Some Cross-Cultural Evidence on Whistle-Blowing as an Internal Control Mechanism
Bibliographic record
Abstract
In this paper, I report on the results of an empirical examination of cultural influences on professional judgments of Australian, Indian, and Chinese-Malaysian accountants in relation to whistle-blowing as an internal control mechanism. Australia serves as a proxy for the Anglo-American cluster of countries comprising the U.S., U.K., and Canada, while India and Malaysia represent the Asian-Indian and Chinese clusters, respectively. I draw on cultural characteristics and differences among these societies to formulate hypotheses that Australian professional accountants are both more likely and more accepting of engaging in whistle-blowing as an internal control mechanism than Chinese-Malaysian and Indian professional accountants. Data were gathered through a survey questionnaire administered to samples of senior professional accountants from Big 6 (at the time of data collection) firms in Australia, India, and Malaysia. The questionnaire comprised two whistle-blowing scenarios, and used both single-attribute and multidimensional attribute measures of professional judgment. The results support my hypotheses about differences in Australian compared to Indian and Chinese-Malaysian professional judgments. Additionally, the results support my expectation that multidimensional attribute measures would have greater explanatory power than single-attribute measures, and would provide insight into complex elements involved in ethical and professional judgments in cross-cultural settings. My findings from this study have implications for the management of local and multinational enterprises. Enterprises that aim to improve effectiveness in their control systems or achieve similar levels of reliability across divisions in various countries need to implement control systems that are compatible with cultural values. Specifically, the results suggest that compared to Indian and Chinese cultures, whistle-blowing as an internal control mechanism is likely to be more effective in Australian culture.
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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 | 0.016 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".