The (Limited) Power of Blockchain Networks for Information Provision
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We investigate the potential and limits of privacy-preserving corporate blockchain applications for information provision. We provide a theoretical model in which heterogeneous firms choose between adopting a blockchain application or relying on traditional third-party intermediaries to inform the capital market. The blockchain’s ability to generate information depends on each firm’s data profile and all firms’ endogenous adoption decisions. We show that blockchain technology can improve the information environment and outperform traditional institutions with firms’ adoption decisions serving as a credible value signal and the application uncovering firm values by analyzing all participating firms’ data. However, we also characterize an adverse mixed-adoption equilibrium in which neither of the two channels realizes its full potential and information provision declines not only for individual firms, but also in aggregate. The equilibrium is a warning sign that has broad implications for policymakers’ regulatory effort and investors’ assessment of corporate blockchain applications. This paper was accepted by Suraj Srinivasan, accounting. Funding: B. Franke and Q. Gao Fritz gratefully acknowledge financial support from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project-ID 403041268–TRR 266 Accounting for Transparency. A. Stenzel gratefully acknowledges financial support from the DFG through CRC TR 224 (Project C03) during prior employment at the University of Mannheim. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4718 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it