1. Measuring Performance in the Voluntary and Community Sector Dr Claire Moxham Manchester Business School c.moxham@mbs.ac.uk

2. Objectives of the Research Project How can the performance of voluntary and community organisations (VCOs) be measured to the satisfaction of the stakeholders? 1.What are the drivers for measuring voluntary sector performance? (Why?) 2.What information do stakeholders require? (What?) 3.What performance measurement frameworks are used? (How?)

3. Overview of the Findings Why measure? VCOs are required to demonstrate accountability for spend in order to sustain trust and confidence. Public sector accountability is a key driver for VCO performance measurement What is being measured? Funders are focusing on measuring outputs rather than outcomes and impact Larger VCOs have more capacity for measuring performance than smaller VCOs and appear able to negotiate strategic long-term funding arrangements with fewer funders and fewer criteria

4. Overview of the Findings How is performance measured? A range of performance measurement models have been developed which are not used in the voluntary sector As VCOs have diverse funding arrangements a ‘one size fits all’ approach is ineffectual Findings disseminated to all interviewees via an executive summary. 3 conference papers and 1 academic journal paper published, 2 academic journal papers in progress

5. Overview of the Methodology Developed an understanding of the context through: –Review of literature (academic and practitioner) –Pilot study (4 VCOs in Manchester) Developed conceptual model from the literature Developed research question

6. Overview of Methodology Embarked upon a multiple case study Selected 6 VCOs (and their associated funders) using a ‘matched pair approach’ Semi-structured interviews that were either tape recorded and transcribed or recorded using contemporaneous notes. Follow-up discussions over telephone or e-mail 18 organisations took part in the study

7. Overview of the Methodology Flow-charts developed to present the ‘story’ of each case study The wealth of qualitative data was then pattern coded to group data into a smaller number of key themes and trends Matrices were used to record the key themes from each case study and to detail the supporting evidence Comparisons were made within and across the matched pairs in order to provide answers to the research questions