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World Development 126 (2020) 104703
Contents lists available at ScienceDirect
World Development
journal homepage: www.elsevier .com/locate /wor lddev
Embracing complexity: A transdisciplinary conceptual framework
forunderstanding behavior change in the context of
development-focusedinterventions
https://doi.org/10.1016/j.worlddev.2019.1047030305-750X/� 2019
The Authors. Published by Elsevier Ltd.This is an open access
article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
⇑ Corresponding author.E-mail addresses: [email protected] (F.
Lambe), [email protected] (Y. Ran),
[email protected] (M. Jürisoo), [email protected] (S.
Holmlid), [email protected] (C. Muhoza),
[email protected] (O. Johnson), [email protected] (M.
Osborne).
Fiona Lambe a,⇑, Ylva Ran a, Marie Jürisoo a, Stefan Holmlid b,
Cassilde Muhoza c, Oliver Johnson a,Matthew Osborne a
a Stockholm Environment Institute, SwedenbDepartment of
Information and Computer Sciences, Linköping University, Swedenc
Stockholm Environment Institute, Kenya
a r t i c l e i n f o
Article history:Accepted 28 September 2019Available online 15
October 2019
Keywords:Behavior changeDevelopment intervention designService
designComplex adaptive systems
a b s t r a c t
Many interventions that aim to improve the livelihoods of
vulnerable people in low-income settings failbecause the behavior
of the people intended to benefit is not well understood and /or
not reflected in thedesign of interventions. Methods for
understanding and situating human behavior in the context
ofdevelopment interventions tend to emphasize experimental
approaches to objectively isolate key driversof behavior. However,
such methods often do not account for the importance of contextual
factors andthe wider system. In this paper we propose a conceptual
framework to support intervention design thatlinks behavioral
insights with service design, a branch of the creative field of
design. To develop theframework, we use three case studies
conducted in Kenya and Zambia focusing on the uptake of
newtechnologies and services by individuals and households. We
demonstrate how the framework can beuseful for mapping individuals’
experiences of a new technology or service and, based on this,
identifykey parameters to support lasting behavior change. The
framework reflects how behavior change takesplace in the context of
complex social-ecological systems – that change over time, and in
which a diverserange of actors operate at different levels – with
the aim of supporting the design and delivery of morerobust
development-oriented interventions.� 2019 The Authors. Published by
Elsevier Ltd. This is anopenaccess article under the CCBY-NC-ND
license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
1. Introduction and aim tions must deal with inherently ‘‘wicked
problems” that are by nat-
The success or failure of interventions that aim to
changebehavior hinge on people thinking, deciding and acting in a
certainway. Thus, for interventions to work, it is critically
important thatthey are designed in accordance with how people
actually think,decide and act (Datta & Mullainathan, 2014).
This is no less truefor the design of programmes or policies aiming
to change behav-ior in low income countries. Behavioral science has
providedapproaches and methods for understanding human
behavior,many of which have proven useful for the design and
delivery ofinterventions aimed at low-income populations.
Given that much development research involves the study
ofcomplex, adaptive systems, we assume that development
interven-
ure ‘‘difficult or impossible to solve because of
incomplete,contradictory, and changing requirements that are often
difficultto recognize” (Rittel & Webber, 1973). Wicked problems
cannotbe solved in a traditional linear fashion, because the
problem def-inition evolves as new possible solutions are
considered and/orimplemented (Rittel & Webber, 1973).
There is increasing acceptance that interventions that
acknowl-edge individuals’ decision-making processes and the
implicit trade-offs required of individuals are likely to be more
successful(Banerjee, Duflo, Glennerster, & Kothari, 2010). To
date, researchto understand individual behavior in the context of
developmentinterventions has tended to focus on the use of
experimental meth-ods to identify where behavioral insights can be
usefully applied toimprove the effect of an intervention.
Behavioral insights have beenparticularly successful for
understanding one-off decision-makingat one point in time, e.g.
farmers purchasing fertilizer (Duflo,Kremer, & Robinson, 2011)
or families deciding to bring their chil-dren to the clinic for
vaccination (Banerjee et al., 2010). There is agrowing body of work
that focuses on understanding ongoing,
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2 F. Lambe et al. /World Development 126 (2020) 104703
repeated behaviors and habit formation, for example, studies
havelooked at the effect of incentives on long term habit
formationaround hand washing in West Bengal, India (Hussam,
Rabbani,Reggiani, & Rigol, 2016), interventions to reduce
household waterconsumption in the long term in Costa Rica (Datta et
al., 2015)and incentives to reduce daytime drinking amongst
informal work-ers in India (Schilbach, 2019). Some studies have
focused on shifts ina set of behaviors within a specific
environment, for example,energy saving behaviours in the workplace
(Klege, Visser, Datta, &Darling, 2018). There are fewer
examples of behavioral insightsapplied to understand behavior in
complex change processes,where a set of different behaviors need to
change within individu-als, orwhere different actors need to
shiftmultiple behaviors simul-taneously (e.g. farmers adopting a
package of agricultural inputs, orhouseholds adopting clean
cookstoves). To effectively apply behav-ioral insights, it is
necessary to know precisely where andwhen in aprocess of changing
behavior that specific behavioral determinantscome into play, as
well as the relative importance of various behav-ioral determinants
in the decision-making landscape and the roleand influence of other
actors on the behavior change process
Research on resilience and social-ecological systems
hasattempted to overcome the challenge of explaining behavior
incomplex change processes, in particular the human dimensionsof
social-ecological dilemmas (Fabinyi, Evans, & Foale, 2014).
How-ever, the research tends to focus on social units, rather than
thecomplex interplay between individuals and the social
units.Fabinyi et al. (2014) highlight the failure of
social-ecological sys-tems research to acknowledge the complexity
and social diversityof studied systems.
In this paper we propose a conceptual framework that aims
tointegrate insights from behavioural science and complex
adaptivesystem dynamics using service design – a qualitative
approach tounderstanding people in their wider context, and their
needs,motivations and behaviours – with the intention of
co-creatingimproved interventions that better meet their needs
(Edvardsson,Kristensson, Magnusson, & Sundström, 2012;
Patrício, Gustafsson,& Fisk, 2018; Pfannstiel & Rasche,
2017).
Following Imenda (2014) we define ‘‘conceptual framework” asa
synthesis of concepts and perspectives drawn from manysources,
which provides an integrated way of looking at a problem.The
purpose of our conceptual framework is to
supportdevelopment-oriented academics, practitioners, and other
profes-sionals to understand the behavior change(s) that are
required byindividuals, over time, to achieve sustained uptake of a
new tech-nology or a change in practice. The framework has been
developedand refined through a series of case studies and through
consulta-tions with development professionals from a variety of
fields dur-ing a four-year research program1. When combined, it is
hoped thatthe framework and the supporting empirical material
presented herewill demonstrate how integrating insights from
service design andbehavioral science, against a backdrop of
social-ecological systemstheory, can support more robust
intervention design.
