Pricing in the Presence of Strategic Consumers and Social Learning Under Contingent Pricing and Price Guarantee
Bibliographic record
Abstract
Purchasing new experience products or services often involves significant quality uncertainty for both consumers and firms. Social learning through online reviews helps reduce this uncertainty but exacerbates strategic waiting, as consumers delay purchases to gain more information. This paper examines the impact of social learning on firms’ pricing policies in the presence of strategic consumers under two widely adopted schemes: Contingent pricing and price guarantee. We find that while social learning always benefits the firm, it enables the price guarantee scheme to outperform contingent pricing in terms of profitability for highly patient consumers, which would not be possible without social learning. Notably, social learning drives a range of pricing patterns, even making price skimming optimal under price guarantee for highly patient consumers, as high initial prices combined with markdowns effectively alleviate strategic waiting, enhancing review outcomes and the firm’s profitability. Additionally, social learning always enables the firm to extract greater consumer surplus for impatient consumers under price guarantee. In contrast, social learning under contingent pricing consistently benefits consumers and can achieve win-win outcomes when consumers are moderately patient. Our extensions validate the robustness of these findings under different assumptions, including fully rational consumers and partially forward-looking firms. In particular, a partially forward-looking firm can achieve win-win outcomes with social learning under price guarantee, expanding its practical applicability. This study provides novel insights into the role of social learning in shaping pricing strategies, highlighting its implications for firm profitability and consumer welfare in markets influenced by review dynamics and strategic consumers.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".