Starting off on the Wrong Foot? Newly Founded Firms, TMT Structures, and the Unusualness Penalty
Bibliographic record
Abstract
*Abstract*: While there is abundant evidence about the effects of top-management-team (TMT) characteristics, less is known about the effects of TMT structures, meaning the array of functional positions that TMT members fill.There are strong norms about TMT structures, so organizations that violate these norms may suffer.This is especially true for startups, which rely on their TMTs for legitimacy.Although many new organizations adopt TMT structures that are typical in their industry, others adopt more unusual structures.To study the effects of TMT structure unusualness, we build on theories of TMTs, legitimacy, and imprinting.We argue that organizations with more unusual TMT structures at startup suffer an /unusualness penalty/ and are more likely to fail, and that this unusualness penalty is stronger for specialists than generalists.We also argue that even if organizations amass experience with unusual TMT structures or their structures become less unusual, the unusualness penalty persists.To test these predictions, we analyze data on firms in one industry over five decades.The results generally support our predictions, indicating that unusual TMT structures have negative consequences that organizations cannot easily overcome.
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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 | 0.004 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".