Section 2 sets out the theoretical background. Section 3
pro-vides a generic description of the methodological framework.
InSection 4 three case studies are used to describe the
frameworkand illustrate its iterative development. Finally, we
discuss theoverall contribution of the framework and present
suggestionsfor its future development and application.
2. Theoretical background
The development interventions in focus operate at the
intersec-tion of environment and development, within complex
adaptive
1
https://www.sei.org/projects-and-tools/projects/sei-initiative-behaviour-choice/.
systems, and deal with interlinkages between a multitude of
actorsand scales. In our conceptual framework, we use
social-ecologicalsystems theory as the theoretical backdrop needed
for capturingthe multi-level system dynamics influencing individual
behaviorand decision making. The logic of the framework is informed
byservice design, a user centered approach to understanding
complexsystems and by behavioral insights, namely a model of
behavioraldesign developed by Datta and Mullainathan (2014) and the
Beha-viour Change Wheel (Michie, van Stralen, & West,
2011).
2.1. Social-ecological systems theory
To study interventions that aim to address wicked problems inlow
income settings, it is important to consider the complexity ofthe
systems under study. Our conceptual framework relies
onsocial-ecological systems thinking, which assumes that social
andecological dynamics interact as a complex adaptive system
(Folkeet al., 2010; Levin et al., 2013) in which the macroscopic
propertiesof a system emerge from an interaction among its
components, andthe interactions themselves can feed back and impact
on subse-quent development. Thus, social-ecological systems theory
viewshumans, or actors, as part of the complex adaptive
system(Berkes, 2008).
In these types of systems: actors interact, often in
unstructuredand unpredictable ways, which leads to the emergence of
cross-scale patterns and feedback loops, influencing
interactionsbetween actors (Levin et al., 2013). Thus, the
components of a sys-tem change as a result of the interplay between
the inherentlyadaptive actors and the developing properties of the
whole(Lansing, 2003; Levin, 1999). To add complexity, the
macroscopicproperties of a system develop from actions at a local
scale, in turnfeeding back to influence the behavior, options and
choices ofactors, diffusely and, over the long-term (Levin et al.,
2013).
Identifying opportunities for creating new feedbacks,
orstrengthening desirable feedbacks, requires an understanding
ofthe drivers of behavior and decision-making at the local
level,and how these relate to the wider social-ecological systems
withinwhich households operate. To address these complexities,
social-ecological systems analysis focuses on the social group in
orderto influence behavior and feedbacks (Fabinyi et al., 2014).
Althoughstudies of social-ecological systems and complex adaptive
systemsrecognize the importance of studying actors within a system,
aswell as the system itself, they have been critiqued for
homogeniz-ing social complexity by assuming that people’s
interests, expecta-tions and experiences are the same (Fabinyi et
al., 2014), and fordownplaying experience-based behavior and the
importance ofcultural context and meaning (Cote & Nightingale,
2012).
To illustrate a behavior change process, we draw on conceptsfrom
resilience and ecology research (Holling, Schindler, Walker,&
Roughgarden, 1995) of an ecosystem shift between ‘stablestates’,
driven by a shift in state variables that alters the landscapeand
causes the system to move into a new state (Beisner, Haydon,&
Cuddington, 2003). Applying this concept to explain a
behaviorchange process, the ball (Fig. 1) represents an actor, or
an actortype, that, due to changes in state variables, (e.g.
behavioral driversand behavior change techniques) transitions from
one behavior, toanother, constituting a new stable state.
2.2. Behavioral insights for low-income settings
‘Behavioral insights’ is the collective term for
empiricallygrounded knowledge based on cognitive psychology,
behavioralsciences and the social sciences about how people behave
andmake choices. Behavioral insights are applied to better
understand,and predict, human decision-making (Anderson &
Stamoulis,2006; Team, 2017). Insights from behavioral research tell
us that
https://www.sei.org/projects-and-tools/projects/sei-initiative-behaviour-choice/
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Fig. 1. Behavior change understood as a shift from one stable
state to another.
F. Lambe et al. /World Development 126 (2020) 104703 3
individuals typically make decisions based only in part on
eco-nomic rationales, acting to the best of their knowledge and
influ-enced by norms or emotional responses (Kahneman,
2013).Several underpinning principles have been shown to be
importantfor explaining decision-making and choice. These include
thinkingautomatically (Kahneman, 2013), the use of mental models,
andthinking socially (World Bank. (2014), 2014). The application
ofbehavioral insights in the field of public policy has gained
signifi-cant traction over the past few years, both nationally and
in inter-national organizations. A study by Lourenço, Almeida,
andTroussard (2016), commissioned by the EU, identified over
200examples in 32 countries of public policy initiatives related
tobehavioral perspectives.
There is a growing body of literature on the influence of
behav-ioral insights in the context of development interventions in
low-income settings, e.g. for programs focusing on agriculture
(Dufloet al., 2011; Liu & Huang, 2013; Verschoor, D’Exelle,
& Perez-Viana, 2016), improving the quality of education
(Benhassine,Devoto, Duflo, Dupas, & Pouliquen, 2015),
encouraging individualsaving (Karlan, McConnell, Mullainathan,
& Zinman, 2016), provid-ing access to electricity (Lee, Miguel,
& Wolfram, 2016) andimproving health outcomes (Hallsworth,
Snijders, Burd, Prestt,Judah, & Huf, 2016). Although widening
the geographical coverageof studies, they have been criticized for
being too narrowly focusedand difficult to generalize beyond
specific cases, and thus difficultto scale up (Datta &
Mullainathan, 2014; Tantia, 2017).
Datta and Mullainathan (2014) propose an approach for apply-ing
behavioral insights to the design of development interventionsand
suggest three stages in the process where behavioral insightscan be
influential: in defining the problem, in diagnosing the prob-lem,
and in designing the intervention. Datta and Mullainathan’smodel of
behavioral design has been further developed to includethe stage
‘‘scale” and possible iterative loops between the firstthree stages
(Tantia, 2017). See Fig. 2.
