Contradictory yet Coherent? Inconsistency in Performance Feedback and R&D Investment Change
Bibliographic record
Abstract
In this paper, we study to what extent inconsistent feedback signals about performance affect firm adaptive behavior in terms of changes made to research-and-development (R&D) investments. We argue that inconsistency in performance feedback—based on discrepancies between two distinct performance signals—affects the degree to which such investments will be changed. Our aim is to show that accounting for inconsistent performance feedback is necessary as predictions for the direction of change in R&D investments based on the individual performance feedback signals are contradictory. Furthermore, we contribute by proposing a holistic consideration mechanism as an alternative to the selective attention mechanism previously applied to inconsistent performance feedback. Our findings show that the impact of inconsistency depends on the exact configuration of the underlying performance feedback signal discrepancies. While consistently negative performance feedback signals would amplify their impact in stimulating increased R&D investments, inconsistent performance feedback signals created more nuanced effects. Having lower performance compared to an industry-based peer group—despite doing well compared to the previous year—made firms decrease their R&D investments. For the opposite case of inconsistent performance feedback, we did not find an effect on change in R&D investments. These findings support to a degree our contention that explaining the effects of inconsistent performance feedback requires a holistic consideration theoretical mechanism instead of one involving selective attention. In sum, these findings suggest future research should take into account the differences between distinct instances of inconsistent performance feedback.
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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 | 0.008 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".