data from interview coding. For each actor, we imported the prioritized
factor that emerged from the open coding process into NodeXL. We then assigned the
priority as an attribute for the organization, alongside other characteristics such as
organization function, type, and sector of focus.
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PRELIMINARY FINDINGS
Issues of alignment emerged from interviews, which are presented below to set the
context and need of alignment in the selected district Learning Alliance. Following this,
we present preliminary findings of the combined actor and prioritized factor analysis.
ISSUES OF ALIGNMENT FROM INTERVIEWS
Often mentioned was the issue related to organizations having separate values and
organizational structures, as well as lack of a shared mission or goal. Euphemisms such
as organizations “running on one leg”, “running separately”, and “running
independently” to reach their own visions were mentioned by half the interviewees.
This is associated with having separate values or goals and being constrained by
differing missions and organization mechanisms such as funding. Some actors saw this
as just a part of working together, while others saw it as a serious barrier that would
inhibit any successful collaboration. No actors discussed how the differences in goals,
born out of different experiences and perspectives, might lead to better outcomes for
the complex problems they seek to solve.
An NGO expressed how all external organizations that work in the area have
different donors, and thus “also have different kinds of priority areas and reporting
systems.” In contrast, they mentioned that organizational structures and goals can
hinder efforts such as establishing a Learning Alliance, in that “even if a coordination
office is available, as the missions of these institutions vary, the coordination office
will be a symbol.” However, the NGO still saw that despite issues that may undermine
coordination, a platform for learning would still enhance ongoing work, as “still it will
add values to share knowledge and experiences.”
This was the only actor that discussed how their own organizational structure would
limit their ability to collaborate on some tasks. In the WASH sector, key actors are
often non-governmental organizations, domestic or international, that are often
constrained by their parent organization’s structure and culture. If some actors don’t
recognize constraints such as incentives and reporting structures, group discussions
around alignment can be exacerbated.
COMBINED FACTOR-ACTOR ANALYSIS, OR (F)ACTOR ANALYSIS
Over thirty factors emerged from the coded interviews as influential for providing
sustained WASH services in the district. Following the second round of coding where
we assigned a single priority to each actor, only five priority factors were identified as
the most important by the group of 22 actors: Budget, Community Awareness,
Coordination, Water Resources, and Site Selection. Table 1 provides the definitions for
these factors along with the number of actors that prioritized them.
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Table 1. Prioritized factors
Factor
Definition
Budget
Finances available through budget allocated by the government,
contributed by the community, or funded through external sources.
Community
Participation and
Awareness
Participation by the community in aspects of service construction and
provision, including community awareness on proper use of the
scheme.
Coordination
Coordination of woreda offices, zone offices, kebele administrators,
and the community, with a common plan. Coordination on planning
activities, monitoring water schemes, training, sharing resources
(technical, material, and financial resources), and sharing information.
Water Resources
Quantity of water available, including groundwater, reservoirs, rivers,
and rainwater. Includes conducting hydrogeological assessments for
groundwater.
Site Selection
The selection of where schemes will be constructed, including
geographical differences in water resources. Includes the unfair
selection of scheme locations.
To undertake preliminary analysis to demonstrate this methodology, we selected a
single relationship from LINC’s analysis. We chose the relationship of “coordination”
to analyze how actors that coordinated planning and activities may prioritize similar
factors. Specifically, in a survey given to each actor in the network, respondents were
asked: With whom did your organization directly coordinate planning or activities in
the past six months? This includes planning your own activities with significant input
and communication with one another, as well as planning joint activities.
In network analysis, each actor is identified by a single dot (from here on called a
node), where node size denotes the actor centrality. Nodes are connected by a line (from
here on called an edge) if they coordinated plans or activities together. The strength of
the edge can be determined based qualitative scoring by the respondent, quantitative
values such as amount of resources or information flowing, or on reciprocity such as if
both actors reported a relationship. Here, we used reciprocity as strength, denoted by a
darker line. Relative placement of the nodes uses the Harel-Koren fast multiscale
algorithm to minimize edge intersection and improve network clarity (Harel and Koren
2002; Kim and Hastak 2018). The factor that each actor prioritized is noted by the color
of the actor node, creating a visual representation of what we term a (f)actor network
(Figure 1).
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Figure 1. Network demonstrating coordination relationships between actors in a
single district in Ethiopia, with node color designating prioritized factor, termed a
(f)actor network.
By visualizing prioritized factors alongside network analysis results through the
(f)actor network, we note trends regarding coordination and community awareness.
First, the four actors that prioritize coordination (red in Figure 1) are those that
coordinate the least with others, and none of them coordinate with each other. This is
supported by the interviews. One of the four organizations stressed that coordination is
the reason for project failure:
“the major solution is coordination among stakeholders… At the first
place there is weak coordination among WASH stakeholders that is why
ongoing water projects are not completed yet… WASH plans and activities
should be discussed together; one office cannot be effective. There should
be a common plan, including capacity building…this might include sending
their quarter year report to water office and participating in annual and
other meetings.” - Woreda Women’s Office
In contrast, the interviews revealed that while all actors discussed coordination as a
factor that has prevented or could enable sustained services, some of the more well-
connected actors did not see it as a problem.
