Factors influencing ethical climate in a nonprofit organisation: an empirical investigation
Bibliographic record
Abstract
Abstract Drawing from Victor and Cullen's[Victor, B. and Cullen, J. B. (1987) ‘A theory and measure of ethical climate in organizations’, Research in Corporate Social Performance and Policy, Vol. 9, pp. 51–71.],[Victor, B. and Cullen, J. B. (1988) ‘The organizational bases of ethical work climates’, Administrative Science Quarterly, Vol. 33, pp. 101–125.] theoretical framework a recent study by Agarwal and Malloy[Agarwal, J. and Malloy, D. C. (1999) ‘Ethical work climate dimensions in a not‐for‐profit organization: An empirical study’, Journal of Business Ethics, Vol. 20, pp. 1–14.] examined ethical work climate dimensions in the context of a nonprofit organisation. This paper reviews the framework and extends the study further by investigating several factors that influence the perception of ethical work climate in a nonprofit organisation. The multiple analysis of variance (MANOVA) procedure is employed to test nine hypotheses. Results demonstrate somewhat unique findings regarding factors that influence ethical climate perception in a nonprofit context. Specifically, the findings of this study point to the level of education, decision style and the influence that superiors and volunteers have upon ethical perception. Results also demonstrate that factors that have been employed traditionally by forprofit management, such as length of service, codes of ethics, size of the organisation and peer pressure, do not effectively influence ethical perception in the nonprofit context. Finally implications of this study are discussed. Copyright © 2003 Henry Stewart Publications
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 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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".