The authors of the behavioral design framework recognize
theopportunity to closely link behavioral insights and
interventiondesign and highlight the need to embed innovation in
the processby designing interventions with an iterative
experimentation pro-cess (Datta & Mullainathan, 2014). This
enables researchers andpractitioners to identify unintended
consequences, generate bettersolutions and diagnoses, and develop
diagnostic techniques rele-
vant for other contexts. An iterative experimental
approachrequires being willing to develop an intervention without
neces-sarily isolating the causal effect of a single cognitive
process orpathway, but rather focusing on a set of interconnected
designinnovations. A key strength of such an approach lies in the
thor-ough testing that takes place at each point in the process
allowingfor mistakes, for example, misdiagnosed problems to be
correctedalong the way.
Behavioral design has been applied extensively to identify
andaddress reasons why public programs are not performing
asexpected, the most advanced study being the Behavioral
Interven-tions to Advance Self Sufficiency (BIAS) project which
tested theeffect of behavioral nudges in 15 randomized control
trials in eightdifferent locations in the United States
(Richburg-Hayes, Anzelone,Dechausay, & Landers, 2017). The
study found that the nudgesapplied had a statistically significant
impact on at least one pri-mary outcome of interest, leading the
authors to conclude thatbehavioural interventions hold promise as a
tool for deliveringeffective public programmes (Richburg-Hayes et
al., 2017). Thebehavioral design approach used in the BIAS project
is similar tothe United Kingdom Medical Research Council’s
framework fordeveloping and evaluating complex interventions to
tackle healthproblems, (Craig et al., 2008) in that the design and
implementa-tion process is iterative.
A number of models have been developed that aim to
explainbehavior change related to the longer-term uptake of
developmentinterventions. Notable are Mosler’s RANAS framework for
behaviorchange in the water and sanitation sector (Mosler, 2012),
the Beha-viour Centered Design Framework (Aunger & Curtis,
2016), USAID’sDesigning for Behaviour Change Framework (USAID,
2017a) andMichie et al. (2011) Behaviour Change Wheel, a synthesis
of 19frameworks of behaviour change, drawn from a wide range
ofdomains. Although each of these frameworks provides a usefulguide
for practitioners seeking to develop behavior-based inter-ventions
in low income settings, they do not fully address the com-plexity
of implementing interventions in terms of how
behaviouraldeterminants change over time, the motivations of
different typesof users of a service or system or the variety of
actors involvedbeyond the individual and household scale, all of
which have con-sequences for how sustainable and scalable an
intervention is overthe long term.
-
Fig. 2. The stages of the behavioral design process adapted from
(Tantia, 2017).
4 F. Lambe et al. /World Development 126 (2020) 104703
2.3. Service design
Service design has a legacy in design and service
research(Patrício et al., 2018) and emerged around the turn of the
millen-nium (Segelström, 2013; Wetter Edman, Göteborgs universitet,
&Konstnärliga fakultetskansliet, 2011). As a design practice,
servicedesign is a creative, human-centered and iterative approach
to ser-vice innovation (Wetter-Edman et al., 2014), gaining ground
as asystematic method for creating systems and services that are
use-ful, efficient, effective and desirable to the user (Penin,
2017;Stickdorn & Schneider, 2012). Service design is a
qualitativeapproach to understanding people’s needs, wider context,
motiva-tions and behaviors, which aims to co-create improved
services orsystems that better meet their needs (Edvardsson et al.,
2012;Manzini, 2015; Pfannstiel & Rasche, 2017). Service design
may beseen both as a set of tools and techniques as well as an
approachto service innovation and shows promise as a methodology
toaddress challenges within the public sector (Malmberg, 2017)and
for addressing wicked problems in social systems (Banathy,1996;
Jones, 2014).
A central tenet in service design research is the principle of
co-creation, where actors in service systems engage in a creative
pro-cess to define problems and explore solutions. In recent years,
ser-vice design has been increasingly applied in low-income
settings toimprove public services to better meet the needs of
users and deli-ver positive social impact through so-called design
labs, or publicpolicy labs (Bason, 2017; Escobar, 2017).
3. Generic description of the methodological framework
Our conceptual framework draws on the Behaviour ChangeWheel
framework, developed by Michie et al. (2011). In this frame-
Fig. 3. Moving from one state of behavior
work, capability, opportunity, and motivation interact to
generatebehaviour in a system known as COM-B (Michie et al., 2011).
Thesecomponents can be linked to more fine-grained
behavior-changetechniques (BCTs), which are active components of an
interventiondesigned to change behavior (Michie et al., 2013).
Michie et al.have systematically generated and applied collections
or ‘‘tax-onomies” of BCTs and from there, developed a ‘‘cross
behaviour”taxonomy which includes 93 distinct BCTs (Michie et al.,
2015).To enhance usability and accuracy of the taxonomy, the
identifiedBCTs were organized into 16 groups (Michie et al.,
2015).
Michie et al. (2011) demonstrate the connection between
capa-bility, opportunity and motivation, key behavioral
mechanismsand BCTs, and the interventions and policies that could
be intro-duced to target or change these mechanisms. However, as
Michieet al. highlight, ‘‘there is a general recognition that
context is keyto the effective design and implementation of
interventions, butit remains under-theorized and
under-investigated”. And althoughthe Behavioral Change Wheel links
behavioral mechanisms todecision-making, it does not account for
the need to coordinateand sequence BCTs within a process of
developing and implement-ing an intervention. Failure to coordinate
BCTs within a changeprocess can result in a ‘‘scattershot” approach
to interventiondesign whereby knowledge about behavioral drivers is
applied atthe wrong point in the process, where synergies between
BCTsare missed or, where BCTs come into conflict with one
another.This is shown in Fig. 3 where BCTs are depicted as
scattered puzzlepieces.
By using service design, it is possible to identify, at the
individ-ual level, behavioral drivers, their underlying mechanisms,
BCTsand behavioral archetypes, and to relate these to a specific
changeprocess in a coordinated way. This is illustrated in Fig. 4
where thepuzzle pieces, representing BCTs, are joined up. The
service design
to another without coordinating BCTs.
-
Fig. 4. Moving from one state of behavior to another with a
coordinated approach.
F. Lambe et al. /World Development 126 (2020) 104703 5
approach strengthens Datta and Mullainathan’s behavioral
designprocess by demonstrating how behavioral change mechanismsand
BCTs could be sequenced over time to support sustainedbehavior
change in the context of a development intervention.
Our conceptual framework for intervention design (depicted
inFig. 5) follows six consecutive stages. Whereas the
behaviouraldiagnosis and design model recommends the iteration
between
Fig. 5. Conceptual framework for beha
the first three stages (define, diagnose and design)
(Barrows,Dabney, Hayes, & Rosenberg, 2018), our framework
suggests thatiterations should be made between each stage.
