Categories and narratives as sources of distinctiveness: Cultural entrepreneurship within and across categories
Bibliographic record
Abstract
Abstract Research Summary Cultural entrepreneurship theory suggests that entrepreneurial narratives need to be optimally distinctive—neither portraying an offering as too similar to nor too distinctive from the conventions of its product category—for attracting superior demand. Building on and extending this literature, we propose that the benefits and downsides of a distinctive narrative fundamentally depend on a category's distinctiveness vis‐à‐vis alternative categories because distinctive categories (a) provide an important source of differentiation for their members and (b) disproportionally attract those audience members that highly value novelty. Our longitudinal study of 159,343 Airbnb listings in 45 categories strongly supports our hypotheses: the relationship between Airbnb listings' narrative distinctiveness and demand‐side performance flips from an inverted U‐shaped curve in indistinctive categories to a U‐shaped curve in distinctive categories. Managerial Summary Entrepreneurs need to craft a compelling narrative around their offering to legitimate and differentiate it from competing offerings. In this article, we explore when and why entrepreneurs should craft narratives that portray their offerings as similar, moderately distinctive, or highly distinctive from other offerings. We study this question in the context of the Airbnb marketplace, in which Airbnb hosts compete with their respective accommodation listings. Our study shows that Airbnb listings in indistinctive categories attract most demand when their narratives portray them as moderately distinctive. In contrast, Airbnb listings in distinctive categories attract the most demand when their narratives portray them as either highly similar or highly distinctive from other listings in their category
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".