Measuring Willingness to Pay: A Comparative Method of Valuation
Bibliographic record
Abstract
Willingness to pay (WTP) is a metric that is widely valued and utilized among both practitioners and academics. However, the conceptualization of WTP is ambiguous, and this ambiguity is reflected across existing methods of measuring WTP. The authors first present a formal mathematical framework that clarifies WTP as a distributional concept—rather than a single number—constructed as a function of customers, comparisons, and situations. The framework further reveals the operation of two comparative mechanisms, direct and indirect, by which situational factors affect WTP. They then introduce a new method to measure WTP—the comparative method of valuation (CMV)—that, unlike existing methods, is designed to account for the inherently comparative and situational nature of WTP. Across nine studies reported in the article and four additional studies in the Web Appendix, the authors (1) examine differences in results between CMV and choice-based conjoint as well as between CMV and the classic Becker–DeGroot–Marschak methodology, (2) demonstrate that CMV is a valid and reliable measure of WTP, and (3) illustrate applications of CMV to managerial problems. This article offers both conceptual clarity and methodological advances to understanding the construction and measurement of WTP for practitioners and academics alike.
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 | 0.034 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".