Stage one – Problem co-definition – is a formal and
thorougheffort to gather evidence to support the initial underlying
assump-tions of an intervention. This is done in close
collaboration withkey actors and stakeholders, including the
intervention funder(s),
viour-based intervention design.
-
6 F. Lambe et al. /World Development 126 (2020) 104703
target beneficiaries and sector experts. This stage can also
involve areview of the existing literature on the context of the
intervention.
Stage two – experience-based problem diagnosis –verifies
theproblem identified in stage one from the perspective of the
benefi-ciary (e.g. individual, household, farmer) to identify the
underlyingcauses. User journey mapping is conducted and key
behavioral dri-vers at the individual level are identified in
different phases of thejourney (see supplementary material for a
detailed account of userjourney mapping). This stage also merges
iterative interventiondesign with the actual journey of the target
actors through anintervention, allowing pivotal ‘behavioral
moments’ to be identi-fied, highlighting, for example, where an
intervention could bederailed due to a disconnect with the users’
needs or motivations,or the emergence of previously undetected
opportunities to sup-port the intervention. Unlike behavioral
mapping which pinpointsdiscrete behavioral action points in a
process and seeks to under-stand ‘‘sub-optimal behavior” (Barrows
et al., 2018; Richburg-Hayes et al., 2017), the objective of
user-centered mapping is tounderstand the user’s behavior and
decision making in relationto the wider system, beyond a given
intervention process.
Stage three – System mapping – involves creating a detailed
mapof the entire system, including the socio-economic,
ecological,structural and institutional dimensions of the setting,
using thetarget beneficiary’s experience or ‘journey’ as the
starting point.Though similar to process mapping whereby steps in a
pro-grammes’ process are analyzed from the perspective of the
pro-gramme clients (users) and staff (Richburg-Hayes et al.,
2017),actor mapping seeks to widen the scope and includes the roles
ofall key actors and processes in the intervention, as well as
actorsnot directly engaged in the intervention but who have
influenceover or interests in it. The resulting system map from
stage twois ‘‘verified” with stakeholders in a workshop setting to
ensureaccuracy. See supplementary material for a detailed
descriptionof system mapping.
Stage four – rapid prototyping – uses the insights gathered in
theprevious three stages to develop rapid prototypes (quick
sketches)of an intervention which are then piloted in a subset of
the targetpopulation to verify the insights gathered so far and to
reducedesign flaws in the intervention. These rapid prototypes
areinformed by the BCTs and behavioral mechanisms, in turn basedon
the behavioral drivers identified in stage two. Rapid
prototypingcan be used in parallel with experience-based problem
diagnosis, togenerate rough ideas about the design of the
intervention whichcan be quickly tested during interviews with
users.
Stage five – design and testing – tests a fully designed
interven-tion in a subset of the population and makes changes based
on theresults.
Stage six – upscaling – scales up the intervention beyond the
ini-tial population, possibly in a new location. This stage
entailsreturning to stage 1 and following the cycle again in order
to verifyassumptions about a new location and new actors. The
second
Table 1Stages of the framework applied in each case study and
key contributions of each case st
Case study Stages of the framework app
Kenya and Zambia cookstoves (2015–2016) Stage two
experience-based pexperience-based problem diaexperience-based
problem diaexperience-based problem diaRapid
prototypingexperience-based problem dia
Kenya mango farmers (2017) experience-based problem diaSystem
mappingRapid prototyping
Zambia mini/grids (2017) experience-based problem dia
stage should be straightforward because it builds on
previouslycollected and verified data. However, care is taken to
identify crit-ical elements that might need to be re-designed for
the interven-tion to be transferrable to a new location.
Ideally, all the above steps would be followed early in the
pro-cess of designing an intervention. However, as illustrated by
thethree case studies described below, the conceptual frameworkcan
also be applied at any stage in the intervention process to
iden-tify and correct flaws in design.
4. Application of the conceptual framework
The conceptual framework was developed iteratively through
aseries of case studies on behavior change in relation to the
uptakeof new technologies. We present findings from three case
studies:uptake of clean cookstoves in peri-urban Nairobi, Kenya,
and urbanLusaka, Zambia; uptake of off-grid electricity services by
house-holds in Zambia; and uptake of pre- and post-harvest
technologiesamong mango farmers in Kenya. The case studies follow
the devel-opment of the interventions with a focus on improving an
existingintervention, rather than informing the design of a new
one. Eachcase study has contributed differently to the development
of theconceptual framework, as Table 1 illustrates.
4.1. Identifying user archetypes and opportunities for behavior
changetechniques: Case study on adoption of advanced biomass
cookstoves inKenya and Zambia
Approximately 80% of households in sub-Saharan Africa do nothave
access to clean energy for cooking (International EnergyAgency.
(2018), 2018). Although advanced cookstoves have beenpromoted for
decades by governments, NGOs and the private sec-tor in different
parts of the world, the level of adoption still falls farshort of
what is needed to achieve substantial benefits (Barnes,2014). We
used case studies from Kenya and Zambia to examinewhat drives
households to adopt clean stoves for most or all oftheir cooking
needs. The study aimed to better understand the dri-vers of
behavior related to adoption of clean cookstoves by house-holds in
Kiambu, Kenya and Lusaka, Zambia. In each case westudied cookstove
users’ experience of purchasing and using anadvanced biomass
cookstove. The main research question askedwhat support is needed,
and at which point in the actor journeyis it needed, to achieve
lasting behavior change? Results and con-clusions from this case
study are further described in Jürisoo,Lambe, and Osborne
(2018).
4.1.1. MethodsThe primary methods to collect data were
open-ended inter-
views using trigger material, rough pen-and-paper sketches
usedto discuss prototypes of possible changes to the intervention,
anduser journey mapping. For selecting interviewees, our main
crite-
udy to the framework.
lied Key contribution to framework development
roblem diagnosis User journey mappinggnosis Identifying BCTs at
different phases in the journeygnosis Sequencing of BCTsgnosis
Identifying archetypes
Targeted solutions for identified archetypesgnosis Linking
archetypes to BCTs
gnosis User journey mappingSystem map/value chain mapTargeted
solutions for specific actors
gnosis User journey mapping, over time
-
F. Lambe et al. /World Development 126 (2020) 104703 7
rion was cookstove users who had purchased an advanced
cook-stove. In both locations, we made use of existing partnerships
tofacilitate interviewee selection and access to households that
hadpurchased advanced cookstoves. In Kenya we conducted 19
inter-views and in Lusaka we conducted 17.
We gathered data to map a composite user journey, broken
intophases of ‘‘before”, ‘‘during” and ‘‘after” using the stove.
