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Towards dynamic research
configurations: A framework for
reflection on the contribution of
research to policy and innovation
processes
Marc Schut
1,
*, Annemarie van Paassen
1
, Cees Leeuwis
1
and
Laurens Klerkx
1
1
Knowledge, Technology and Innovation group, Wageningen University and Research Centre,
P.O. Box 8130, 6700 EW Wageningen, The Netherlands; Emails: marc.schut@wur.nl,
annemarie.vanpaassen@wur.nl, cees.leeuwis@wur.nl and laurens.klerkx@wur.nl.
*Corresponding author.
This paper seeks to contribute to a better understanding of the complex dynamics that shape the
contribution of research to policy and innovation processes that address ‘competing claims’ on
natural resources and their management. Research in the context of competing claims requires
strategies that: (1) can cope with high uncertainty and unpredictability; (2) are concerned with
understanding the multiple dimensions of the issue at stake; (3) can facilitate change across
different scales and levels; (4) include collaboration with different actors and stakeholders; and
(5) may imply new roles for research and researchers. This paper reviews and builds upon
research approaches to address these challenges. These research approaches are combined in
a framework for dynamic research configurations that aims to stimulate reflection among re-
searchers and to promote more embedded, context-sensitive and flexible research strategies.
Keywords: natural resource management; environmental science; research policy; science–policy
interface; interdisciplinarity; multistakeholder; Mode 2; agricultural innovation;
complex problems.
1. Introduction
Access to, and management of natural resources lie at the
heart of many local, national and international conflicts and
disputes (Giller et al. 2008). One of the reasons is that
natural resources have characteristics—limited quantity,
increasing scarcity, extractability, culturally defined
meaning and unevenly distributed (Cloke and Park
1985)—that give rise to people having competing claims
on those natural resources. The concept of ‘competing
claims’ was introduced and reconceptualised for the
field of environmental and agricultural sciences by Giller
et al. (2008) amongst others. They describe competing
claims in the light of: (1) uncertain and unpredictable
natural resource management contexts; (2) complex
multidimensional problems; (3) interactions between levels
within and across, for example, spatial and administrative
scales; and (4) multi-actor processes. Consequently, they
argue that: (5) research is likely to become contested in
such contexts and that new roles for research and re-
searchers may be required to contribute to solving
competing claims problems. In this paper we analyse the
dynamics of, and interactions between, these five challenges
and how they shape the credibility, legitimacy and relevance
of research in policy and innovation processes addressing
complex natural resources management problems.
Interest in the contribution of research to policy and
innovation processes dealing with competing claims on
natural resources has increased considerably (Cortner
2000; Hall et al. 2003; Sumberg 2005; Dilling 2007; Boaz
Science and Public Policy 41 (2014) pp. 207–218 doi:10.1093/scipol/sct048
Advance Access published on 11 August 2013
ß The Author 2013. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com
et al. 2009). This has resulted in studies that highlight the
(apparent) gap between research and societal stakeholders
(e.g. policy-making communities), but also in practical and
theoretical approaches to how this gap can be bridged (cf.
Cutts et al. 2011; Edelenbos et al. 2011; Hegger et al. 2012).
An example is the growing attention for the role of
intermediaries and innovation brokers who can connect
the worlds of research and, for example, policy (McNie
2007; Pielke Jr. 2007; Klerkx et al. 2009; Michaels 2009). A
review by Hessels and van Lente (2008) discusses the
disconnect between research and policy/innovation pro-
cesses from the perspective of different types of ‘traditional’
versus ‘new’ knowledge production systems. They compare
these with the distinction between Mode 1 (traditional) and
Mode 2 (new) research made by Gibbons (1994) and
Nowotny et al. (2003) which they regard as the ‘most
famous’ example of different knowledge production
systems (Hessels and van Lente 2008: 740). Table 1
provides examples of traditional and new knowledge pro-
duction related to the five key characteristics of competing
claims contexts.
Although thinking about different types of knowledge
production can be useful, we feel that the contribution of
‘traditional’ versus ‘new’ modes or systems of knowledge
production is too often classified as ‘either/or’, where one
system is favoured or promoted over the other (e.g.
Cortner 2000). Following Michaels (2009), who looked
at the relationship between knowledge brokering and dif-
ferent types of complexity levels of environmental policy
issues, we deem such ‘either/or’ approach too static to
address the complexity and unpredictability in policy and
innovation processes. Research can benefit from
perceiving, for example, Mode 1 and Mode 2 as two
ends of a spectrum between which many blends of
modes, theories, concepts, methods and roles for re-
search(ers) can co-emerge and coexist (Hessels and van
Lente 2008). Although this need for (more) flexible
research approaches to support policy and innovation
processes has been frequently advocated in the scientific
literature (Kristjanson et al. 2009; Dilling and Carmen
Lemos 2011), it requires unremitting attention as substan-
tial institutional changes in incentive and funding struc-
tures to actually promote research flexibility appear to
fall behind (Holmes and Clark 2008; Holmes and
Savga
˚
rd 2009). The contribution of this paper must be
positioned in this debate, as it provides a framework for
reflection on the relation between dynamic research con-
figurations and the contribution of research to policy and
innovation processes.
