The Usefulness of Accounting Information and Management Accounting Practices under Environmental Uncertainty
Bibliographic record
Abstract
The purpose of this paper is twofold. Firstly, we aim to investigate the relationships among environmental uncertainty, broad-scope and timely management accounting information usefulness, and (traditional and contemporary) management accounting practices (MAPs) usage. Secondly, we intend to explore how these relationships influence decision-makers’ satisfaction with management accounting information. Survey data were obtained through an online questionnaire from 114 large manufacturing companies operating in Portugal. The findings indicate a positive relationship between environmental uncertainty and timely management accounting information usefulness and between (broad-scope and timely) management accounting information usefulness and (traditional and contemporary) MAPs usage. The findings also show that decision-makers’ satisfaction with management accounting information improves when there is a good fit between environmental uncertainty, broad-scope and timely management accounting information usefulness, and MAPs usage. In this way, organisations need to adjust the implementation and usage of MAPs to contextual factors, using both contemporary and traditional MAPs, to achieve greater decision-makers’ satisfaction with management accounting information. Thus, the results achieved in this study are useful for both theory and practice and have several implications for professionals engaged in MAPs implementation and decision-making activities.
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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 | 0.006 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".