A Liability of Breadth? The Conflicting Influences of Experiential Breadth on Perceptions of Founding Teams
Bibliographic record
Abstract
Although it is well established that top management team (TMT) experience is highly valued in new ventures, research has largely focused on the value of experience depth. However, founding teams often bring a myriad of different types of experience to their business. Less is understood about how these experiences are perceived by key stakeholders, and prior theory suggests that TMT breadth could be viewed as either an asset or a liability. Drawing from theory on cognitive categorization, we hypothesize that the perceived value of executive breadth depends on the context in which a venture is situated. We argue that the characteristics of the environment shape the degree to which experience breadth is valued, and we show that investors assess breadth positively in opportunistic environments but negatively in threatening environments. Contrary to previous research, we show that breadth can, at times, be viewed as a distinct liability for a new venture. In supplementary analyses, we also show that these effects are not contingent upon the depth of the founding team’s experience. Further, we find that founding team breadth does have significant influences on firm strategy, including the structural positioning of the firm in an industry’s value chain and the cultivation of diverse revenue streams, but that the effect of breadth on investor perceptions is not mediated through these differences in strategy.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".