Price and quality optimization with the intertemporal reference price effect
Bibliographic record
Abstract
When making purchasing decisions for a certain product, consumers normally take the historical price as a reference. Focusing on the intertemporal reference price effect (IRPE), this study investigates the joint optimization problem of price and quality from a new product releaser's perspective. More specifically, we establish a choice model based on the multinomial logit model to deal with consumers' behaviour when facing a newly released product and an existing alternative. Our analyses start from a short-term case, where the optimization is only about determining the best price, and then move to a long-term case, in which the firm can adjust both the price and quality. The consumers' attitudes toward the IRPE are examined respectively in the asymmetric format (i.e., the loss-averse and gain-seeking consumers) and the symmetric format (the loss-neutral consumer). The analytical results with and without the reference price are compared regarding reference price sensitivity parameters, from which managerial insights are outlined to facilitate the firm's pricing and quality decisions. Finally, the intertemporal reference quality effect (IRQE) is studied on top of the IRPE. Such an extension sheds light on the additional impact of IRQE and the importance of jointly considering IRQE and IRPE while optimizing price and quality.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".