The impact of the diffusion of a financial innovation on company performance: an analysis of SWIFT adoption
Bibliographic record
Abstract
How does a major financial network innovation influence firm performance? Despite muchspeculation we have little hard quantitative evidence about the impact of technology diffusionin financial services. In this paper we use the entire adoption history for SWIFT (the Societyfor Worldwide Interbank Financial Telecommunication - standards provider and messagingcarrier) matched to bank-level panel data for the US, Canada and 27 European countries. Ourdataset covers almost 7,000 banks (including 1,689 SWIFT adopters) between 1998 and2005. We find that adoption appears to have large effects on profitability, but it takes severalyears before any positive return is discernible, consistent with the idea of significantcomplementarities between new technologies and firm organization. The profitability effectoperates by both raising sales and decreasing operating costs and is greater for smaller firmsthan larger firms. Although the long-run effects are similar, US and UK banks appear to reapthe benefits from adoption more quickly than their Continental European counterparts. This isconsistent with the idea that the impact of information and communication technologies isstronger in the US than Europe due to lower adjustment costs.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".