"Category Promotion: How Hybrid Ventures Integrate ""Standing Out"" and ""Fitting In"""
Bibliographic record
Abstract
Scholars are increasingly attending to the process by which categories emerge and gain legitimacy. This research argues that category promotion – the practice whereby an organization voluntarily signals its affiliation with a recognizable and shared symbol or device to potential audiences – increases the legitimacy of new market categories. Yet this research also argues that category promotion of new categories is unlikely given the questionable legitimacy of those new categories. To better understand why an organization might promote a new category despite legitimacy concerns, this paper draws on old and new institutional theory, treating organizations not merely as members of isolated categories, but rather as members of a variety of both physical (e.g., geographic) and virtual (e.g., industrial) communities, all of which shape their actions. We test our hypotheses using a sample of 302 Certified B Corporations, finding that organizations’ geographic and industrial communities both influence the intensity of promotion, but do so in different ways. Our findings contribute to ongoing scholarship on category emergence and institutional theory as well as emerging scholarship on sustainable and hybrid organizations.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".