The ‘‘be-fore” phase refers to the stages of hearing about the
stove anddeciding to purchase it; the ‘‘during” phase refers to the
periodof starting to use the stove and establishing a new cooking
prac-tice; the ‘‘after” phase refers to the period when the user
startslooking for a new technology to replace and/or complement
thestove. The user journey phases were developed during the
Kenyanstudy and further tested and validated in the Zambian
case.
4.1.2. Key results and discussionThe composite user journey is
presented in Fig. 6. The red dots
illustrate key components of the behavioral change process
understudy: becoming aware of advanced stoves, buying a stove,
andmaking it the household’s main or only cooking device. The
bluedots represent points during the user journey where, if
conditionsare unfavorable, the opportunity to induce a change in
cookingpractices can be lost. These can also be viewed as points in
thedecision-making landscape where a specific type of support is
vitalfor achieving a long-lasting change in behavior.
A key finding of the case studies was that for the
cookstoveinterventions to be successful (i.e. advanced cookstove s
areadopted by households), BCTs, need to be identified and
carefullysequenced throughout the user journey. There is a tendency
forcookstove programs to focus their efforts on the ‘‘before”
phaseof the user journey, on the provision of marketing and
technicalinformation to support potential users to acquire a new
cookstove.We found, in both studies, that acquiring the new stove
is only thefirst step. What the user experiences when getting
started with thenew technology, in the ‘‘during” phase, and how
well the technol-ogy meets their expectations, is critical. See
supplementary mate-rial for a table summarizing the BCTs identified
in the ‘‘during” and‘‘after” phases of the user journey.
Fig. 6. Composite actor journey map for impro
Thus, the identified BCT grouping ‘continuous social support’,
tohelp users overcome technical problems, remind householdsabout
how to use and maintain the stove, and build confidenceusing the
new technology, is imperative at the start of the ‘‘dur-ing” phase.
However, this BCT grouping will be less useful if it isnot
available to the actors early on; they may already have
expe-rienced disappointment that the intervention is not fulfilling
theirexpectations.
In addition, the study identified that the BCT groupings
‘changein physical environment’ (i.e. increasing fuel availability)
‘rewardand threat’ (i.e. financial incentives) and ‘shaping
knowledge’ (i.e.information access) are also crucial at different
stages along thejourney. For more detail see supplementary material
and (Jürisooet al., 2018).
Beyond sequencing of BCTs, the user journey mapping alsohelped
in identifying three distinct types of cookstove user,
looselydefined by their main motivation for purchasing the stove.
We alsoobserved that each type requires specific support at
differentpoints in the adoption process. Those who were motivated
to pur-chase a stove by saving money tend to take several weeks
beforethe value of the stove is realized. These users seem aware of
thefact that change takes time and are willing to continue to use
thestove even where problems were encountered early on. In termsof
support, these users need accurate technical information onstove
use, how to optimize use so as not to waste fuel, and howto avoid
accidents with the stove.
The user group motivated by convenience needs a
relativelyimmediate improvement for the value of the stove to be
realized,otherwise they tend to become disillusioned. This group
requirescontinuous support from the start, ideally from a trusted
source.Users attracted to the aesthetic appeal of the stove
reported pur-chasing the stove to increase their social status or
to be perceivedas modern and aspirational. We found that for this
group of usersthe ‘‘newness” tended to decrease over time and the
immediaterewards in terms of less smoke and fuel saved do not
necessarilymotivate long-term use. Compared to other types of
users, thisgroup did not appear to need as much support in the
early phaseas those motivated by convenience or by saving
money.
ved cookstove users in Kenya and Zambia.
-
8 F. Lambe et al. /World Development 126 (2020) 104703
4.2. User mapping to understand the wider context: case study
onsolar PV mini-grids for household electricity provision in rural
Zambia
More than half of the population of sub-Saharan Africa –
590million people – do not have access to electricity
(InternationalEnergy Agency, 2018). Renewable energy mini-grids are
expectedto play a major role in the pursuit of universal access to
modernenergy services, particularly in areas where grid extension
is tech-nically or financially unviable (IRENA, 2013; Szabó, Bódis,
Huld, &Moner-Girona, 2011). Out of the roughly 315 million
rural Africansthat the IEA envisions will gain electricity access
by 2040, about45% would be served by mini-grids (International
Energy Agency,2014). However, little is known about the
socioeconomic determi-nants in Africa of uptake of electricity from
renewable energymini-grid systems. This case study explored the
behavioral andsocio-cultural factors that support and constrain the
adoption ofelectricity services provided by a solar mini-grid
project in ruralZambia.
The case study location is Mpanta, Zambia. Mpanta is a
ruralcommunity of 2673 people, situated on the shores of Lake
Bang-weulu in Luapula Province in northern Zambia
(RuralElectrification Authority. (2016), 2016). In November 2013,
a60 kW solar mini grid in Mpanta was commissioned by the
RuralElectrification Authority to provide essential electricity
servicesfor lighting (including street lighting) and light load
appliances(such as televisions, radios, fridges and mobile phones)
to 450users comprising of households, a school and staff houses, a
ruralhealth center, harbor facilities, small businesses and
churches.
After commissioning of the mini-grid in 2013, users were
ini-tially connected for free. Each user was required to pay a
monthlyfixed tariff based upon their user category (i.e.
residential, com-mercial and social services) and, if a residential
user, the numberof rooms in their house. Following this initial
free connection per-iod, a 50 ZMW (5 USD) connection fee and 15 ZMW
(1.5 USD) wir-ing fee were introduced. Meanwhile, to encourage
communityparticipation and ownership of the project, mini-grid
operation,plant maintenance and revenue collection was handed over
to alocal Multi-Purpose Cooperative Society (hereafter
KafitaCooperative).
4.2.1. MethodsUser journey mapping was used to map and explore
users’
needs, expectations and experiences ‘‘before”, ‘‘during” and
‘‘after”connecting to the solar mini-grid. The user journey mapping
wasconducted based on 28 semi-structured interviews with usersand
non-users of the solar mini-grid services. Intervieweesincluded 21
households (12 still connected, 4 disconnected and 5never
connected), 5 businesses (4 still connected, 1 disconnected)and 2
institutions (both still connected). Data saturation wasreached
after 28 interviews, thus determining the sample size.
For data analysis, user and non-user interview responses
werecoded in a spreadsheet based on user category (households,
busi-nesses and institutions) and connection status (connected,
discon-nected and not connected). Responses were then analyzed
togenerate insights on the varied emotional and physical
experiencesassociated with connecting to the mini-grid and using
its servicesand the different contextual factors shaping adoption
or non-adoption of mini-grid electricity services. For more
detailed resultsand conclusions see (Muhoza & Johnson,
2018).
