Assessing Research Use in Policy and Decision-Making Contexts: The Importance of Models

The relationships between research and policy and research and practice have been theorized in different ways. Several models have been conceptualized over time, which provide different frameworks for thinking about and understanding the research use process (Nutley, Walter, & Davies, 2007a). These models take several complexities into consideration that forms of research use can take and highlight some of the assumptions underpinning different studies on the subject. They also trigger the desire to explore ways in which one might improve the use of research. Research use models typically focus on the relationships between research and macro level policy or the relationships between research and front-line practice (Nutley et al., 2007a). These models can be best framed by the ways in which the policy process itself is conceptualized and the role that research plays in policy making. The models of the policy process can help in understanding the interconnections between policy and information use (Nutley et al., 2007a).

The early models emphasized a one-way relationship between research and policy. These models have been heavily criticized and have been replaced through growing acknowledgement of the complexity of research information use. These models have been rejected in favor of more interactive models that involve diverse stakeholders. The shift in the opinion that research use is not a simple transfer of knowledge to passive recipients has also contributed to the evolution of new models. At present, both policy makers and practitioners are involved in research about models and information use. They offer their own perspectives and experiences, and the specific contexts of their information use (Nutley et al., 2007a).

The traditional (rational and linear) model of policy making involves four stages: a) problem identification and agenda setting; b) decision making; c) policy implementation; and d) monitoring and evaluation. This model considers the policy making process as a relatively simple cycle. Though this model is considered as a heuristic device, “an ideal type from which every reality will curve away,” it is highly criticized on the grounds of ignoring the complexities in the policy process and representing an extremely simplistic and linear view of the process (Nutley et al., 2007a). Other rational models also represent the linear view of the policy process and are aligned with models of research use as a knowledge-driven or problem-solving process, proposed by Weiss in 1979. Critiques of these models argue that human rationality is bounded and, therefore, there is doubt whether policy makers do or can act in solely a rational way. Policy makers also often lack information, resources, and the time required to carry out any comprehensive research and as a result they will focus on solutions to problems that are good enough (Nutley et al., 2007a).

The two communities model is based on the premise that policy/decision makers seem to rarely use research, and this creates an issue of communication between researchers and policy makers who function in separate worlds. Therefore, it is very important to enhance communication between these two communities so that the research-policy gap can be bridged. The two-communities thesis promotes the idea of greater interaction between researchers and policy makers so that better decisions can be made, and the quality of the research can also be enhanced through knowledge sharing. This model also requires different forms of knowledge sharing for different types of research or policy decisions. However, there are some limitations to this approach as well. Interactions between researchers and decision makers are typically not straightforward and the model ignores the diversity among the two groups and other stakeholders. The end goals of the researchers and policy makers may also differ, which can lead to confusion or conflict. In order to overcome the drawbacks of two-communities thesis, other theories like the “General Utilization Theory” by Wingens and the “Linkage and Exchange” model by CHSRF were developed. These latter theories have their roots in the two-communities model and they place a stronger focus on instrumental rather than conceptual uses of research.

The policy network approach is a pluralist model that suggests that policy/decision making should be decentralized. This approach recognizes the role of policy networks in accounting for how research and policy interconnect. Policy communities, advocacy coalitions, epistemic communities, and issue networks are four network approaches that have been theorized under this model and they differ in terms of the level of integration and basis of their membership.

The context, evidence, and links model, proposed by Crewe and Young in 2002, integrates a wide range of theories and already established frameworks for exploring research-policy relationships. They argued that policy making is structured by a complex interaction between political interests, competing discourses, and the agency of multiple actors (Nutley et al., 2007a). This model has its roots in the previous established frameworks and it integrates them in a more sophisticated manner to overcome the drawbacks of older models. It depicts the “use of research as a dynamic, complex, and mediated process, which is shaped by formal and informal structures, by multiple actors and bodies of knowledge, and by the relationships and play of politics and power that run through the wider policy context” (Nutley et al., 2007a, p. 111). This model emphasizes the importance of interactions between various stakeholders in the policy process to overcome the complexities and the communication gap (Nutley et al., 2007a).

With the increasing involvement and contribution of different stakeholders in the research-policy process, it has become difficult to integrate different types and sources of knowledge (scientific, managerial, indigenous, etc.). The inclusion of indigenous knowledge in science/research is an example. It is extremely important that the knowledge contributions from different sources are recognized and included. The concept of a knowledge system may help in identifying knowledge relations. Three things that must be considered when designing the framework for knowledge system are: a) how knowledge systems may be conceptualized; b) the way in which different studies frame the types of knowledge system dynamics to be investigated; and c) how the relationships between different knowledge systems are addressed (Coffey & O’Toole, 2012). Knowledge systems can be evaluated using four approaches: stakeholder analysis, network analysis, institutional analysis, and discourse analysis (Coffey & O’Toole, 2012).

In order to integrate the perspective and knowledge of various stakeholders (researchers, decision/policy makers, funders, citizens, etc.), interactive models of research use can also be considered as the best available to describe research-policy relationships. These models bring together the best knowledge about the kind of factors that support the use of research and at the same time they promote mutual exchange between researchers and decision makers. They highlight the impact of a complex range of contextual factors such as cultural, organizational, personal, and political, in deciding the use, effectiveness, and efficiency of research. However, postmodern theories critique the integrative models by arguing that they are affected by power relations within the research use process and they fail to take account of local knowledge fully (e.g., knowledge from indigenous communities).

