Hedging Your Bets: Explaining Executives’ Labeling Strategies in Nanotechnology.
Bibliographic record
Abstract
Executives use market labels to position their firms within market categories. Yet this activity has been given scarce attention in the extant literature that widely assumes that market labels are simple, prescribed classification brackets that accurately represent firms’ characteristics. By examining how and why executives use the nanotechnology label, we uncover three strategies: claiming, disassociating, and hedging. Comparing these strategies to firms’ technological capabilities, we find that capabilities alone do not explain executives’ label use. Instead, the data show that these strategies are driven by executives’ aspiration to symbolically influence their firms’ market categorization. In particular, executives’ perception of the label’s ambiguity, their avoidance of perceived credibility gaps, and their assessment of the label’s signaling value shape their labeling strategies. In contrast to extant research, which suggests that executives should aim for coherence, we find that many executives hedge their affiliation with a nascent market label. Thus, our study shows that in ambiguous contexts, noncommitment to a market category may be a particularly prevalent strategy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".