Learning from the Failure of the EU Payment Services Directive (PSD2): When Imposed Innovation Does Not Change the Status Quo
Bibliographic record
Abstract
Payment Services Directive (PSD2) regulations were introduced to stimulate growth and competitiveness in the EU financial sector by simplifying the sharing of the infrastructure and customer data between incumbent banks and other players, including new financial institutions and fintech startups. Alas, the new rules received a lukewarm or hostile response from the industry incumbents, who perceived them as additional costs and a possible threat to their competitive advantage. As such, PSD2 became an exemplar of the gaining attention in literature phenomenon of “imposed innovation,” a change that does not make microeconomic sense to incumbent firms but is instead mandated by influential non-market stakeholders. So far, most of the imposed innovations cases were studied in corporate social responsibility, environmental or safety domains, with limited understanding of this phenomenon in other areas. Based on a detailed case study of PSD2 implementation failure in the German banking industry, we demonstrate that without certain identifiable contextual factors, the societally-important innovations within an established industry might not materialize. By studying the implication of PSD2 and its effect on the EU banking industry, for the first time, we provide practical suggestions for how to improve the effectiveness of imposed innovation, from the public policy and firm perspectives.
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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 | 0.018 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.014 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.011 | 0.008 |
| 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".