Strength and Weakness in Numbers? Unpacking the Role of Prevalence in the Diffusion of Reverse Mergers
Bibliographic record
Abstract
A common prediction in research on practice diffusion is a “strength in numbers” effect (i.e., that a growing number of past adopters will increase the number of future adopters). We advance and test a theoretical perspective to explain when and how practice prevalence may also generate a “weakness in numbers” effect. Specifically, in seeking to explain the diffusion of reverse mergers (RMs) — a controversial practice that allows a private firm to go public by merging with a publicly listed “shell company” — we suggest that prevalence affected their diffusion in a complex way, based on two divergent social influence pathways, creating: (1) a direct and positive effect of practice prevalence on potential adopters, who view prevalence as evidence of the practice’s value, and (2) an indirect and negative effect, mediated through third-party evaluators (i.e., investors, and the media) who view prevalence as a cause for concern and skepticism. We also highlight the utility of this theoretical framework by analyzing how a decline in the status of past adopters exerts a negative effect on diffusion through both social influence pathways. Employing structural equation modeling techniques, we find support for the hypothesized relationships and we discuss the implications of the study for future research on practice diffusion.
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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 | 0.015 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.004 |
| 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".