The objective of this paper is threefold. The first object-
ive is to elaborate on the challenges for research in
competing claims contexts, and to review, build upon,
and explore synergies between different approaches for
research to address these challenges (Section 2). The
second objective is to combine the different approaches
in a framework for dynamic research configurations that
can stimulate reflection among researchers working in
competing claims contexts (Section 3). The third objective
is to discuss how the framework for dynamic research con-
figurations can be used in practice and to identify chal-
lenges related to its use, resulting in suggestions for
further research (Section 4).
2. Research approaches to address
competing claims challenges
This section is structured along the analysis of the five key
characteristics of competing claims contexts:
(1) uncertainty and unpredictability,
(2) multidimensional problems,
(3) interactions between scales and levels,
(4) multi-actor processes,
(5) contested research(ers).
Below, we discuss how these characteristics relate and
what type of challenges they pose to the contribution of
research to policy and innovation processes in competing
claims contexts.
Table 1. Features of traditional and new knowledge production in relation to the key characteristics of competing claims contexts
Traditional knowledge production New knowledge production
Uncertainty and
unpredictability
Reduction of uncertainty and unpredictability are key object-
ives of research
Uncertainty and unpredictability are inherent part of the
research process
Multidimensional
problems
Problem dimensions are studied separately and along discip-
linary lines
Problem dimensions are studied in a holistic and integrated
way and from interdisciplinary perspective
Interactions between
scales and levels
Boundaries of scales and levels are clearly demarcated.
Limited attention for interactions between levels within
and across different scales
Boundaries of scales and levels are negotiated and
renegotiated over time. Attention for interactions between
levels within and across different scales
Multi-actor
processes
Research is conducted in isolation of societal actors and
stakeholders
Research is conducted in collaboration with different groups
of actors and stakeholders
Contested
research(ers)
Research produces authoritative, objective and value-free
knowledge
Research contributes to, and is subject of, ongoing negoti-
ations between actors or stakeholders
208
.
M. Schut et al.
Different types of approaches are explored to address
these challenges for research. These approaches are
neither exhaustive nor mutually exclusive. On the
contrary, they overlap, and we seek to explore synergies
between the different approaches in enhancing the contri-
bution of research to policy and innovation processes.
2.1 Uncertainty and unpredictability: Embedded and
process-oriented research strategies
Competing claims problems are often characterised by high
uncertainty and unpredictability. This relates both to the
nature of the problem and how it develops over time, as
well as to the impact and the potential side effects of
strategies to deal with the problem. In policy or innovation
processes adressing uncertain competing claims problems,
phases are often not linear or sequential, but iterative with
stages that can be more problem-oriented or more solution-
oriented (Sabatier and Jenkins-Smith 1999; Graffy 2008).
Problem-oriented phases include agenda setting, describing
and explaining problems, whereas solution-oriented phases
include exploring, designing, implementing and monitoring
strategies to solve the problem. (Giller et al. 2008).
Often, phases in research processes and policy or
innovation processes do not align (Schut et al. 2010).
One of the reasons is that research generally needs time
to generate credible findings, whereas decision-makers
often require quick results to demonstrate financial, ad-
ministrative and political accountability (Haas 2004;
Hoppe 2005). Furthermore, the perceived credibility, legit-
imacy and relevance of research is influenced by the phase
of the process. Research findings that are mobilised during
the solution-implementation phase may be framed as a
threat to the agreed upon solution, whereas similar
finding findings could have provided the basis for develop-
ing alternative solutions if mobilised during the problem-
oriented phase in the process. A study on the role of
research in developing flood prevention policy in the
Netherlands for example showed how lack of scientific
data resulted in ‘administrative’ river discharge norms,
and how during a later policy phase, ‘research-based’
river discharge norms that questioned the necessity of
some of the flood prevention policy measures were
ridiculed by the Dutch government (Schut et al. 2010).
This example illustrates that timing is a crucial factor
that influences the contribution of research to policy and
innovation processes. To better understand the dynamics
of policy and innovation processes in competing claims
contexts, researchers should try to strategically embed
themselves in policy and innovation processes and apply
context-sensitive research strategies. This can provide a
degree of flexibility to anticipate the changing problem
context, and to constantly determine when, how, and in
what form, research can efficiently contribute to exploring
solutions.
