Dynamics of Consumer Adoption of Financial Innovation: The Case of ATM Cards
Why this work is in the frame
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Bibliographic record
Abstract
We develop a structural consumer life-cycle model to investigate consumers' adoption and usage decisions of ATM cards. If consumers are forward-looking with a known discount factor, our framework can control for the heterogeneous life span faced by consumers of different ages, and hence measure adoption costs more accurately. Moreover, our framework can recover the monetary value of total adoption costs. To estimate our model, we use an Italian panel data set, which contains information on consumers' adoption decisions for ATM cards, and their cash withdrawal patterns before and after adoption. Our results suggest that one could significantly overestimate adoption costs for the elderly when ignoring their shorter life span. Our policy experiments show that a sign-up bonus targeted to the elderly could be much more effective if implemented as a limited-time offer rather than a permanent offer. Interestingly, if the sign-up bonus is permanent, younger consumers may strategically postpone adoption. This paper was accepted by Pradeep Chintagunta, marketing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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