4.2.2. Key results and discussionKey results from the Zambia
case relate to the importance of
embedding local context and the needs and motivations of
theusers of services in the design of an intervention. Mapping
theexperiences of individual users over time highlighted
inconsisten-
cies in the delivery of the intervention which can result in
disap-pointment and reduced overall effect of the scheme.
The user journey map in Fig. 7 visualizes the experience of
usersand non-users before, during and after connecting to the
Mpantasolar mini-grid. These three stages in the user journey
correspondto: becoming aware of the mini-grid service, getting
connected toit, and continued or discontinued use of electricity.
Fig. 5 highlightstouch points associated with the various phases in
the userjourney.
The case study found that information about the interventionwas
not coherently provided to all users, in the same way. Forexample,
some were informed that the electricity provided wouldbe free of
charge while others were aware of the actual costs. Somewere told
that they would be able to use the electricity for cookingand other
uses that would require heavy loads, even though thesystemwas not
designed for heavy loads. Others had a clear under-standing of the
capacity of the system.
The experience of becoming connected to the mini-grid alsovaried
greatly depending on when households joined the scheme.Those who
participated in the scheme early on, and benefited fromfree
connections, were first visited by an agent from the Rural
Elec-trification Agency who brought the application form to their
homefor completion, and then by an engineer who would install the
nec-essary wiring to connect the household to the distribution
networkand provide free lightbulbs. Those who joined the scheme
later hadto visit the Kafita Cooperative offices in person and
apply and paythe connection fees, which were often prohibitive for
low-incomehouseholds.
Many users (47%) were disconnected because they defaulted
ontheir payments. The user journey mapping provided
insightfulinformation about why so many were disconnected. The
commu-nity relies on small-scale fishing as the main source of
income,but during December to March every year a ban is imposed
toallow fish stocks to replenish. During this time, household
incomestend to decrease, leaving many users unable to afford the
fixed tar-iff. Thus, if a similar mapping had been conducted while
designingthe business model for the mini-grid, economic incentives
would,preferably, have been directed to cover costs during this
period,and attract and retain users.
4.3. Situating individual behavior within the wider system: case
studyon technologies for reducing post-harvest losses in small
scale mangoharvesting in Kenya
Agriculture is the most important provider of livelihoods
inKenya, with more than 75% of the population depending on
thesector for food and income (USAID, 2017b). Mango is an
importantfood and cash crop, with a six-fold production increase
between2000 and 2014. However, more than 25% of the crop is
currentlylost during and after harvesting due to pests, inadequate
on-farmstorage and a lack of direct access to markets among
small-scalefarmers to sell their produce (Financial Sector
Deepening (FSD),2015). To reduce losses, development interventions
have beenintroduced to small-scale farmers to reduce losses and
improveincomes by producing higher-quality fruits, to process the
man-goes (into more durable and/or higher-value products), and
toimprove fruit storage. However, uptake of technologies is
generallylow, particularly among more marginalized groups,
includingfemale-led households.
The study focused on two sites: Tana River County in
easternKenya, and Meru County in the center of the country. With
76.9%of the population living below the poverty line, Tana River
Countyis among the poorest in Kenya (CRA, 2011). Approximately 40%
ofhouseholds in Tana River County are engaged in small-scale
farm-ing (MOPHS & IMC, 2010). The poverty rate in Meru County
is28.3% which is well below the national poverty rate of 47.2%
-
Fig. 7. Actor journey map for users and non-users of electricity
from Mpanta mini grid.
F. Lambe et al. /World Development 126 (2020) 104703 9
(CRA, 2011). High-input, rain-fed agriculture complemented
byirrigation is the main source of livelihood in the county,
contribut-ing about 80% to the average household income (MoALF,
2016).The study intended to investigate how to improve the
develop-ment and implementation of technologies, aiming to reduce
lossesamong smallholder mango farmers.
4.3.1. MethodsIn this case study, the intervention aimed to help
small-scale
farmers to reduce loss of their mangoes by marketing
improvedharvest and post-harvest technologies. The primary actors
wereassumed to be smallholder farmers, and technologies were
alreadydeveloped and introduced to farmers at different locations
in inKenya. However, we applied user journey mapping to help
identifynot only what could be improved in terms of technology
design,but also the underlying factors behind the low uptake of
technolo-gies. In addition, we conducted participatory
observations, open-ended interviews with a range of stakeholders
and two field work-shops, in total 206 interactions with
stakeholders in the two loca-tions. Periodically, we presented our
evolving analysis to farmersand other stakeholders for their
feedback. We also mapped themango value chain within which the new
technologies and ser-vices would be provided, to understand the
roles of, and relation-ships between, different actors and the
links between them atmultiple scales. These insights were
consolidated in a systemmap and a corresponding narrative for mango
farmers in Holaand Meru.
4.3.2. Key results and discussionThe case study demonstrated the
need to account for the inter-
ests and incentives of a wide system of actors when designing
anintervention. In terms of reducing losses in Kenyan mango
produc-
tion, we found that the underlying problem that the
interventionsought to address had been misdiagnosed, and as a
consequence,interventions were not developed and introduced to the
rightactors. As illustrated in Fig. 6, the user journey of a farmer
couldfollow a number of different scenarios: the farmer could
indeedbe harvesting the fruits, and thus be the target beneficiary
of inter-ventions aiming to reduce pre-harvest and post-harvest
losses.However, many farmers did not harvest the fruits but either
hiredharvesters, or sold fruits to a broker, who used their own
har-vesters. In these cases, the technologies were introduced to
thewrong group of actors. The systems mapping identified an arrayof
actors, interacting in various ways. Besides farmers and end-buyers
(such as retailers or mango-processing companies) therewere three
other key actors (or key roles) in the value chain: har-vesters,
brokers and farmer organizations (see Fig. 8 below). With-out
mapping the entire system, the importance of these otheractors and
their connection to the farmers would not have beenidentified.
The pre-harvest and post-harvest interventions had been
devel-oped with the objective of enhancing the quality of the
produceand thus improving farmers’ incomes. However, the study
foundthat farmers did not have access to the market, or buyers,
thatwould give them increased return for better quality fruits.
Further-more, since harvesting was carried out by hired labour,
reduceddamage to fruit during the harvest was out of the farmers’
control,which meant that technologies to reduce such losses were of
littleuse to the farmers.
5. General discussion
In this section we discuss the conceptual framework consider-ing
the case study findings, with a focus on overarching parameters
-
Fig. 8. Actor scenarios along the mango value chain.