2.2 Multidimensional problems: Interdisciplinary
research approaches
Competing claims problems often have biophysical, social-
cultural, economic, institutional and political dimensions
(Schut 2012). Consequently, exploring solutions to
competing claims problems requires integrated approaches
in which the dynamics between the different problem
dimensions are analysed holistically and from different
disciplines (Spielman et al. 2009). Interdisciplinarity can
be interpreted as the practices that consciously transcend
the disciplinary or dimensional modes of knowledge pro-
duction (Weingart 2000). In interdisciplinary research, re-
searchers combine insights from different disciplines, or
researchers from different disciplines work together.
Traditionally, these disciplines are often categorised ac-
cording to the biophysical nature of the problem (e.g.
biology), the social and cultural practices in which the
problem is embedded and within which solutions can be
explored (e.g. anthropology), and the economic impact of
the problem and the economic viability of different solu-
tions (e.g. economy). More recently, increasing attention
has been paid to the formal and informal institutional
‘rules of the game’ and political power dynamics, and how
they constrain or enable solution space in policy and
innovation processes (Edelenbos et al. 2011; Hounkonnou
et al. 2012). Such institutional and political analyses provide
information on the feasibility and acceptability of policy
scenarios for different actor groups and the broader insti-
tutional context in which solutions can be explored and
implemented.
Interdisciplinary research approaches are generally
found to lead to more credible, legitimate and relevant
research outcomes for different groups of actors and stake-
holders (Cash et al. 2003; Hegger et al. 2012; Schut 2012).
Furthermore, it is often the combination of knowledge
related to ‘content’ and ‘process’ that can enhance the con-
tribution of research to policy and innovation processes.
For example, insight into multi-actor interactions or
formal decision-making procedures (process) may facili-
tate better understanding of when and how research on
the nature of the issue at stake (content) can contribute
effectively (Schut et al. 2010). Interdisciplinary research
approaches do not imply that there is no space for trad-
itional disciplinary research. Nor do they imply that one
researcher must be able to address all the dimensions of
competing claims problems alone. Rather, researchers
should work together and develop structures that enable
them to work across disciplines. This is further elaborated
in Section 2.5.
2.3 Interactions between scales and levels: Analysis
of scale dynamics
The biophysical, social-cultural, economic, institutional
and political dimensions of competing claims problems
discussed in Section 2.2 often have different meaning
Towards dynamic research configurations
.
209
across different scales and levels. Scales can be defined as
the frame of reference to structure, measure or study a
phenomenon, for example the spatial scale (Gibson et al.
2000; Vervoort et al. 2012). In line with Cash et al. (2006)
and Termeer et al. (2010), we define levels as the positions
on a scale, for example the global, zonal, habitat, land-
scape or patch levels that constitute the spatial scale.
Scale dynamics refer to the interactions between levels
within and across different scales (Cash et al. 2006).
Accordingly, scale dynamics analysis refers to the process
of describing and explaining such interactions.
A broad variety of scales and levels has been identified
(Gibson et al. 2000; Cash et al. 2006). In this paper, we
elaborate on the interactions between levels within and
across the spatial and administrative scales (see Fig. 1),
as these have particular relevance for policy and innova-
tion processes addressing competing claims on natural re-
sources. Depending on the nature of the issue at stake,
other scales and their respective levels can be included in
scale dynamics analysis.
Climate change is a classic example that illustrates the
interactions between different levels on the spatial scale, as
phenomena at the global level are rooted in processes at
landscape and patch level and vice versa. The administra-
tive scale contains different decision or policy-making
levels, ranging from supranational levels (e.g. the UN) to
more local levels (e.g. village or household) where
strategies for dealing with complex problems (such as
climate change) can be developed. Developments at one
administrative level can both enable and constrain devel-
opments at other administrative levels, although local
decision-making is often constrained by decisions or
policies developed at higher levels. According to Giller
et al. (2008), feasible solutions for competing claims
problems may emerge from balancing interests and
bridging perceptions across different scales and levels, for
which the analysis of scale dynamics is crucial. The ex-ante
analysis of scale dynamics can provide the basis for de-
veloping, for example, policy scenarios that inform
policy-makers on how changes or actions at one level
may provide opportunities or create challenges at other
levels (Cash and Moser 2000).
Cash et al. (2006) identified three types of scale dynamics
challenges: (1) ignorance about interactions between levels
within and across different scales, (2) different forms of
mismatches between scales and levels, and (3) plurality,
referring to the representation and participation of actors
and their scale- or level-related interests in policy and
innovation processes. The ex-ante analysis of scale
dynamics can effectively contribute to transforming these
scale dynamics challenges into opportunities (Schut et al.
