It Is Not the Whole Story: Toward a Broader Understanding of Entrepreneurial Ventures’ Symbolic Differentiation
Bibliographic record
Abstract
Entrepreneurial ventures strategically communicate information about themselves to convey their distinctiveness and attract favorable audience attention. This study explores how the possession of quality-signaling resources, such as patents, influences the degree to which entrepreneurial ventures convey distinctiveness in their entrepreneurial narratives. Cultural entrepreneurship research spotlights such resources as the ingredients around which entrepreneurs construct distinctive narratives and proposes that resource-rich ventures will present themselves as particularly distinctive. Challenging this, we argue that ventures rich in quality-signaling resources—while ideally positioned to convey their distinctiveness—will likely forgo this symbolic differentiation opportunity under certain industry conditions due to a lack of external incentives. Our analysis of 31,270 UK-based ventures launched between 2010 and 2021 finds that, compared to patent-poor ventures, patent-rich ventures exhibit higher levels of narrative distinctiveness when situated in industries that receive little attention, but substantially lower levels of narrative distinctiveness when situated in hot industries that attract a lot of attention. In doing so, our study challenges the assumption that entrepreneurial ventures always aim to present themselves as distinctive as legitimately possible, delineates conditions under which this assumption is likely violated, and lays the groundwork for a broader research agenda on organizations’ substantive and symbolic differentiation.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".