Private firms’ portfolio expansion responses to (in)consistent performance feedback
Bibliographic record
Abstract
Purpose Performance feedback can be constructed using firms’ own (historical) performance, or the performance of peers (social). Those two types of performance feedback can be consistent (both positive, both negative) or inconsistent (one positive, the other negative). The research on the impact of consistent versus inconsistent feedback has been inconclusive, suggesting that inconsistent feedback might lead to more intense or less intense responses, or no response. In this paper, we theorize and test how firms respond to (in)consistent performance feedback. Design/methodology/approach We test our hypotheses on a longitudinal sample of 2,819 private, high-growth firms in the US with 6,688 observations between the years 2007 and 2016. Our dataset comprises 25 different industries. We use topic modeling on textual data from firms’ web pages to capture portfolio expansion. Findings We find that consistent negative performance feedback strengthens portfolio expansion, but consistent positive feedback does not influence portfolio expansion. We also find that inconsistent performance feedback weakens portfolio expansion, but only with negative historical feedback and positive social feedback. Originality/value We contribute to the Behavioral Theory of the Firm by improving our understanding of mechanisms of feedback configurations. Specifically, we elaborate on the role of (in)consistent social feedback when firms respond to historical performance feedback. We also contribute to the theory by better understanding private firms’ responses to 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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".