KEY
Budget
Community Awareness
Water Resources
Site Selection
Coordination
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“At a woreda level we have no coordination problem.
Particularly the WASH program supports water, health and
education sectors. These sectors use their budget according to the
plan without violating the finance regulations. If their pans are not
feasible, our office evaluates and reject their budget proposal. Thus,
our coordination with these sector offices is good.”
- Woreda Finance Office
If the actors expected to take the lead on collaborative efforts fail to see it as a
pressing issue, they may feel less motivation to initiate actions such as establishing
Learning Alliances and incorporating other actors at the outskirts of the network. This
is similar to findings in a study of fishermen networks by Crona and Bodin (2006),
where “without the appropriate incentives and knowledge, favorably positioned actors
will not exploit their positions to initiate collective action” (p. 18).
Besides this easily-visualized trend, it is also apparent that there are groups of nodes
that coordinate with each other (e.g. have a coordination tie) and share priorities (e.g.
are the same color). For example, the group of zone offices and NGOs that prioritize
community awareness appears to be well-connected and close to one another.
Furthermore, no woreda offices belong to that group. In this context, responsibility for
working on community awareness lies at the woreda government level:
“There is a community mobilization unit at woreda level. This unit is
responsible to support WASH committees and water user associations.
However, in practice they are not supporting the community related to
operation and management of water schemes. Thus, I recommend that this
community mobilization unit should have a plan to support the community on
operation and management of water schemes. This unit should also
collaborate with non-governmental organizations in building the capacity of
the community to ensure sustainable water service in the area.” – NGO 1
NGO 1 points out the actors with the responsibility to work on community
awareness, the woreda government, does not coordinate activities with NGOs. Looking
at the (f)actor network map, the woreda government does have ties to the three NGOs
that prioritize community awareness, but there appears to be a disconnect of who works
with whom to improve community awareness. Looking at the priorities of the woreda
government actors, budget, coordination, and site selection are more important than
community awareness and thus may limit their interactions with the NGOs that do
prioritize community awareness. By visually considering interview responses with
network data, we can better understand where misalignment occurs, which can be the
first step toward negotiating a shared plan forward.
While visual cues are useful for facilitating discussion and noticing initial trends,
to develop a nuanced understanding and characterization of alignment for this
complicated network, we employ social network metrics from the analysis software.
We are still exploring the metrics that best characterize nuanced differences and their
implications on alignment. We seek to expand upon known relationships of “who
works with whom” to better understand “who shares priorities with whom” (identified
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through ties and priorities). For example, we seek to not only demonstrate who works
together on activities – but who works together as a means to the same end.
Considering that some actors may be better positioned to work towards some
priorities, we investigate alignment as the right people converging on the same
priorities, we currently envision using two complimentary types of network metrics to
evaluate alignment: (1) density within the groups partitioned by priority and (2)
external-internal indices of varying levels. Density within each group can help us better
understand how often those with the same priorities work together on the same issue,
revealing levels of alignment towards a common goal. The optimal amount of density
here is context-specific: as density increases, at what point does it reach an ability to
achieve collective action and at what point is a high density of ties indicative of “group
think”, or homophily?
The External-Internal (E-I) index represents an idea of “birds of a feather flock
together”, testing the frequency of which those with a certain priority have ties to those
that also share that priority. The E-I index metric compares the number of External
connections (different priorities) to the number of Internal connections (same priority)
relative to the total number of possible connections. These can be calculated for the
entire network, each group that shares priorities, and for individuals. Table 2 presents
our preliminary analysis of the network, with potential insights, using the two
aforementioned metric types.
Table 2. Proposed metrics for measuring alignment of priorities within a network,
between groups that share priorities, and between individuals. Outlines what the
metrics tell us, outputs, and potential insights.
Proposed
metric
Tells us
Output
Potential Insights
Density
(group that
shares
priorities)
The percent of
ties present
within one group
Budget: 0.17
C. Awareness: 0.5
Coordination: 0.0
Water Res: 0.17
Site Selection: N/A
- Half of the actors that prioritize
community awareness are jointly
coordinating activities.
- None of the actors that prioritize
coordination are coordinating activities
E-I Index
(whole
network)
Ratio of
ties outside a
group to
ties inside the
group
Ranges from:
-1 (all internal) to
1 (all external)
Raw: 0.354
Max possible: 1.0
Min Possible: -0.57
(Max and Min given
density & group size)
Scaled to between
Max/min: 0.176
There is a tendency toward external
connections; actors in the network
coordinate with those that do not share
their priorities.
