Bibliographic record
Abstract
We study the market consequences of advances in consumption tracking technologies—such as mobile banking apps that help consumers monitor their spending and avoid overdrawn accounts—using a two-period consumption model. In the model, consumers pay a penalty fee if they consume in both periods. In the second period, consumers may be forgetful of their first-period consumption, although the use of consumption tracking can remind them. According to our analysis, the availability of consumption tracking often helps consumers at the expense of the firm; such benefits may be direct, where consumers make use of the technology to avoid penalty fees, or indirect, where the mere availability of consumption tracking forces the firm to lower its penalty fee. If consumers are partially sophisticated regarding their forgetfulness, however, the availability of consumption tracking may instill a false sense of security in that consumers expect to use consumption tracking to avoid penalty fees but ultimately, decide not to bother, making them especially susceptible to penalty fees. In some cases, the availability of consumption tracking may actually compel a firm to impose a penalty fee that would not otherwise be viable, leading to higher profits and lower consumer surplus. As we show, this scenario is attained within an intermediate range of forgetfulness and at a level of (partial) sophistication for which consumers overestimate their demand for the technology. This paper was accepted by Dmitri Kuksov, marketing. Funding: M. Shi appreciates financial support from HKUST Yuk-Shee Chan Professorship Fund and China NSF [Grant 72272036]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.00522 .
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 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".