Uncovering Governance And Mindfulness Patterns For Improved Performance: The Role Of Management Accounting Systems Change
Bibliographic record
Abstract
This study extends the model developed in Williams and Seamans [Williams, J. J. and Seaman, A. E. (2010). Corporate Governance and Mindfulness: The Impact of Management Accounting Systems Change, The Journal of Applied Business Research, Vol. 26, No. 5, pp. 1-17] exploratory paper examining the moderating effects of management accounting systems (MAS) change on the corporate governance/mindfulness relationship for a Canadian sample of 124 top-level accounting professionals. Canonical correlation analysis was applied to the linkage of multiple cognitive processes of mindfulness (Weick and Sutcliffe, 2001; 2007) and the governance dimensions of performance and conformance specified by the International Federation of Accountants (2009), underpinned by the moderating effects of five different components of MAS change, which yielded 13 significant relationships. The latter were subsequently analyzed for important gestalts (i.e., patterns) in the overall relationship, and assessed within the context of aligning professional accounting practices involving systems changes to the IFAC (2009) governance framework. These findings appear to have implications for improved governance structures in practice as well as offering a rich foundation for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".