Effects of Complex Price Communication on Fairness: Case of a Sequential Communication
Bibliographic record
Abstract
Nowadays, pricing is one of the most challenging tasks for marketers. Despite its importance for both academics and practitioners, consumers’ reactions to prices remain not clear, especially with the emergence of new forms of prices, among which we can highlight the use of complex prices, which is becoming increasingly popular. Complex prices’ perception is highly dependent on the way they are communicated; consequently, complex prices communication plays a crucial role in shaping perception. This study is a continuity of previous researches that have validated the perceptual effects of complex prices communication. It attempts to show the effects complex prices communication has on its perceived fairness. In addition, the effects of the moderating variables; Seller Credibility and Responsibility Attribution (i.e., inferred motive) are studied. One hundred thirty-five undergraduate students participated in the study. They were randomly assigned to 2 (sequential communication of complex price vs non sequential communication of complex price) x2 (credible seller vs less credible seller) conditions. Manipulation consisted of presenting a scenario of buying an online air ticket. The results of our research highlight that sequential complex price communication has a significant effect on its perceived fairness. In particular, the results show that the perceived fairness of price is more negatively affected when the seller lacks credibility according to consumers. Also, it has been proved that the delayed communication of some of the complex price components could be perceived as a way to get a higher profit, which deepens the negative perceived fairness.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".