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What’s being tested and what’s being learnt? A contribution to lessons learned evaluation methods for community-based sustainability initiatives

Authors :
Mitchell, Andrew
Lemon, Mark
Publication Year :
2018
Publisher :
Central European Review of Economics and Management, 2018.

Abstract

What’s being tested and what’s being learnt? A contribution to lessons learned evaluation methods for community-based sustainability initiatives. Abstract: Aim: There is little good practice guidance with respect to methods and skills for conducting lessons learned evaluations of community-based development projects. In this paper we utilise a mixed methods approach to evaluate the lessons learned by the team members and stakeholders of a funded five year ‘test-and-learn’ UK-based sustainability initiative. The approach combines a statistical and a qualitative thematic analysis of transcribed textual data and presents an analytic framework with which to track the lessons learned by community development projects. Design/ Research methods: A mixed methods approach combining text and sentiment mining complemented by a qualitative thematic analysis is applied to the same data collected from stakeholder responses to an on-line survey and the transcribed audio recordings of four focus groups in which stakeholders participated. Conclusions/ findings: Employing replicable tools, augmented by qualitative research methods, provide a framework for a systematic approach to elicit and capture lessons learned by a sustainable community development project. These bear on how project activities, from engagement to supporting the local food economy, have been experienced by stakeholders and their learning acquired over the course of the project. Implications for future project design and funding options are considered. Originality/ value of the article: Despite the evident value of its contribution to improving project design and funding options, the evaluation of lessons learned in community-based sustainability work remains under-researched. The novel combination of text and sentiment mining techniques with more traditional qualitative thematic analysis on the same data offers an original contribution to research in this field. JEL: R58, Q01, Z18

Details

Database :
OpenAIRE
Accession number :
edsair.dedup.wf.001..83c36885a5433edc4d55372c8ea17ea4