Market Structure and the Diffusion of Electronic Banking
Bibliographic record
Abstract
This paper studies the role that market structure plays in affecting the diffusion of electronic banking. Electronic banking represents a process innovation since it reduces the cost of performing many types of transactions for banks. However, electronic banking (and electronic commerce more generally) is particular since the full benefits for firms from adoption only accrue once consumers begin to perform a significant share of their transactions online. Since it is costly for consumers to switch to the new technology (they must learn how to use it) banks may try to encourage consumers to go online by affecting the relative quality of the online and offline options. Their ability to do so is a function of market structure since in more competitive markets, reducing the relative attractiveness of the offline option involves the risk of losing customers (or potential customers) to competitors, whereas, this is less of a concern for a more dominant bank. Based on the Beggs and Klemperer (1992) model of price competition, we develop a model of branch-service quality choice with switching costs meant to characterize the trade-off banks face when rationalizing their network between technology penetration and business stealing. The model is solved numerically and we show that the incentive to lower
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.
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 | 0.001 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".