Based on the above discussion, we can conclude that the models of the research-policy relationship have developed over time and, as Nutley et al. (2007a) mention, these developments are shifts in the ways in which research knowledge itself is conceptualised. Though we can see some similarities in the models, it is crucial to distinguish the ways in which they differ. The strategies that need to be developed in order to enhance the relationship between research and policy depends on the ways in which research-policy relationships are framed and understood (Nutley et al., 2007a).

Given the complexities, diversity, and messiness of research use, it is important to identify how one can integrate more sophisticated understanding of research use into better studies of research impact (Nutley et al., 2007b). We need to know why and how research findings are influential, are used, and have non-academic impact. The two main drivers of this non-academic impact assessment are political and practical. Nutley et al. (2007b) identify four purposes for carrying out research impact assessment: a) accountability, b) value for money, c) learning, and d) auditing evidence-based policy and practice. It is important to understand how one can infer reasonable research policy implications from impact assessment and the resource implications of conducting impact assessment.

The interaction and relationship between science/research and policy have increased over the years. The role of information use in the policy making is very important. With the help of various concepts and tools, the roles of information can be measured in the context of awareness, use, and influence, from the perspective of information management (​Soomai, Wells, MacDonald, de Santo, & Gruzd, 2016). In order to determine the effectiveness of science-policy interfaces (SPIs), researchers and scholars have proposed several criteria, among which are factors of the usability of information, namely, credibility, relevance, and legitimacy (CRELE). In the context of SPIs, effectiveness can be described as the ability to influence the behavior of intended audiences by enhancing their knowledge of the consequences of their decision (Sarkki et al., 2014). Credibility can be described as “scientific and technical believability to a defined user (of an assessment), often in the scientific community” (Farrell et al., 2006). In the same context, research findings can be considered relevant to policy if they can logically be considered in making the policy (Wilson 2009). Legitimacy can be defined as the condition of being in accord with established principles (Rantala, 2008). These three concepts are often complex and multidimensional. The interpretation of CRELE plays an important role in determining the effectiveness of a policy. Variations in the underlying conception of CRELE result in differences in the perception of effectiveness, which make it difficult to apply CRELE as criteria when evaluating the interfaces (Heink, et al., 2015). In order to be used and applied in a meaningful way, CRELE must be defined specifically for the particular context of an SPI (Heink, et al., 2015). Conceptual analysis can help in the construction of idealized CRELE attributes. This analysis can be divided into two steps. The first involves identification and verification of the characteristics of the CRELE concepts in the context of SPIs and analysis of variations in the definitions and applications. The next step is concept clarification where different and inconsistent meaning of CRELE is evaluated and then the different contexts in which particular uses prevail are highlighted (Heink, et al., 2015). This approach will result in the construction of idealized CRELE characteristics, which can be used in knowledge production and decision making.

Based on the above discussion we can conclude that in order to assess research use in policy and decision making, it is important to assess its impact in non-academic settings. Impacts can be evaluated based on the different criteria mentioned above. However, it is very important that these criteria be well defined in the context of policy processes and information use. Clear understanding of these parameters is crucial.



Coffey, B., & O’Toole, K. (2012). Towards an improved understanding of knowledge dynamics in integrated coastal zone management: A knowledge systems framework. Conservation and Society, 10(4), 318-329. doi:10.4103/0972-4923.105513

Farrell, A. E., & Jäger, J. (Eds.). (2006). Assessments of regional and global environmental risks. Designing processes for the effective use of science in decision making. Washington, DC: Resources for the Future.

Heink, U., Marquard, E., Heubach, K., Jax, K., Kugel, C., Nesshöver, C., & Vandewall, M. (2015). Conceptualizing credibility, relevance, and legitimacy for evaluating the effectiveness of science-policy interfaces: Challenges and opportunities. Science and Public Policy, 42, 676- 689.

Nutley, S. M., Walter, I., & Davies, H. T. O. (2007a). Using evidence:How research can inform public services (Chapter 4, pp. 91-123). Bristol, UK: The Policy Press.

Nutley, S. M., Walter, I., & Davies, H. T. O. (2007b). Using evidence:How research can inform public services (Chapter 9, pp. 271-294). Bristol, UK: The Policy Press.

Rantala, T. (2008). Discourse on legitimacy of forest and nature conservation policy in Finnish print media: Framework for analysis and revised principles of democratic legitimacy. In M. Bocher, I. Giessen, & D. Kleinschmit (Eds.). Environmental and forest governance. The role of discourses and expertise. Proceedings of the International Conference, Göttingen 2007 (pp. 41–68). Göttingen: Universitätsverlag Göttingen.

Sarkki, S., Niemela, J., Tinch, R., van den Hove, S., Watt, A. & Young, J. C. (2014). Balancing credibility, relevance, and legitimacy: A critical assessment of trade-offs in science–policy interfaces. Science and Public Policy, 41, 194–206.

Soomai, S. S., Wells, P. G., MacDonald, B. H., De Santo, E. M., & Gruzd, A. (2016). Measuring awareness, use, and influence of information: Where theory meets practice. In B. H. MacDonald, S. S. Soomai, E. M. De Santo, & P. W. Wells (Eds.). Science, information, and policy interface for effective coastal and ocean management (pp. 253-279). Boca Raton, FL: CRC Press.

Wilson, D. C. (2009). The paradoxes of transparency: Science and the ecosystem approach to fisheries management in Europe. Amsterdam: Amsterdam University Press.


Authors: Yashvi Pathak and Rita Ugbebor


This blog post is part of a series of posts authored by students in the graduate course “The Role of Information in Public Policy and Decision Making” offered at Dalhousie University.


Please follow and like us: