Groupcentric budget goals, budget-based incentive contracts, and additive group tasks
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the effects of three different types of budget goals (egocentric individual, groupcentric individual and group) on group performance of an additive task, assigned within an individual budget-based incentive contract. While previous research has established that budget-based incentive contracts motivate higher group performance than piece rate contracts for additive group tasks, no studies, which we are aware of, have considered explicitly the type of goal within this context. Design/methodology/approach – We conduct a 3 × 2 experiment in which we manipulate the presence of an individual goal (egocentric, groupcentric and absent) and a group goal (present and absent) on group performance of an additive task. Findings – Group performance is higher for groups assigned groupcentric individual goals than for groups assigned egocentric individual goals, either alone or in combination with a group goal. Practical implications – Egocentric individual goals may reinforce an individualistic orientation, which may work against the potential gains from having group members adopt more of a group focus. Originality/value – This paper considers how groupcentric individual goals may improve group performance. The management accounting literature typically examines just egocentric individual goals.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".