Feedback and Incentives on Nonfinancial Value Drivers: Effects on Managerial Decision Making*
Bibliographic record
Abstract
This paper examines how adding leading non-financial value drivers to a lagging summary financial measure affects managerial decision making in firms where either intangible assets (intangible assets firm) or tangible assets (tangible assets firm) are more important for future financial performance. Using an experiment, I compare a control performance evaluation system (PES) with feedback and incentives on only a summary financial measure to a PES with added feedback on non-financial measures and a PES with added feedback and incentives on non-financial measures. I find that managers increase their decision quality more in the intangible assets firm than in the tangible assets firm when both feedback and incentives on non-financial measures are added, but not when only feedback on non-financial measures is added. Early in the experiment, managers of the intangible assets firm do not make better decisions with the adding of only feedback on non-financial measures, but do so with the further adding of incentives on non-financial measures. However, managers of the intangible assets firm improve their decisions over time with the adding of only feedback on non-financial measures. On the other hand, managers of the tangible assets firm do not make better decisions with the adding of only feedback on non-financial measures nor with the further adding of incentives on non-financial measures. The results suggest that the benefits of adding non-financial value drivers may vary based on a firm's dependency on tangible versus intangible assets, and on whether the non-financial value drivers are explicitly rewarded in the incentive contract.
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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 | 0.007 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".