Culture matters: Cultural variability in corporate codes of conduct as a means to foster organizational legitimacy
Bibliographic record
Abstract
Abstract In recent decades, implementing a code of conduct (CoC) as part of an organization's CSR infrastructure has become a sine qua non for gaining trust and fostering credibility. Despite numerous studies aimed at identifying cultural differences in the content of CoCs, little is known about what causes those differences and how they relate to an organization's communicative endeavor to gain trust and strengthen its legitimacy. In response, this article examines potential cultural differences in the public availability, design, and content of CoCs of corporations headquartered in countries in the Confucian Asian versus Anglo cultural clusters from the perspective of strategic communication. Drawing on the concepts of individualist versus collectivist culture and low‐ versus high‐context communication, the findings reveal significant differences, including that Anglo‐based companies more often make their CoCs publicly available and, in turn, significantly more comprehensive than Confucian Asian codes. Furthermore, compared with Anglo CoCs, significantly fewer CoCs of companies headquartered in Confucian Asia address the importance of moral values in daily business practices and sensitive issues such as prohibited behavior, whistleblowing, and sanctions following code violations. Those findings indicate significant institutional and cultural differences in companies' communication about ethical principles and corresponding conduct and suggest that, across cultures, CoCs differ in their content and are not accorded equal relevance as a means to foster legitimacy via CSR communication.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".