A longitudinal and cross sectional study of the impact of usage‐based attributes on used product price advertisements
Bibliographic record
Abstract
Purpose The growing volume of consumer transactions in used products markets has given rise to strategic decisions made by sellers in the amount of information disclosed on usage‐based attributes – product attributes that quantify the degree by which a product has been used. This paper seeks to address this issue. Based on the phenomenon referred to as the dilution effect, it is proposed that sellers would have less desire to disclose usage‐based attribute information in product advertisements for older products. The effects of usage‐based attributes on asking prices of used products are also examined and profiled across time and contrasted across three countries: Britain, Canada, and the USA. Design/methodology/approach Used product advertisements were obtained for 1995 (USA) and 2010 (USA, Canada, and Britain). The ratio of ad content dedicated to usage‐based attributes was then computed and profiled across various conditions, and variations were tested using statistical methods. Findings The results indicate that a dilution‐based strategy, whereby usage‐based product information is less likely to be disclosed for older products, was evident in 1995 as it was in 2010. Furthermore, cross‐country variations were observed in the use of this strategy. Research limitations/implications The research presented can be expanded to cover other product categories where used products are exchanged and usage‐based attributes constitute a fundamental consumer consideration in such exchanges. The research can also be extended to cover a wider range of countries for analysis. Originality/value This paper extends traditional pricing theory to consider the effects of usage‐based attributes on used product prices and associated consumer communications. Given the growth in the volume of consumer transactions in used product markets, this line of inquiry is relevant not only from an academic perspective but can also inform public policy and potential regulatory measures.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".