The Bright Side of Loss Aversion in Dynamic and Competitive Markets
Bibliographic record
Abstract
A well-established phenomenon of consumer buying behavior is that consumers evaluate prices relative to a reference point and exhibit loss aversion; i.e., their propensity to buy is more negatively affected by prices above the reference point than it is positively affected by prices below the reference point. The objective of this paper is to analytically examine how the competitive strategy and profitability of firms are affected by the presence of consumer loss aversion in the price dimension. Although we assume that consumer loss aversion increases consumer propensity to search for lower prices, we find that it does not necessarily lead to lower prices or profits when firms compete over multiple periods and when the consumer reference price in subsequent periods is affected by current prices. Specifically, consumer loss aversion could lead to higher prices and profits when consumer valuation is sufficiently high relative to search costs and the proportion of consumers with positive search costs is in an intermediate range. We also show that when forward-looking firms incorporate the negative effect of price promotions on future profits, the equilibrium range of price promotions may actually increase.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".