2013a). First, ex-ante analysis can contribute to increasing
awareness about the interactions between scales and levels,
and their implications for policy and innovation processes;
for example by showing how policies developed at the
supranational level influence developments at the
national level. Secondly, it can support identifying
matches and prevent mismatches between scales and
levels. Mismatches occur when different levels within or
across different scales do not correspond, for example
when seeking to address a transfrontier problem (such as
river basin management) at the local administrative level
(e.g. village or household level) (Cumming et al. 2006;
Veldkamp et al. 2011).Thirdly, the analysis of scale
dynamics facilitates the identification of different actors
and their stakes and interests. This can provide the basis
for collaborative multi-actor learning.
2.4 Multi-actor processes: Analysis of boundary
arrangements at research–stakeholder interfaces
In any policy and innovation process, the relationships and
interactions between actors, their organisations and
networks are complex (Morriss et al. 2006). Following
the definition by McNie (2007 19), stakeholders are those
actors or groups:
[W]ith a vested interest in the outcome of a [...] decision and
can include just about anyone, e.g., citizens, farmers, resource
managers, business, politicians, and the like.
1
Stakeholder participation in policy and innovation
processes has become an established way of addressing
complex environmental problems and is perceived as a
critical success factor for sustainable development. The in-
volvement of different groups of stakeholders provides im-
portant insights about the different dimensions of the
problem and what types of policy solutions are technically
feasible, social-culturally acceptable and economically
Figure 1. Spatial and administrative scales and different levels
on these scales (based on Cash et al. 2006).
210
.
M. Schut et al.
viable. However, as stakeholders often tend to act stra-
tegically rather than collaboratively, multistakeholder
processes in competing claims contexts can easily become
‘arenas of struggle’ (Leeuwis, 2000: 946).
In multistakeholder processes where researchers are
actively involved, two types of interrelated interfaces can
be distinguished; stakeholder–stakeholder interfaces and
research–stakeholder interfaces. At these interfaces, re-
searchers, decision-makers and other stakeholders con-
tinuously negotiate their roles and the division of tasks
and responsibilities: a process that is referred to as estab-
lishing boundary arrangements (Hoppe 2005). The contri-
bution of research to facilitating boundary arrangements
at stakeholder–stakeholder interfaces in natural resources
management has been discussed extensively (e.g. Steyaert
et al. 2007). However, existing theories of boundary ar-
rangements that describe the relation between research
and stakeholders rarely go beyond the research–policy
interface as the unit of analysis (cf. Waterton 2005;
Klerkx and Leeuwis 2008). Furthermore, the theory is
applied statically to empirical material in the sense that
one boundary arrangement is used to characterise the rela-
tionship between research and stakeholders throughout a
policy or innovation process (Sterk et al. 2009). To better
understand the contribution of research to multistakeholder
policy and innovation processes, there is a need to analyse
boundary arrangements at multiple research–stakeholder
interfaces (e.g. research–government, research–private
sector, research–civil society) and how such boundary ar-
rangements evolve and change over time. Table 2 provides
examples of boundary arrangements that can emerge
between research and stakeholders. Boundary arrangements
at the research–stakeholder interface do not equal
researchers’ roles, as researchers may fulfil different roles
and undertake different activities within each of the
specific boundary arrangements (see Section 2.5).
A number of recent studies highlight that different
boundary arrangements at multiple research–stakeholder
interfaces can co-emerge and coexist (Neef and Neubert
2011; Hegger et al. 2012; Schut et al. 2013b). Boundary
arrangements at different research–stakeholder interfaces
influence each other, and are influenced by interactions at
different stakeholder–stakeholder interfaces. For example,
research efforts to build the capacity of both government
and civil society stakeholders in a policy process are likely
to be influenced by an emerging conflict between these two
stakeholder groups. Another feature of boundary arrange-
ments at research–stakeholder interfaces is that they are
path-dependent, meaning that their feasibility or credibil-
ity at a particular point in time is influenced by earlier
boundary arrangements between research and stake-
holders. For example, claiming an independent position
as a researcher may not be credible in a multistakeholder
process in which the government has determined the
research agenda and how the research was conducted.
The analysis of boundary arrangements at multiple
research–stakeholder interfaces provides a more realistic
image of how multistakeholder dynamics affect the contri-
bution of research to policy and innovation processes in
competing claims contexts.
2.5 Contested research(ers): New roles for research
and researchers in policy and innovation processes
Working in an embedded and process-oriented way on
different dimensions of competing claims problems,
Table 2. Boundary arrangements at the research–stakeholder interface (Descriptions are inspired by Burton 2006; Michaels 2009; Edelenbos et al.
