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Record W7117573700 · doi:10.5539/jsd.v19n1p78

The Influence of Organizational Learning on the Relationship between Dynamic Capability and Sustainability of Non-Governmental Organizations in Nairobi County

2025· article· W7117573700 on OpenAlexvenueno aff
Alphonce Ochieng Okoth, Zachary Bolo Awino, Moses Machuki Otieno, Mary Kinoti

Bibliographic record

VenueJournal of Sustainable Development · 2025
Typearticle
Language
FieldBusiness, Management and Accounting
TopicOrganizational Learning and Leadership
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityDynamic capabilitiesSample (material)Vulnerability (computing)Organizational learningPopulationMacroResilience (materials science)

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.026
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.026
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.223
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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