10 F. Lambe et al. /World Development 126 (2020) 104703
that should be considered when designing robust and
imple-mentable interventions in a low-income context.
5.1. Behaviour change occurs (and is reinforced) within separate
butinterconnected phases of the actor journey
Depending on the type of behavior change required, usersengage
with varying intensity in different parts of the user journey.For
adoption of an intervention, such as a cookstove, a
significantchange in behavior is required in the sense that it
involves activeengagement in several of the phases of the user
journey. The casestudy on adoption of improved cookstoves in Kenya
and Zambiademonstrates that developing a new habit with a new
technologyrequires a user to stay motivated over a long period of
time. Inaddition, users need to learn how to use a new technology
earlyin the process, which requires changing several behaviors at
once,e.g. cooking food more quickly, not leaving the stove
unattended ornot using the traditional stove, and maintaining the
behaviorchange until new habits are formed.
With high-effort behavior change, such as technology
adoption,supportive actions may be needed over a long period, as
the changein behavior is not immediate, nor a one-time action.
Breakingdown the experience of adopting a new technology into
con-stituent phases provides a simple way to identify when, in
thejourney, the user needs support to develop a new habit and
whattype of support is needed.
As proposed in this study, key behavioral drivers, BCTs
andbehavioral mechanisms must be identified and sequenced intothe
decision-making landscape, for interventions to be successfuland
scalable. Based on our case study findings, we suggest
thatdesigning interventions requires active engagement on the
partof the implementer or service provider to iteratively develop
theservice or intervention in collaboration with the users,
particularlywhen the intervention in question seeks to introduce a
new tech-nology or displace an old one.
In the cookstove and mini-grid cases, we found that the extentto
which user expectations set in the ‘‘before” phase of an
interven-tion are met in the ‘‘during” phase is central to success.
In the cook-stove case, individuals and households were provided
withinformation about the functioning of advanced cookstoves byway
of marketing and promotion – often related to the high effi-ciency
of the stoves and the potential to save significantly on
fuelpurchase. Indeed, the potential to save fuel was the most
com-monly cited reason for purchasing an advanced cookstove.
Where
users encountered problems getting started with the stoves,
andfuel savings were not quickly realized, the result was often
disap-pointment and, in some cases, reduced or discontinued use of
thenew stove.
In the mini-grid case there is a clear pattern of unmet
expecta-tions among service users. Following connection to the
mini-grid,households reported being disappointed that they were
unable touse the electricity for cooking or productive uses and for
someusers the connection fee was higher than expected.
Although awareness raising and promotion of new services
andtechnologies is necessary, implementers and service
providersneed to strike a careful balance between communicating the
ben-efits of the new service or technology and ensuring a clear
under-standing on the part of their users of the costs and
limitations. Thecareful management of expectations in the ‘‘before”
phase, basedon a clear understanding of the factors motivating
users to adopta new technology or service, is a prerequisite for
adoption and sus-tained use of the technologies and services in the
‘‘during” phase.
In the case of Kenyan mango farmers, the technologies
them-selves were not necessarily malfunctioning; the problem was
thatthey did not target the challenges or bottlenecks that users
faced,nor were the behavioral components that needed to
changesequenced in the decision-making landscape of the system.
Thestudy showed that to achieve the objectives of the
intervention,farmers must be provided with the appropriate
incentives. Farmersneed assurance that adopting a technology to
improve the qualityof their produce would generate a higher income,
otherwise thetraditional harvesting methods, which are less costly
both in termsof time and capital, will remain more attractive.
5.2. Identifying where BCTs or groups of BCTs could be
applied
The Kenya and Zambia cases identified behavioral drivers
ofimproved cookstove uptake, and the BCTs that could support
theprocess of behavior change. In a recent review, the BCT
‘‘shapingknowledge” was identified as the active ingredient in 85%
of cook-stove interventions globally and ‘‘social support” in 64%
(Goodwinet al., 2015). Thesefindings suggest thatdesigners of
improvedcook-stove interventions have confidence in the effect of
these BCTs. TheBCTs ‘‘Shaping knowledge” and ‘‘social support” are
often appliedtogetherwith ‘‘reward and threat” (most often in the
formof a finan-cial incentive) to encourage uptake of improved
cookstoves.
However, our conceptual framework extends the work ofGoodwin et
al. (2015) by not only identifying key BCTs but also
-
F. Lambe et al. /World Development 126 (2020) 104703 11
illustratingwhere in the behavior change process they are most
rel-evant. For example, in the case of cookstove adoption, our
studiesidentify that ‘‘shaping knowledge” (information and
demonstra-tion), ‘‘reward and threat” (in the form of financial
incentives)and the ‘‘impact of peers” (friends and social groups)
are importantBCTs in the ‘‘before” stage of cookstove adoption,
while ‘‘social sup-port” (user support to overcome problems using
the stove) and ‘‘re-ward and threat” (ongoing financial incentives,
e.g. subsidized fuel)are more important in the ‘‘during” phase.
5.3. The importance of user archetypes
The Kenya and Zambia case studies demonstrate that the
con-ceptual framework can be useful for identifying categories of
users,and the type of support that they require in different stages
inintervention. For example, we identified three main cookstove
userarchetypes, defined by their key motivation for purchasing
andusing an improved cookstove: those who were motivated by
savingmoney, those who sought convenience and those who
appreciatedthe aesthetic appeal of the new cookstove. The user
journeys foreach archetype revealed positive and negative
experiences in dif-ferent phases of the actor journey and, thus,
very different needsin their journey toward adopting the
cookstoves.
The mango case study demonstrates the importance of
under-standing the roles, motivations and incentives of all key
actors inthe system when designing an intervention aimed at
improvingsystem outcomes for individual actors. It also highlights
the needto work closely with the target users of a technology in
the earlystages of intervention design, both in co-identifying the
problemand co-designing the intervention.
5.4. Connecting interventions to the wider social-ecological
context
Users in our case studies are not behaving and making
decisionsin isolation; rather they are embedded in multi-level
systems withother actors and ongoing processes. All three case
studies highlightthe importance of situating an intervention or
change processwithin a broader societal context or social system.
The mini gridscase study in Zambia makes shows that it is important
to acknowl-edge that development interventions operate within a
social-ecological system. Despite economic incentives (e.g. low
fixed tar-iffs) users were unable to afford the cost of the
electricity service.The business model had failed to recognize that
the interventionwas introduced in a community highly dependent on
seasonalincomes. This could easily have been acknowledged and
designedfor, if actors had been consulted early in the design
process andimplementation plan. In order to improve actors’ ability
to payon a regular basis, there is a need to diversify actors’
sources ofincome. In the mango case study, interventions were
introducedthat sought to change behavior and decisions in relation
to agricul-tural management. However, the targeted value-chain was
notthoroughly mapped to identify interlinked actor scenarios.
