Underperformance duration and innovative search: Evidence from the high‐tech manufacturing industry
Bibliographic record
Abstract
Research Summary Behavioral theory examines how the intensity of underperformance influences firms' strategic decisions; yet, it largely fails to consider the effect of underperformance duration. Drawing on behavioral theory and organizational learning, we argue that the length of time that a firm has been underperforming contributes to shaping firms' innovative search patterns. We test our theory merging COMPUSTAT and NBER patent data for 1,610 high‐tech manufacturing companies between 1986 and 2006. Our results largely support our predicted curvilinear relationships. We find that innovative search magnitude and scope each first decreases and then increases with underperformance duration. In addition, we find marginal evidence that innovative search depth first increases and then decreases with underperformance duration. The statistical and practical significance of the results is also discussed. Managerial Summary Innovation is vital for a firm's survival and competitive advantage and requires a search for knowledge. Previous research suggests that the gap between current performance and desired performance is an important trigger for firms' innovative action. We suggest that how long the firm has been underperforming also plays an important role in firm innovation. Using financial and patent data on public high technology manufacturing firms, we show that there are nonlinear relationships between the duration of a firm's underperformance and its innovative activities. We find that underperforming firms first decrease and then increase R&D spending and the use of new knowledge as underperformance prolongs. Our results imply that underperforming firms face competing short‐ and long‐term pressures that influence the nature of its innovative activities.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".