E-I Index
(group that
shares
priorities)
Budget: 0.44
C. Awareness: 0.02
Coordination: 1
Water Res: 0.67
Site Selection: 1
- Actors that prioritize coordination and
site selection only coordinate with those
that prioritize different actions.
- E-I indices would be negative if actors in
groups work more with actors that share
priorities. However, no groups had
negative indices.
E-I Index
(individual)
[Values for each,
some examples:]
Woreda Ag Office: 0
W. Water Office: 0.5
W. Women Office: 1
Z. Water Dept: -0.09
Z. Women Dept: -0.2
- Two actors (Z. Water Dept and Women
Dept) have negative E-I indices, meaning
that they work more with those that share
their priorities than those that do not.
- Six of the 22 actors had scores of 1,
where they only work with actors that do
not share their priorities.
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Comparing group-level metrics provides insights into how each group works with
each other. However, group-level metrics do not assess who exists in each group. Thus,
it could be useful to compare the E-I index with other attributes, such as organization
type or sector of focus, with other metrics, such as average centrality of group members.
These comparisons could start to answer questions such as to what extent do NGOs
share priorities with zone offices and woreda offices? and, if central actors are those
that typically drive group action, which priorities do the central actors prioritize?, or
if there is concern of isolation, do well-connected actors tend to be more endogenous
(relate to more internal actors, EI index less than 0) or exogenous (relate to more
external actors, EI index greater than 0)? Findings from the E-I index indicate that
only a few actors exhibit slight homophily, and overall the groups and whole network
are more exogenous than endogenous. This reinforces the findings of Ogada et al. (2017)
and contradicts expectations of collective action principles where complete alignment
is desired.
CONCLUSIONS AND FUTURE RESEARCH
As the Learning Alliance develops, members will collectively decide which actions the
alliance should undertake; however, generating consensus requires overcoming these
differences in priorities. The extent of alignment required to efficiently achieve
“collective action” is context specific and not well documented. Through the analysis
of the actor network and each actor’s prioritized factors, a (f)actor network, we inferred
how the relationships between actors and their relative priorities influence the ability
for the right actors to work together collectively. Our study presented here presents a
preliminary method to uncover key areas of alignment or misalignment that the
Learning Alliance should leverage or address, considering interaction between
organization types, subgroups of actors that worked together, as well as their position
in the collaborative network. More broadly, this work demonstrates how analyzing
network structure alongside interview data reveals important areas of alignment. These
can facilitate discussions that lead to more effective action in collaborative partnerships.
As a part of an ongoing project, we aim to expand and adapt this research in four
ways. First, we will expand this methodology to more cases to allow for cross-case
comparison. Cross-case comparison will point to useful metrics to compare alignment
between different stakeholder groups.
Second, we will re-assess alignment in the same cases over time to allow for
longitudinal analysis. This intends to show how particular collective action
interventions such as establishing knowledge-sharing platforms and backbone
organizations change aspects of alignment, if the changes are what organizations
expected, and how long these changes can take.
Third, we will evaluate which network analysis metrics can be used to demonstrate
changes in alignment over time to measure and report alignment evolution in collective
action work. Metrics will be used to quantify the key differences observed in cross-
case comparison and the longitudinal assessment.
Fourth, we will validate the results with the local actors themselves. This will
involve presenting the results and then conducting interviews and focus group
discussions to better understand the extent to which the visualization, assessment, and
measurement aspects of the factor-actor network agree with what is experienced by
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those within the network. Future applications would aim to engage local actors in the
analysis and interpretation of findings.
With these next steps, we intend to build an evidence base about how collective
action approaches create alignment. Conjointly, we plan to provide a validated
methodology for how stakeholder analysis can be used to improve understanding of the
extent to which actors in networks share priorities toward a common goal, and how this
might influence WASH service delivery outcomes. This work builds evidence toward
a broader research question of how alignment of actors in a collaborative WASH
network be measured and assessed using network analysis.
LIMITATIONS
There are well-established methodologies for determining priorities, such as the
Analytical Hierarchy Process (AHP) and others. We recognize that in establishing a
more rigorous understanding of priorities requires a more systematic approach.
Furthermore, our analysis assumes that the perspectives of the actors (organizations
and government offices) can be represented by the perspective of the individual person
that was interviewed. We aim to continue investigating which methodology is best for
determining priority actions of the actors and invite the reader to provide additional
insights by contacting the authors.
This work focused on priorities, but we recognize that misalignment could manifest
in other ways. We plan to restructure interview questions and coding to consider where
other areas with differing perspectives could prove problematic; such as definition of
collaboration, definition of sustainability, perceived effectiveness of collaborative
efforts, and information used to make decisions.
ACKNOWLEDGEMENTS
This work is funded by USAID Cooperative Agreement # AID-OAA-A-16-00075, and
is supported and enabled by strong collaboration with partner organizations.. As a
whole, the USAID Sustainable WASH Systems Learning Partnership is itself a diverse
collaborative partnership of varying stakeholders and actors, whose collaborative
efforts make this research, and the partnership, possible.
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