Use of Corporate Disclosures to Identify the Stage of Blockchain Adoption
Bibliographic record
Abstract
SYNOPSIS Several studies have pointed to the transformative effects of blockchain on a wide spectrum of firms, industries, and professions. Despite the arguable consensus within the business community that blockchain will have a real impact on the way firms do business, views diverge when it comes to the timing of diffusion (i.e., when blockchain will achieve mass adoption). We propose that information gathering helps potential adopters form expectations regarding payoffs from blockchain adoption. Information-gathering activities and the resulting information sources, such as web searches, news articles, book titles, and corporate disclosures, can proxy the expectations of potential adopters. Corporate disclosures directly reflect firms' expectations and interests in the new technology. We leverage the corporate disclosure data from the SEC Edgar database to identify the current stage of blockchain adoption. Our analysis shows that while blockchain adoption is still nascent, the focus has been shifting from cryptocurrencies to business applications. Data Availability: Data are available from public sources cited in the text. JEL Classifications: M15.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".