How do you think you are doing? Managerial perceptions, aspirations and innovation
Bibliographic record
Abstract
Empirical studies on the behavioral theory of the firm have been mostly silent on the possibility that managerial perceptions of relative performance may diverge from the relative performance measured using objective firm performance metrics. Considering this presents an opportunity to deepen the literature on firm-level aspirations and the behavioral theory of the firm. Using organizational-level survey data we examine how the inconsistency between managerial perception of relative performance and actual relative performance, affects the propensity to launch innovative products or processes. Based on literature on managerial cognition, we also consider two types of social and historical inconsistencies – one, when perceptual performance is markedly above actual performance change, which we term as overestimation, and, two when perceptual performance change falls markedly below actual performance change, which we term as underestimation. In line with broader BTOF literature, the main results show that innovation propensity reduces more rapidly with performance feedback when social performance is negative than when it is positive. This supports the more generally accepted hypothesis that problemistic search is intensified under negative social performance. However, we also find that positive historical performance increases the propensity to innovate suggesting a reinforcement argument. The second set of findings pertain to inconsistency between perceived and actual relative performance. We find that both types of inconsistencies in social and historical performance reduce the propensity to innovate, except when historical performance is overestimated - when the propensity increases. These findings suggest that divergence between perceptions and actual relative performance confounds an organization due to multiple interpretations, and therefore dampens its responsiveness. However, our interaction analysis reveals that in the case of one type of inconsistency – underestimation -- this inertia is overcome with increases in actual social and historical performance such that when perceptions of performance is negative, increasing actual performance adds ‘fuel to the fire’ of problemistic search.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".