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Supporting Impact with Evidence: A Content Analysis of Project Completion Reports

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  • Bia Carneiro
  • Alessandra Garbero

Abstract

Project Completion Reports (PCRs) are used by development institutions to tell a project’s story – achievements, failures, and learning. As such, they should provide evidence of effectiveness in bringing about development. But is this the case? Descriptive content analysis was applied to assess the extent of evidentiary support presented in the International Fund for Agricultural Development's end of project documentation, employing a custom conceptual framework to classify claims about project results found in PCRs based on results level, tone, evidence sources, and themes. Findings show the majority of claims relate to output or outcome level results and are positive, though not explicitly supported by evidence. The lack of evidence-based reporting carries implications to the objective measurement of development effectiveness.

Suggested Citation

  • Bia Carneiro & Alessandra Garbero, 2018. "Supporting Impact with Evidence: A Content Analysis of Project Completion Reports," Journal of Development Studies, Taylor & Francis Journals, vol. 54(8), pages 1426-1449, August.
    • Handle: RePEc:taf:jdevst:v:54:y:2018:i:8:p:1426-1449
    DOI: 10.1080/00220388.2017.1324148
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    Cited by:

    1. Abas Mirzaei & Cynthia M. Webster & Helen Siuki, 2021. "Exploring brand purpose dimensions for non-profit organizations," Journal of Brand Management, Palgrave Macmillan, vol. 28(2), pages 186-198, March.
    2. Elena Feo & Pieter Spanoghe & Els Berckmoes & Elodie Pascal & Rosa Mosquera-Losada & Alexander Opdebeeck & Sylvia Burssens, 2022. "The multi-actor approach in thematic networks for agriculture and forestry innovation," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 10(1), pages 1-24, December.
    3. Garbero, Alessandra & Carneiro, Bia & Resce, Giuliano, 2021. "Harnessing the power of machine learning analytics to understand food systems dynamics across development projects," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    4. Primo, Marcos André Mendes & Paiva, Ely Laureano, 2024. "Tech hub entrepreneurial training to low-income communities: Evidence from Brazil," Technological Forecasting and Social Change, Elsevier, vol. 203(C).

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