2011. An adapted version of this table has been published in Schut et al. 2013b.)
Boundary arrangement Description
Independent research Research is independent of stakeholder or political interests. Research is not concerned with how research findings are
mobilised and used by stakeholders in policy and innovation processes
Research steers stakeholders Research actively seeks to persuade stakeholders to select a specific solution for problem or a certain way of
organising policy or innovation process
Informative relationship Dissemination of information on e.g. policy content and process. Research and stakeholders inform one another in a
supply-oriented fashion
Advisory relationship Research and stakeholders operate in their own separate domains, but research provides advice to stakeholders, and
stakeholders can advise research about relevance of research questions
Exchange relationship Research acknowledges that stakeholders have specific needs and questions, and proactively seeks to reconcile
demand and supply. Research and stakeholders interact on research demands and exchange information
Co-learning relationship Co-production of research. Researchers and stakeholders engage in a joint learning relationship to produce stake-
holder-relevant research. Research and stakeholders seek to complement each other
Capacity building relationship Research builds capacity and seeks to strengthen position and capacity/skills of stakeholders in policy and innovation
process. Stakeholders can also empower research by providing research with a platform to mobilise research
findings
Selective use of research Research is used opportunistically, selectively and strategically by stakeholders to defend their interests and pursue
their goals. Research has little influence on how findings are interpreted, mobilised and used by stakeholders
Stakeholders steer research Stakeholders influence and determine research agenda setting, how research is conducted and/or used. Degree to
which researchers can participate in, or contribute to, policy and innovation process is controlled by stakeholders
Towards dynamic research configurations
.
211
across multiple levels and with a variety of actor and stake-
holder groups has implications for the role of research and
researchers in policy and innovation processes. As Section
2.4 showed, research can engage in multiple relationships
(boundary arrangements) with different stakeholder
groups. Research that aligns with stakeholders’ objectives
and perceptions is likely to be framed as credible, legitim-
ate and relevant, whereas research that does not align with
stakeholders’ objectives is likely to be framed as less
credible, legitimate and relevant (Cash et al. 2003). As
actors and stakeholders in competing claims contexts
often have conflicting objectives and needs, there is a
high probability that research becomes contested. Hence,
a key challenge becomes to explore what (combination of)
roles can enhance the credibility, legitimacy and relevance
of research for different groups of actors and stakeholders.
Researchers can fulfil a variety of roles to support the
policy and innovation process (Pohl et al. 2010). To cat-
egorise different researchers’ roles we distinguish between
knowledge management and innovation management roles
(see Table 3) (cf. Schut et al. 2011). Knowledge manage-
ment roles include the more traditional knowledge produc-
tion roles related to the production of new knowledge and
insights, and the analysis of existing knowledge and
insights. Knowledge brokering strategies require a willing-
ness between researchers and stakeholders to collaborate
or interact. Knowledge brokering
2
refers to different types
of activities that can enhance the accessibility and meaning
of research for actor groups (Turnhout et al. 2013).
Knowledge brokerage strategies and activities can vary
from the transfer and dissemination of research findings
among different actor groups, to facilitating joint know-
ledge production and learning with or among involved
actors and stakeholders. Based on their action-research
to support the policy debate on biofuel sustainability in
Mozambique, Schut et al. (2011) concluded that the joint
interpretation of research data with different stakeholder
groups enhanced the credibility, legitimacy and relevance
of the research findings for the different groups,
strengthened the relationship between the researchers and
the stakeholders, and improved the overall quality of the
data analysis.
The accessibility and usability of research in policy and
innovation processes is highly influenced by the way in
which research is packaged. There are different ways of
packaging research that are more or less useful for different
stakeholder groups. Traditional types of packaging of
research include reports, scientific research papers, tables,
charts or models. More innovative packaging can be in the
form of games or interactive scenario planning tools.
Strategic packaging of research can enhance the credibility,
legitimacy and relevance of research in policy and
innovation processes. It can bring together and create
shared understanding between researchers and different
groups of actors. If research outcomes or products have
this ability they are often referred to as ‘boundary objects’
(cf. Star and Griesemer 1989; Carlile 2002; Turnhout 2009).
Knowledge management is important, but several other
roles and activities—such as ensuring the availability of
financial resources or creating an enabling institutional
or political environment for problem solving—can be
just as decisive for the course and outcome of policy and
innovation processes. Innovation management acknow-
ledges that the contribution of research to policy and
innovation processes is to a large extent determined by
institutional and political factors (Klerkx et al. 2012).
Table 3. Examples of knowledge management roles for researchers (based on: Edelenbos et al. 2003; van Buuren et al. 2004; Michaels 2009) and
innovation management roles for researchers (based on: Swan et al. 1999; Hall et al. 2003; Hall and Clark 2010; Klerkx et al. 2010; van Mierlo et al.
