The Effectiveness of Tiered Goals in Performance Measurement Systems
Bibliographic record
Abstract
Goals are an important aspect of any organization's performance measurement system. Yet setting appropriate goals to motivate individuals in the organization is not a trivial task. In this study we investigate an alternative to both single stretch goals and individual goals in the form of tiered goal systems. Tiered goals have received increased practitioner attention with companies such as BI(R) using goal bands in their GoalQuestTM product. While prior academic literature has pointed to this approach as viable (Latham and Locke 1991) there has been no empirical testing of either its effectiveness over time or its effectiveness against other established goal setting approaches such as the single stretch goal. In this study we investigate not only the performance outcome but also some of the underlying cognitive processes in terms of goal commitment, goal attractiveness and self-efficacy. Our results show an initial significant increase in performance when the tiered goal system is introduced, which is subsequently reversed as individuals find the ratcheting up of performance goals unacceptable and reduce their commitment to the higher goal. We also find that contrary to expectations that compared to a single stretch goal, the tiered goal neither resulted in significant initial nor long term differences as individuals in the stretch goal persisted with the goal over time.
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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 | 0.034 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| 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".