The Effects of National Culture on Changes in Management Accounting Systems
Bibliographic record
Abstract
This study utilises Hofstede’s (1980, 1991) framework to investigate the influence of national culture on the volume of management accounting control systems (MACS) changes, and their location, nature and perceived success. We collect data on French manufacturing companies which are comparable with those collected in Canada (Libby & Waterhouse, 1996), Singapore (Williams & Seaman, 2001) and Malaysia (Sulaiman & Mitchell, 2005). We find dramatic differences in MACS changes attributable to cultural differences. A high degree of uncertainty avoidance is reflected in more changes to controlling systems, and individualism and power distance reinforce this result. A high degree of Confucian dynamism is reflected in more changes to planning systems. However, the effect of culture on changes to bonuses is limited. Individualism explains differences in the nature and perceived success of changes. Belonging to a collectivistic culture limits elimination of MACS, while belonging to an individualistic culture increases the likelihood of addition of MACS. Managers in individualistic cultures rate the success of changes more highly than managers in collectivistic cultures. More specifically, managers in individualistic cultures consider they have been most successful in introductions of new techniques while managers in collectivist cultures think they have been most successful in modifications. Other differences are explained by macroeconomic factors and similar results are observed in the location and nature of change. This study also enhances examination of the heterogeneous nature of change.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".