The Influence of Organizational Learning on the Relationship between Dynamic Capability and Sustainability of Non-Governmental Organizations in Nairobi County
Bibliographic record
Abstract
The increasingly dynamic macro environment poses significant challenges to the sustainability of programs implemented by Non-Governmental Organizations (NGOs). This study investigates the moderating role of organizational learning (OL) on the relationship between dynamic capability (DC) and organizational sustainability (OS) among NGOs in Nairobi County, Kenya. Grounded in the Resource-Based View and Dynamic Capability Theory, the research addresses the vulnerability of NGOs to shifting funding patterns and socio-economic uncertainty. Using a descriptive survey design, data was collected from a sample of 85 NGOs drawn from a population of 547 through simple random sampling. Structured questionnaires targeted senior management to capture insights on strategic practices. Findings reveal a strong moderating effect of OL on the DC–OS relationship, indicating that NGOs fostering a learning culture and dynamic capabilities—such as needs assessment and resource reconfiguration—are better positioned to achieve sustainable outcomes. These results underscore the importance of integrating learning-driven strategies with capability development to enhance resilience and long-term viability.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".