Dynamic Pricing in the Presence of Social Intervention and Consumer Learning
Bibliographic record
Abstract
With the booming growth of social media, product evaluation by key opinion leader (KOL) plays an increasingly important role in purchasing decisions of consumers. In particular, consumers may strategically delay purchases in anticipation of the KOL’s evaluation, which may be influenced by firms through social intervention, especially for the product that is first introduced with uncertain quality. This paper examines how social intervention affects consumer learning and in turn the strategic interaction between a monopolistic firm and strategic consumers in a two-period model. Taking into account discount factors of the firm and consumers, we find that the firm always invests a suitable effort, instead of overexerting, to intervene in the KOL’s evaluation, thereby, influencing product word-of-mouth (WOM). Nonetheless, social intervention may not invariably benefit the firm, even harm him in some cases. As for pricing policies, though commitment pricing has been deemed an optimal way to respond to strategic consumer behavior in some previous studies, we show that it is suboptimal relative to responsive pricing once profit discount is considered in the absence of social intervention. Driven by social intervention, responsive pricing and commitment pricing have their own strength. The firm favors the former if his preferred sales period and strategic consumers’ preferred purchase period are incompatible, otherwise, the latter is adopted. Moreover, the incremental value of social intervention in the latter dominates that in the former, as such value relies more on intervention effort.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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 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".