Pre-and post-harvest technologies were introduced to farmers who
inmany cases were not involved in pre and post-harvesting of
man-goes. In addition, farmers were assumed to be primarily
mangofarmers, yet mangoes were seldom their primary source of
income.If the development and delivery of technologies had been an
iter-ative and collaborative process involving all relevant
stakeholders,it is likely that relevant actor groups and the most
appropriateBCTs for targeting them would have been identified early
on.
5.5. Limitations and future research
The conceptual framework has been applied in only three
cases,all located in either Kenya or Zambia, which may limit the
gener-alizability of the findings to other contexts. In addition,
the frame-
work has so far only been tested in cases focusing on the uptake
ofnew technologies, and not yet applied in cases where the focus
ischange of practice. However, the case studies focused on
technolo-gies that are relevant far beyond the geographic scope of
the casestudies, and on communities of smallholder farmers and
house-holds that share characteristics with millions of people in
sub-Saharan Africa and South and Southeast Asia. Furthermore, for
allcase studies described here, the ‘‘real world” interventions in
focuswere already in the early implementation phase at the time
weconducted fieldwork, which meant that we did not have the
oppor-tunity to study other phases, for example ‘‘upscaling”. Thus,
ourinsights about the usefulness of the conceptual framework
arebased on the study of a limited number of stages of the
interven-tion development process and the approach would benefit
fromfurther development and testing.
There is a growing number of studies that apply
behaviouraldiagnosis and design approaches to intervention design,
and somestudies have conducted rigorous testing of the behavioural
designapproach (e.g. (Richburg-Hayes et al., 2017). In addition,
there arestudies looking at sustained change in behaviour, for
example(Allcott & Rogers, 2014; Ashraf, Bandiera, & Jack,
2014; Hussamet al., 2016). Our proposed framework seeks to
contribute to thefield by addressing behavior change in complex
systems wheremultiple behaviors need to change at different points
in time dur-ing an intervention process, and where actions may be
needed by arange of actors at different parts of the system.
6. Conclusions
Behavioral science-based approaches to designing and
testingdevelopment interventions have come a long way in terms of
iden-tifying key cognitive processes and behavioral levers to
triggerbehavior change in low income settings. However,
theseapproaches do not fully account for the complexity and
interde-pendence within social-ecological systems, or for the fact
thatchange processes, once triggered, play out over time and are
expe-rienced differently by different people or archetypes in a
system.
The conceptual framework proposed in this paper seeks tomerge
the methodological approach of service design with behav-ioral
insights to better address complexity in social-ecological
sys-tems. Service design offers both a process for carefully
findingsolutions, and a methodology for basing such solutions on a
knowl-edge base that is as widely and inclusively informed as
possible.The user journey component of the framework allows us to
visual-ize the experiences and perceptions of users of a given
technologyor service throughout a change process and ensures that
importantbehavioral drivers and social processes are captured at
every phaseof the journey. The systems mapping component situates
the livedexperiences of users within complex social-ecological
systems andhighlights connections between users and other
potentially impor-tant actors and processes at different levels of
society.
In the case studies presented here we examined the
factorsaffecting a sustained shift in behaviors over time. These
are situa-tions where a change in behavior on the part of
individuals andhouseholds is required every day, and where behavior
may beinfluenced by multiple factors operating at different levels
– cogni-tive, psychological, social and structural – and where
feedbackloops may occur. We show how the proposed framework can
beused to pinpoint when in a in a temporal continuum a
behaviorchange technique or group of behavior change techniques
andbehavioral determinant is relevant, at what point in the
changeprocess they matter, and based on this how those that steer
inter-ventions can intervene to support lasting behavior change. As
such,the framework could help development practitioners and
donorsto plan and allocate constrained funding to focus on phase
process
-
12 F. Lambe et al. /World Development 126 (2020) 104703
that is likely to need more attention and resources. Our
aimbeyond this article is to is to apply the framework in
additional‘‘real world” case studies, cases that focus on changing
practicesand to update and refine the framework accordingly. We are
seek-ing opportunities for applying the framework in all phases of
inter-vention design, from Problem co-definition to scaling and
replicating.
Declaration of Competing Interest
This work was supported by the Swedish International
Develop-ment Agency (Sida), Stockholm, Sweden and the Rockefeller
Foun-dation, New York, U.S.A. There is no specific grant
numberassociated with the funding. Neither funding body was
involvedin the study design, data collection, analysis and
interpretation,report writing or decision to submit the article for
publication.
Acknowledgements
We wish to thank all the individuals and households in Kenyaand
Zambia who took time to speak with us and share their experi-ences
as service and technology users in the three case studies. Weare
also grateful to our partners at SNV, the Netherlands Develop-ment
Organisation; Emerging Cooking Solutions and Vitalite fortheir
invaluable input on the field work design and emerging con-ceptual
model. Finally, we wish to thank our service design col-leagues at
Expedition Mondial, Sweden for their supportconducting the case
studies, and their thoughtful input and creativeideas for
integrating service design into the conceptual model.
Appendix A. Supplementary data
Supplementary data to this article can be found online
athttps://doi.org/10.1016/j.worlddev.2019.104703.
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Embracing complexity: A transdisciplinary conceptual framework
for understanding behavior change in the context of
development-focused interventions1 Introduction and aim2
Theoretical background2.1 Social-ecological systems theory2.2
Behavioral insights for low-income settings2.3 Service design
3 Generic description of the methodological framework4
Application of the conceptual framework4.1 Identifying user
archetypes and opportunities for behavior change techniques: Case
study on adoption of advanced biomass cookstoves in Kenya and
Zambia4.1.1 Methods4.1.2 Key results and discussion
4.2 User mapping to understand the wider context: case study on
solar PV mini-grids for household electricity provision in rural
Zambia4.2.1 Methods4.2.2 Key results and discussion
4.3 Situating individual behavior within the wider system: case
study on technologies for reducing post-harvest losses in small
scale mango harvesting in Kenya4.3.1 Methods4.3.2 Key results and
discussion
5 General discussion5.1 Behaviour change occurs (and is
reinforced) within separate but interconnected phases of the actor
journey5.2 Identifying where BCTs or groups of BCTs could be
applied5.3 The importance of user archetypes5.4 Connecting
interventions to the wider social-ecological context5.5 Limitations
and future research
6 ConclusionsDeclaration of Competing
InterestAcknowledgementsAppendix A Supplementary dataReferences