Intrinsic and Extrinsic Motivation and Participation in Budgeting: Antecedents and Consequences
Bibliographic record
Abstract
ABSTRACT: Based on Self-Determination Theory (SDT; Ryan and Deci 2000b; Gagne´ and Deci 2005), the present research proposes and tests a motivation-based model of participation in budgeting that distinguishes among intrinsic motivation, autonomous extrinsic motivation, and controlled extrinsic motivation for participative budgeting. The proposed model was tested using a survey conducted among managers of an international bank. The results suggest that while intrinsic motivation and autonomous extrinsic motivation for participation in budgeting are positively related to performance, controlled extrinsic motivation is negatively associated with performance. These findings highlight the importance of distinguishing among various forms of motivation in participative budgeting research and suggest that the mechanism by which the information benefits of participation in budgeting are obtained may be more complex than assumed. The results also provide evidence of the viability of using the proposed model to study commonly assumed reasons for participative budgeting within a general theoretically based framework of motivation.
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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 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".