Research based information is important in decision and policy making, and problem management. Understanding how information is used offers an opportunity to improve these aspects of decision making and the inclusion of different networks. Models, framing, structure, and knowledge systems are all components that influence how research-based information is used and are explored below. Considering each of these components separately helps to identify how improvements can be made to processes, stakeholder involvement, and the framing of an issue – ultimately leading to a more comprehensive understanding of how information informs decision-making and where improvements can be made.
Overview of Models
Through various models and frameworks, the relationship between research and policy, and research and practice, can be conceptualized (Nutley, Walter & Davies, 2007). For the research-policy interface, models tend to focus on the relationship between research and policy making at the national level. Alternatively, research-practice models are theorized as a connection between research and front line practice (Nutley et al., 2007). Models provide a visual representation to start to understand and acknowledge how the use of research can be improved in policy and decision making. Nutley et al. (2007) describe a number of different models that present varying approaches to explain how research enters the decision-making process, along with how research is used and perceived.
Both research-to-policy and research-to-practice first can be understood through traditional and rational models. These types of models typically represent the policy-making process or research-to-practice process as a simplistic cycle or logical set of steps (Nutley et al., 2007). However, these earlier models have been criticized for disregarding the complexities that define policy making and the research use process (Nutley et al., 2007). Models that tend to remain one-dimensional and linear by nature fail to reflect policy and research realities along with ignoring various stakeholders.
To this end, it is understandable why traditional models have largely been discarded for more interactive and contemporary approaches of viewing the research-to-policy and research-to-practice relationships. Nutley et al. (2007) argue that research should not be seen in isolation from policy or practice. Later models expand on the interactivity between groups and disciplines. Further, the strength of interactive models show how research is transferable. These models illustrate how knowledge becomes negotiated, adapted, and reconstructed throughout the cycle. Moreover, research in policy and practice should be viewed through the wider political and organizational contexts (Nutley et al., 2007). Through improved communication the gaps that exist between researchers and policy makers can be closed. Nutley et al. (2007) explain how the use of research will be improved through more sophisticated models and frameworks that interpret the process as dynamic and multifaceted.
Factors Affecting Information Use
The framing of information can affect its use, particularly when uncertainty is involved. If information is framed positively (pointing out benefits), people are likely to give it more weight than if the same information is framed in a negative manner (highlighting the consequences) (Morton, Rabinovich, Marshall, & Bretschneider, 2011). If there is uncertainty about the results, the credibility of the information is further reduced if it is negatively framed; however, uncertainty is less likely to affect use in situations where information is framed positively.
The ways in which problems or issues are structured (i.e., the similarity or divergence in the ways stakeholders view a problem) determine the role that scientific information plays in addressing problems. Problem structure types include well-structured, moderately-structured, poorly-structured, and unstructured problems. For well-structured problems, values are alike and converging. The stakes are low for well-structured problems: with different parties sharing almost equal risks. As a result, in addressing this type of problem, policy-makers are faced with less disagreement amongst stakeholders. The application of scientific and technical information is more suited to this problem structure (Leith et al., 2014)
In moderately structured problems, the majority of stakeholders share similar values and reach agreed goals. There is still some relatively high certainty about the role of science in addressing the issues. Poorly structured problems are characterized by divergent values and stakes. A positive outcome to one group might be seen as negative by another group. In unstructured problems, divergent perspectives exist about what the issue actually is. Agreeing on common goals and achieving outcomes is difficult.
Leith et al. (2014) emphasize the role of boundary spanning in linking issues with stakeholders, in order to understand how roles and relationships affect the way issues are understood. The process of boundary spanning facilitates the sharing of information among various groups.
Numerous studies have found that information produced by researchers working in partnership with users is more likely to be used. However, Dilling and Lemos (2010) point out that the interaction between groups required in these situations rarely happens by default. Creating this interaction is a challenge that needs to be actively owned or championed by someone. This individual can be internal to one group (embedded capacity), an independent individual (knowledge broker) an organization (boundary organization) that bridges the groups, or a broad practice-level network including both producers and users (knowledge network) (Dilling & Lemos, 2010). Beyond ownership, Dilling and Lemos (2010) identify two other factors that facilitate interaction. The first is flexible research agendas, whereby researchers are allowed – and must be willing – to adjust research goals in response to interaction with research users. This scenario implicitly suggests that research (and funding) timelines should be longer to allow time for the interaction to be built and occur. The second factor is defining success in ways that encourage interaction, a marked difference from traditional academic success metrics that often focus on the number of academic publications produced by a researcher.
Knowledge systems offer another way to understand the importance of multiple stakeholders, their interactions, and the knowledge they bring to any issue. Understanding knowledge systems provides an epistemological view of actions and ideologies that inform different groups or stakeholders. These systems focus on the social and interpersonal aspects of networks and thus can be used as important tools for managing complex, multi-stakeholder problems such as coastal management. Dominant or more traditional ways of understanding knowledge systems often place science at the top of the hierarchy of ways of knowing. This approach fails to acknowledge the legitimacy of the public’s knowledge and may discourage participation in problem management (Coffey & O’Toole, 2012). Understanding that many legitimate stakeholders are involved in complex management projects allows room for different networks and knowledge systems to be involved in “knowledge generation, dissemination and uptake” (Coffey & O’Toole, 2012, p. 320). Understanding and utilizing knowledge systems in the management of an issue increases participation, builds legitimacy, and credibility within the stakeholders and community at large (Coffey & O’Toole, 2012).
Early models of research-to-policy and research-to-practice relationships were primarily rational and sequential. However, these models have been critiqued as being overly simplistic and dependent on a view of knowledge as discrete, unambiguous facts that can be straightforwardly applied to policy and practice (Nutley et al., 2007). Modern models such as the linkage-exchange model, instead, emphasize interaction between researchers and research users throughout the process of knowledge production, and recognize that knowledge is often negotiated and adapted during use (Nutley et al., 2007).
The framing and structuring of problems are important factors to explore in order to understand how information influences decision making. Furthermore, utilizing knowledge system frameworks provides insight into the epistemological roots behind stakeholders’ understanding and how these systems influence values and perceptions. Considering these systems as important pieces in decision making helps create a more comprehensive understanding of how information is created, disseminated, and used.
In sum, problem framing, the structure of problems, and stakeholder interactions are all factors that affect the use of information. Models and frameworks can provide a visual and procedural representation for improving the use of research in policy and decision making, ultimately leading to more comprehensive policies and management practices.
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.
Dilling, L., & Lemos, M. C. (2011). Creating usable science: Opportunities and constraints for climate knowledge use and their implications for science policy. Global Environmental Change, 21(2), 680-689.
Leith, P., O’Toole, K., Haward, M., Coffey, B., Rees, C., & Ogier, E. (2014). Analysis of operating environments: A diagnostic model for linking science, society, and policy for sustainability. Environmental Science & Policy, 39, 162-171.
Morton, T. A., Rabinovich, A., Marshall, D., & Bretschneider, P. (2011). The future that may (or may not) come: How framing changes responses to uncertainty in climate change communications. Global Environmental Change, 21(1), 103-109.
Nutley, S. M., Walter, I., & Davies, H. T. O. (2007). Using evidence. How research can inform public services. Bristol: The Policy Press.
Authors: Avery Masewich, Alice McVittie, Afolabi Opanubi, and Shane Warner
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.