The Architecture Of Management Account-ing Systems Change: A Multidimensional Approach
Bibliographic record
Abstract
We extend the model developed by Williams and Seaman [Williams, J. J. & Seaman, A. E. (2001). Predicting change in management accounting systems: national culture and industry effects. Accounting, Organizations and Society, 26, 443–460] and utilize the same sample of 93 Chinese family owned businesses to test the multivariate relationship between a set of operational effectiveness measures and a set of changes in management accounting and control systems components under the contingency effects of size, organizational capacity, intensity of competition and centralization. Significant relationships emerge for high intensity of competition, high centralization, and low and high levels of organizational capacity. Variables of major importance in these relationships converge to form two larger patterns of a structural and environmental nature, which are consonant with the core national cultural values of Singapore documented in the contemporary cross-cultural management control literature.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".