Does Information Technology Reputation Affect Bank Loan Terms?
Bibliographic record
Abstract
ABSTRACT This study investigates whether Information Technology (IT) reputation, captured by the accumulation of consistent IT capability signals, influences bank loan contracting even though banks have access to inside information. We predict that IT reputation is associated with better loan terms because it lowers credit risk via its impact on default and information risks. Results based on 4,218 loan facility-years reveal, as predicted, that firms with a reputation for IT capability tend to have more favorable price and non-price terms for loan contracts and are less likely to have their credit rating downgraded or to report internal control weaknesses than firms with no IT reputation. The study contributes to the banking and IT business value literature by showing that banks incorporate borrowers' nonfinancial characteristics, such as IT reputation, into loan contracting terms. JEL Classifications: G21; G32; M41; O32. Data Availability: All data are available from sources identified in the study.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".