Stakeholder analysis for better policymaking: A case of plug-in cars in the UK

Introduction

Public policymakers often have to balance the interests of many different stakeholders, that is, groups of people who can affect or are affected by the achievement of policy goals. The recent promotion of uptake of plug-in cars in the UK is no exception. This research aims to perform stakeholder analysis of recent British government policies to transition to plug-in cars in the UK. It seeks a format to identify and profile stakeholder groups and to quantitatively evaluate policy impact on different stakeholder groups.

Methods and results

The multi-criteria and multi-stakeholder nature of the focal policy issue has defined two task groups of data collection and analysis. The first focus is on building a system model, which defines the policy context, key variables, and the cause-effect relations between them. The other is a focus on stakeholder identification and elicitation of their preferences. First, I conducted background research, collecting over 30 documents and performing qualitative content analysis. From the content analysis I built a qualitative system model, represented as a fuzzy cognitive map. Further, the policy of subsidy for purchases of plug-in cars in the UK was simulated in the model, thus outputting the policy performance values. The background research also revealed an initial set of stakeholders as important actors. The set was further cross-validated and significantly extended by applying a soft-system methodology and the value network analysis. To elicit preferences I employed either of two strategies: i) deriving preferences from the document content analysis and value network analysis; ii) deriving appropriate representatives for stakeholder groups, who were asked to use the cardinal ranking method to rank variables of interest (criteria). Finally, the policy impact was aggregated as a weighted sum of the performance values and (criteria) importance weights for variables unique to each stakeholder group, the aggregation being performed for negative and positive impact separately, as well as both for each stakeholder group and for each variable (criterion).

Conclusions

This research is built upon mixed methods to couple system modeling of a policy issue with stakeholder preferences regarding the outcomes of the modeled scenarios. This provides a comprehensive and balanced view of the stakeholders being positively/negatively affected by a focal policy.

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Please note these Proceedings have received limited or no review from supervisors and IIASA program directors, and the views and results expressed therein do not necessarily represent IIASA, its National Member Organizations, or other organizations supporting the work.

Last edited: 08 February 2016