2010; Leeuwis and Aarts 2011)
Knowledge management roles Innovation management roles
Knowledge production: generate and mobilise new and existing
knowledge and insights
Manage boundary arrangements at multiple research–stakeholder interfaces
Develop adaptive capacity in policy and innovation processes
Develop enabling environment to facilitate continuous stakeholder learning,
e.g. fundraising, lobbying or criticising political agendas
Address institutional constraints and structural power asymmetries
Enhance reflexive monitoring and evaluation, and strategic adjustment
of policy and innovation process
Knowledge brokerage:
. Inform: transfer and disseminate content
. Consult: mobilise and provide expertise
. Matchmake: connect experts and actor/stakeholder groups
. Engage: involve stakeholders in, e.g. policy debates
. Collaborate: facilitate collaboration at multiple
stakeholder–stakeholder interfaces
. Capacity building: develop process architecture
and joint knowledge production and learning
Knowledge packaging:
. Enhance accessibility of research for different stakeholder
groups
. Develop boundary objects
212
.
M. Schut et al.
Consequently, knowledge and innovation management
roles are not mutually exclusive, but rather mutually
reinforcing, and sometimes even inextricably bound
together. Building upon the above example of action-
research in Mozambique, knowledge management—the
joint interpretation of research data with different groups
of stakeholders—can build a degree of trust between the
researcher and stakeholders. This can lead to a more
embedded position of the researcher in the policy process
(see Section 2.1) which can provide the basis for engaging in
innovation management roles such as lobbying, penetrating
political agendas, fundraising and addressing structural
power asymmetries in policy processes. Consequently,
such innovation management roles and activities can facili-
tate more effective knowledge management (e.g. mobilising
funds for multi-actor interaction and learning); showing
how the combination of knowledge and innovation man-
agement roles can enhance the contribution of research to
policy and innovation processes.
Depending on the types of boundary arrangements at
multiple research–stakeholder interfaces, certain know-
ledge management or innovation management roles may
be more or less appropriate for the researcher to fulfil.
Researchers do not necessarily have to fulfil or master
the multiplicity of knowledge and innovation management
roles. The majority of knowledge management and innov-
ation management roles can also be fulfilled by specialised
intermediaries, or knowledge/innovation brokers (Klerkx
et al. 2009; Michaels 2009). The objective here is to create
awareness on the importance of fulfilling specific know-
ledge management and innovation management roles and
the strength of combining different roles in policy and
innovation processes.
3. A framework for dynamic research
configurations
To support researchers in their reflection, we combine our
findings and recommendations in a framework for
‘dynamic research configurations’ (see Fig. 2). The frame-
work aims to increase awareness about the complexity of
research in policy and innovation processes in competing
claims contexts, and to support individual researchers,
groups of researchers or research projects in reflection on
their contribution to, and role in, policy and innovation
processes in competing claims contexts. It is not a heuristic
framework in the way that it tells the user: what to do next
and how to do it. Nevertheless, the framework can support
researchers in better understanding the complex dynamics
that influence interactions between research processes and
policy or innovation processes, and reflecting on this.
Potentially, the framework can be used to design or
revise research strategies to enhance the contribution of
research to policy and innovation processes.
The framework comprises of several layers (phases, di-
mensions, scales and levels, stakeholders, boundary ar-
rangements and roles for research(ers)) that relate to the
key challenges of competing claims contexts identified in
this paper, and the theoretical, conceptual and methodo-
logical approaches that can support researchers in address-
ing these challenges. Some of the layers contain of
sublayers such as the spatial and administrative scales,
and knowledge management and innovation management.
Each layer consists of different categories or levels, for
example the different spatial or administrative levels,
different stakeholder groups, or different types of
boundary arrangements. Furthermore, the categories or
levels contain ‘tick boxes’ that can be turned either ‘on’
or ‘off’ as they are not always applicable.
3
The slide rule running vertically down through the dif-
ferent layers indicates the focus of the policy, innovation
or research process at a particular moment in time. Each
horizontal layer is movable to the right or to the left in
relation to the position of the slide rule. The upper part of
the framework includes the policy or innovation process
considerations, e.g. what does the policy/innovation
process look like in terms of the phase, the dimensions it
addresses, the level(s) at which the problem is framed and
is being addressed, and the actors and stakeholders
involved. The lower part of the framework includes the
research process considerations that include the type of
research activities and roles. In Fig. 2, boundary arrange-
ments form the dividing line of policy/innovation process
and research process considerations: they visualise the re-
lationship and division of tasks and responsibilities
between research and stakeholders in policy and
innovation processes. However, this line is not static (as
indicated by the arrows) as decisions on which dimensions
to address in research, and decisions on the inclusion and
exclusion of scales and levels as part of scale dynamics
analysis are just as much part of research process con-
siderations as they are of policy/innovation process
considerations. Also the other way around, the type of
research(ers) roles, and the boundary arrangements
between research and stakeholders (e.g. capacity building
of farmers) are likely to affect the focus of the policy or
innovation process in terms of how problems are defined
and what type of solutions are being explored.
The first layer relates to the uncertainty and unpredict-
ability in competing claims contexts. Policy and innovation
processes in competing claims contexts consist of various
iterative problem- and solution-oriented phases.
