Team-based employee remuneration
Bibliographic record
Abstract
Purpose The purpose of this paper is to relate the balanced scorecard (BSC) to strategy and teams. Design/methodology/approach This paper proposes deriving performance targets and weights using a multiparty collaborative decision model that can be integrated into team-based bonus formulas. Findings Cross-functional division managers face a more complex problem in setting goals for individual managers. The proposed approach is intended to develop such goals and link them for team-based incentives. An example illustrates the application of the proposed BSC model and the team-based pay formula. Practical implications The model can be used to determine group bonus. Originality/value The paper has two objectives: to relate the BSC to the team setting with a participative flavor rather than with imposed targets and weights, and to develop a better way of relating behaviors and outcomes to the team’s and/or the organization’s goals. Integrating the strategies of various units adds a new dimension that differs from rationalizing the superior’s and the subordinate’s goals. The proposed model considers input from all value chain functional managers involved in implementing an organizational strategy. A methodology is provided to operationalize (Hope and Fraser, 2003) beyond the budgeting model principles.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".