Missed chances and unfulfilled hopes: Why do firms make errors in evaluating technological opportunities?
Bibliographic record
Abstract
Abstract Research Abstract This study examines commission and omission errors in the evaluation of technological opportunities. Integrating structural and cognitive perspectives, we propose that inventors with more cohesive collaboration networks within the firm or geographically closer to the corporate headquarters exert greater influence on the dominant representations shaping opportunity evaluation within the firm. Thus, their inventions are more likely to be positively assessed, even if quality considerations suggest otherwise. Conversely, even when superior in quality, inventions from individuals with less cohesive collaboration networks within the firm or located far from the corporate headquarters are less likely to be positively evaluated, leading to omission errors. The study provides evidence based on 22 interviews and archival data from the mobile phone and personal digital assistant industry between 1990 and 2010. Managerial Abstract This study examines commission and omission errors in decision‐making about technologies. Studying patent renewal decisions of 42 firms in the mobile phone and personal digital assistant industry between 1990 and 2010, we show that inventors with more cohesive collaboration networks within the firm or located close to the corporate headquarters have their inventions positively assessed even when of lower quality, leading to commission errors. On the other hand, inventors with less cohesive collaboration networks within the firm or located far away from the corporate headquarters have their inventions disregarded even when of higher quality, causing omission errors. These findings call for managerial vigilance in technology evaluation decisions, ensuring valuable ideas are not overlooked due to an inventor's network position or location.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".