The SROI puzzle: exploring barriers and strategies for effective social value measurement
Bibliographic record
Abstract
Purpose The study aims to explore the complexities and challenges of measuring social impact, with a particular emphasis on the practical application of the social return on investment (SROI) methodology. By investigating social enterprises in Georgia, the study seeks to understand how social value is quantified and the implications of such measurements for policy and practice. Design/methodology/approach This study uses a mixed-methods approach, centered on the SROI methodology, to measure the social impact of 11 social enterprises in the Republic of Georgia. It incorporates qualitative interviews and quantitative financial analysis, engaging stakeholders from enterprises, beneficiaries and local authorities. The methodology adapts SROI to the Georgian context, addressing challenges such as data scarcity and cultural sensitivity. Findings Findings reveal significant challenges in social impact measurement, including the complexity of quantifying diverse impacts, lack of standardized methodologies and resource constraints. The application of SROI in Georgia demonstrates its flexibility and the critical role of stakeholder engagement, yet underscores the need for context-specific adaptations and rigorous data collection. Research limitations/implications The study is limited by its geographic focus on Georgia, which may affect the generalizability of findings. In addition, the reliance on stakeholder-reported data introduces potential biases. These limitations highlight the necessity for broader, cross-cultural studies and methodological advancements in social impact measurement. Practical implications The study offers practical insights for organizations implementing SROI, emphasizing the importance of stakeholder engagement, cultural sensitivity and adapting methodologies to local contexts. It suggests strategies for overcoming data limitations and enhancing the credibility of social impact assessments. Social implications The research underscores the transformative potential of social enterprises in addressing societal challenges. By quantifying social impact, organizations can better articulate their contributions to societal well-being, informing policy decisions and fostering a culture that values social over mere economic returns. Originality/value This study contributes to the literature on social impact measurement by detailing the application of SROI in a novel context – Georgia. It addresses a significant gap in understanding how social impact can be measured in transitional economies and offers valuable insights into the methodological and practical challenges involved.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.336 | 0.436 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.010 | 0.046 |
| Scholarly communication | 0.025 | 0.039 |
| Open science | 0.009 | 0.032 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".