Advancing Target Setting Research in Management Accounting: Insights and Emerging Directions from the <i>JMAR</i> Forum
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Targets are a central component of management control systems and are widely used to direct employee attention and motivate their effort. Accounting research has largely validated the performance-enhancing effects of targets, especially when linked with rewards. However, unique and dynamic institutional features differentiate accounting settings from those examined elsewhere. In this introduction, we introduce the articles appearing in the Journal of Management Accounting Research special forum on target setting in management accounting. We then highlight five promising directions for future accounting research: (1) target pursuit in teams, (2) remote and AI-supported target setting, (3) targets and affect, (4) neurophysiological research on targets as goals, and (5) field experiments on targets. Together, we hope this article can inspire future research aimed at advancing theory and informing practice around targets.
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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 | 0.016 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.002 |
| 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 it