Embedded research approaches can enhance insight into
how these phases evolve over time and improve the
allignment of phases in research processes, and in policy
and innovation processes. The second layer refers to the
bio-physical, social-cultural, economic, institutional and
political dimensions of competing claims problems.
Exploring, designing and implementing strategies to
address competing claims problems, requires integral
Towards dynamic research configurations
.
213
Figure 2. Framework for dynamic research configurations consisting of various (sub-)layers and levels/categories that visualise dynamic nature of research in a context of
interconnected policy/innovation and research process considerations.
214
.
M. Schut et al.
analysis of the different problem dimensions, for which an
interdisciplinary research approach is essential. The third
layer analyses the scale dynamics that can contribute to
creating awareness of interactions between scales and
levels, identify matches and mismatches, and provide the
basis for multi-actor collaboration across different levels.
The fourth layer addresses multi-actor processes and
boundary arrangements at the multiple research-
stakeholder interfaces. Policy and innovation processes in
competing claims contexts are characterised by the in-
volvement of multiple actor and stakeholder groups,
whose interests and mutual relations (stakeholder–stake-
holder interfaces) evolve and change over time. Research
is likely to engage in multiple boundary arrangements with
different groups of stakeholders. The last layer concerns
the variety of roles for research and researchers in policy
and innovation processes in competing claims contexts.
The combination of knowledge and innovation manage-
ment roles can enhance the contribution of research to
policy and innovation processes. Table 4 includes key
questions related to the above-described layers that can
support researchers in their reflection.
The researchers who use the framework should keep in
mind that within each of the layers, different categories or
levels can be selected. For example, the research(ers) can
simultaneously collaborate with different groups of actors
or stakeholders such as farmers, politicians and develop-
ment workers. The vertical slide rule indicates a focus, not
that all other categories and levels are excluded or
irrelevant. Moreover, changes in one of the layers are
likely to imply changes at other layers. For example, if the
focus changes from addressing food security at national
level to the village level, different questions regarding the
biophysical, social-cultural, economic, institutional and
Table 4. Key questions related to using framework for dynamic research configurations
Layers Key questions
1. Uncertainty and
unpredictability
. In what phase is the policy or innovation process?
. What has happened in previous phases?
. Who was involved in agenda setting?
. To what phase in the process does research seek to contribute?
. How can research process and policy/innovation process be aligned to prevent mismatches and optimise timing
of research findings?
2. Multidimensional . What are the biophysical, social-cultural, economic, institutional and political dimensions of the problem?
. What existing research is available on the different dimensions of the problem?
. How do different dimensions relate?
. How can insights from different disciplines be combined?
. What dimensions can be addressed by researcher/research team?
. What type of expertise is missing in the research team and how can this expertise be mobilised?
. What conditions for interdisciplinary research have to be put in place?
3. Scale dynamics . At what spatial level(s) does the problem occur?
. At what administrative level(s) is the problem being addressed?
. What are potential consequences of scale and level selection in terms of inclusion and exclusion of stakeholders
in the process?
. How do interactions across different scales and levels influence each other?
. What scale and level matches and mismatches can be identified and how can these be dealt with?
. What type of research has relevance to decision-makers at different levels?
4. Multi-actor
processes
. Which actors have a stake in the policy/innovation process?
. What are objectives and needs of different stakeholder groups?
. How is research mobilised and used by different stakeholder groups?
. Are stakeholders willing to collaborate in addressing the problem?
. What are ideas of stakeholders about division of tasks and responsibilities?
. What boundary arrangements at multiple research–stakeholder interfaces can coexist?
. How do dynamics at stakeholder–stakeholder interfaces influence the boundary arrangements at different
research–stakeholder interfaces?
. How have boundary arrangements at multiple research–stakeholder interfaces evolved over time?
. What type of path-dependency has emerged in relation to credibility of different boundary arrangements?
5. (New) roles for
research(ers)
. What roles is/are the researcher(s) willing to play?
. What roles is/are the researcher(s) expected to play by actors and stakeholders?
. How do such expectations vary across different groups of actors and stakeholders?
. What combinations of knowledge management and innovation management roles can enhance contribution of
research to policy/innovation process?
. Is there sufficient trust or mandate for researcher to fulfil innovation management roles
. What type of packaging can enhance accessibility and meaning of the research for different actors?
Towards dynamic research configurations
.
215
political dimensions of the problem will be posed, different
stakeholders need to be involved, and different types of
roles may need to be fulfilled by the researcher(s). In sum,
the changing policy or innovation context is likely to require
a different research configuration.
As is often the case when a dynamic framework is pre-
sented on paper, a number challenges need to be discussed.
