Optimal price strategy when facing privacy-concerned customers: uniform pricing <i>vs.</i> personalized pricing
Bibliographic record
Abstract
The increased availability of customer information has inspired personalized pricing in recent years. However, the implementation of personalized pricing faces some challenges. On the one hand, personalized pricing is imperfect and its accuracy depends on firms’ big data capability. On the other hand, customers have growing privacy concerns about personalized pricing. The above two downward factors hinder the profitability of personalized pricing, but how these factors affect the interests of supply chain members and customers remains unclear. Moreover, when facing privacy-concerned customers, it is worth exploring which strategy is better: personalized or uniform pricing. Our findings indicate that (i) As big data capability increases, supply chain members’ profits increase and customer surplus decreases; as privacy concerns increase, both supply chain members’ profits and customer surplus decrease. (ii) Personalized pricing can reduce double marginalization compared to uniform pricing. Privacy-concerned customers always prefer uniform pricing than personalized pricing. (iii) Uniform pricing is a win-win strategy when the big data capability is low, and personalized pricing is a win-win strategy when the big data capability is high. These findings can provide practical insights for firms to choose an optimal price strategy when facing privacy-concerned customers.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".