Examining the relationship between sustainability reporting processes and organizational learning and change
Bibliographic record
Abstract
Although there have been a number of publications discussing sustainability reporting (SR) in private and public sectors within the last decades, the number has been quite low when compared to works on non-governmental organizations (NGOs). This research explores this and finds that SR is a key driver for organisational learning and change in NGOs. A combination of descriptive statistics, grounded theory (GT) and inferential statistics was used to analyse the data. The findings show that SR and organisational learning and change share a reciprocal relationship that begins as the driver for learning and extends as change. This reciprocal relationship is repetitive and improves reporting process through enhanced sustainability performance in a mimetic approach. The research shows that SR fosters opportunities for cost and benefit evaluation, the institutionalization of sustainability, transfer of skill and innovation, attitudinal change towards sustainability, stakeholder engagement and ownership, as well as increasing the donor base. The findings further reinforce the contention that SR is influenced by organisational culture, donor behaviour and management decisions. The study also communicates the various lessons learnt from NGOs’ sustainability efforts that other NGOs, private and public sectors can benefit from.
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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 | 0.037 | 0.129 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".