Sustainability of Non-Profit Organizations: A Systematic Review
Bibliographic record
Abstract
This study aims to present the state of the academic literature on the sustainability of non-profit organizations (NPOs) and how previous researchers have approached NPO sustainability. The review has resulted in the final 30 articles for review and analysis. The research characteristics of the articles are identified by examining the publication outlet. It is observed that research in the 1990s on NPO sustainability is scarce, with a focus on understanding and identifying models of sustainability. Research in the first decade of the 21st Century has focused on the financial and non-financial approaches to address the sustainability of NPO. Meanwhile, research in the 21st Century's second decade acknowledged the need to integrate the diverse perspective of NPO sustainability into a comprehensive framework. This study found various approaches that influence NPO sustainability: financial or economic approach; organizational characteristics and strategy; capacity building; partnership or collaboration; and value-based perspective. Theoretically, this study provides an overall view of how academic researchers have attempted to address NPOs' sustainability. In terms of practical contribution, this study serves as a guide for the NPOs and relevant regulators on how to address the organization’s sustainability from various perspectives. We further set out suggestions for future research as well as the limitations and contributions of this study.
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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 | 0.015 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".