First, the framework suggests that different configurations
can be randomly and unlimitedly selected. In line with our
earlier findings on boundary arrangements, research con-
figurations are stored in the ‘memory’ of involved actors
and stakeholders and consequently show patterns of path
dependency. This implies that the credibility, legitimacy
and relevance of research configurations as perceived by
actors and stakeholders at a particular point in time are
influenced by earlier research configurations. Secondly, at
what time intervals can or should the framework be used?
In particular, the boundary arrangements between
research and stakeholders are never fixed but are con-
stantly negotiated and renegotiated in interactions as the
pollicy or innovation process evolves. Therefore, there is
no rule or norm on how often the framework can or
should be used. As stated, the main objective is to
support reflection on the key characteristics of competing
claims contexts, how they relate, and how they influence
the credibility, legitimacy and relevance of research in
policy and innovation processes in competing claims
contexts.
4. Conclusions and recommendations for
further research
One of the objectives of this paper was to better under-
stand the characteristics of competing claims contexts and
how they influence the contribution of research to policy
and innovation processes addressing in competing claims
problems. Essential are awareness about: (1) uncertainty
and unpredictability in competing claims contexts; (2) a
better understanding of the different dimensions of
competing claims problems; (3) interactions between dif-
ferent scales and levels; (4) multi-actor processes; and (5)
the (new) roles for research and researchers. Rather than
promoting Mode 1, Mode 2 or proposing a ‘Mode 3’ type
of research, we conclude that more dynamic approaches to
enhance the contribution of research to policy and
innovation processes in competing claims contexts are
needed. Within such approaches there is space for ‘trad-
itional’ and ‘new’ forms of research, and the various blends
and combinations between them. Embedded, context-sen-
sitive and flexible research strategies can support re-
searchers to strategically and continuously reflect on
when, how, and in what form or role the contribution to,
and credibility, legitimacy and relevance of research in,
policy and innovation processes can be enhanced. The
framework for dynamic research configurations can
support researchers in their reflection. However, the frame-
work also poses challenges, which are related to how the
framework can be used in practice. First, there is a chal-
lenge with regard to making choices, and who determines
the research configuration. Making choices implies a
degree of power. This can be power in relation to what
research questions are formulated, which approach is
found most appropriate, and which actors and stake-
holders are perceived to be legitimate participants in the
policy and innovation arena. This makes the inclusion or
exclusion of certain dimensions (e.g. the economic dimen-
sion), or scales and levels (e.g. study food security at village
level or at national level) complex and political (cf. Lebel
2006). Hence, strategies or tools that can facilitate making
such choices in multi-actor policy and innovation
processes are needed.
Secondly, there are challenges related to the changing
boundary arrangements at different stakeholder–stake-
holder and research–stakeholder interfaces. As policy
and innovation processes unfold, it is likely that different
boundary arrangements at these interfaces will evolve. In
this paper we have mainly reflected on (changing)
boundary arrangements at multiple research–stakeholder
interfaces. We believe that the combined analysis of inter-
actions at multiple stakeholder–stakeholder interfaces and
at multiple research–stakeholder interfaces through time
can provide additional insights into factors that influence
the contribution of research to multi-actor policy and
innovation processes.
Thirdly, research and researchers face an institutional
challenge. In competing claims contexts, embedded and
context-sensitive research strategies seem most effective.
Such research approaches require flexibility in terms of
adapting research questions, scales and levels of analysis,
working together with different groups of actors and stake-
holders and fulfilling different types of roles. Funding re-
quirements and incentive structures often stimulate
research to be ‘predefined projects’ rather than dynamic
‘processes of inquiry’.
Funding
This work was supported by the strategic research pro-
gramme ‘Sustainable spatial development of ecosystems,
landscapes, seas and regions’ funded by the Dutch
Ministry of Economic Affairs. It also contributed to
the Competing Claims on Natural Resources programme,
funded by the Interdisciplinary Research and Education
Fund of Wageningen University, the Netherlands.
Findings also form part of the PARASITE programme
(<www.parasite-project.org>), funded through the
Integrated Programme Scheme of the Netherlands
Organisation for Scientific Research – Science for Global
Development (NWO-WOTRO).
216
.
M. Schut et al.
Acknowledgement
The authors are appreciative of the support of Joris
Ketelaar (<www.14over.nl>) in developing and designing
Fig. 2 in this paper.
Notes
1. Although we acknowledge that researchers can also
have a stake in policy or innovation processes, we do
not treat them as stakeholders in this paper.
2. The knowledge brokerage roles as described in Table 3
are based on work by Michaels (2009). Michaels de-
scribes knowledge brokerage as a role that is fulfilled
by independent intermediaries while we think they can
equally be described as part of researchers’ roles in
policy processes.
3. For reasons of illustration, the categories and levels in
Fig. 2 have been randomly selected as ‘on’ or ‘off’.
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