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Goal Setting Example

What stakeholders can expect to get out of a project or programme is largely dependent on how well the issues underlying the research are defined and the degree of shared understanding of them that already exists. Funders, programme and project design teams, and researchers need to adjust their goal setting and stakeholder engagement and KE processes accordingly.

Example - Rural Economy and Land Use (Relu) Programme Deer Project (see also the Project Flexibility Case Study)

This project followed two case studies (one in Scotland and one in England). Within Scotland, deer management represents a commercially viable activity; in England, deer broadly represent a pest species. In Scotland there were many pre-existing structures and stakeholder groups in place which were relatively easy to access and work with. Within England, however, the same structures didn't exist and the project had to spend considerably more time locating stakeholders and drawing the community together. As a result, both case studies had significantly different contexts and approaches.

Adjusting Your Dialogue Process

Context is important in determining how narrowly knowledge needs should be specified. For exploratory work to scope a new issue, or where there is a need for fresh ideas and perspectives, or for research that challenges the established view, it may be appropriate to express the knowledge needs broadly: the initiative then lies with the research community to come up with specific proposals.

However, if the issue is well established and/or there are particular knowledge needs, the processes of dialogue between the research and user communities need to be effective at teasing them out and expressing them as specifically as possible. A commonly encountered problem, particularly for collaborative research, is that knowledge needs are expressed rather too broadly for the knowledge context due to inadequacies in the dialogue processes.

Help from Academic Literature

Research into the commissioning of evidence and advice for policy-making suggests that it's helpful to decipher the particular knowledge context you're working in. The definitions that follow may help you to recognise what sort of context you're working in and therefore how to manage expectations and adjust the goals and objectives you set. Although these are written to address the governmental policy context, they might just as well apply in other situations, such as with respect to business policy and strategy issues.

- Well structured problems: there is consensus/clarity about both the policy question and the relevant knowledge. There are clear cause and effect relationships that are agreed by the players. The questions to science are clear and the answers can be derived routinely and known with a high degree of certainty, so science has a clear and leading role to play in the policy decision-making process.

- Moderately structured problems: the policy questions are agreed but there is missing knowledge or uncertainty about the best way to answer them. However, through research and analysis, the cause and effect relationships can be established and the uncertainties resolved such that the relevant specialists can help the policy-makers converge on answers.

- Badly structured problems: there is agreement on the evidence but a divergence of views about what the policy issue is or what the goal should be. New scientific evidence tends to raise new questions and generate new possibilities, hence fuelling debate without helping that debate to move towards a consensus and a policy decision.

- Unstructured problems: there is disagreement about both the policy question and the potentially relevant system understanding. Cause and effect can only be seen in retrospect, once some form of consensus has emerged. There are conflicting and interwoven ‘facts’ and ‘values’, and a diversity of players tend to be involved. Scientific research is one voice policy-makers listen to as they negotiate different values and rival interests, diverse views and rival claims about whose knowledge counts, until consensus emerges or they arrive at a practical solution.

Further sources of information:

- Boaz, A., Coxhead, F., Gray, A., Grauberg, J., Jackson, P., Parker, M., Parsons, W. and Shaxson, L., 2008. Evidence-based Policy Making. Public Management and Policy Association.

- Hisschemöller, M. and Hoppé, R., 2001. Coping with Intractable Controversies: the Case for Problem Structuring in Policy Design and Analysis. In 'Knowledge, Power and Participation in Environmental Policy Analysis and Risk Assessment' (eds. M. Hisschemöller, R. Hoppé, W.N. Dunn, J.R. Ravetz), pp. 47-72. NewBrunswick: Transaction Publishers.

- Shaxson, L., 2008. Structuring Policy Problems for Plastics, the Environment and Human Health: Reflections from the UK. Philosophical Transactions of the Royal Society B, Vol. 364, No. 1526, pp. 2141-2151. 

- Snowden, D. and Boone, M., 2007. A Leader’s Guide to Decision-Making. Harvard Business Review November 2007. Reprint R0711C. Boston: Harvard University Press.