Operationalizing business ethics in organizations: the views of executives in Australia, Canada and Sweden
Bibliographic record
Abstract
Purpose: Codes of ethics have become the mainstay of the ethics programs of corporations. Many studies have explored their contents, but few have examined what makes them effective. This international study aims to identify the measures viewed as being important by top executives in determining the worth to their organizations of corporate codes of ethics. Design/methodology/approach: Data were collected by questionnaires sent to the top 500 companies ranked by revenue operating in the private sectors in Australia, Canada and Sweden. By analyzing the survey results from the top corporate executives in these countries, the research team was able to test for a number of determinants of effectiveness for codes of ethics. Findings: In a statistically significant model, it was found that four factors related to the internal management of the corporation are positively correlated to executives’ perceptions of the value of their corporate codes of ethics. Research limitations/implications: Future research may seek to address features of this study that limit its generalizability, as it was conducted on the largest of companies in each country and thus this sample may not reflect the way that business ethics are managed in smaller organizations in those countries. Originality/value: If executives see particular items as important to their business ethics success, one could postulate that this has arisen from a perception that implementing these measures has been effective for their organizations. This provides guidance to other organizations on what items could enhance the effectiveness of their codes of ethics.
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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 | 0.018 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.018 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.011 |
| 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".