New Empirical Generalizations on the Determinants of Price Elasticity
Abstract
The importance of pricing decisions for firms has fueled an extensive stream of research on price elasticities. In an influential meta-analytical study, Tellis (1988) summarized price elasticity research findings until 1986. However, empirical generalizations on price elasticity require modifications because of (1) changes in market characteristics (i.e., characteristics of brands, product categories, and economic conditions) and (2) changes in the research methodology used to assess price elasticities. Therefore, the authors present a meta-analysis of price elasticity with new empirical generalizations on its determinants. Across a set of 1851 price elasticities based on 81 studies, the average price elasticity is −2.62. A salient finding is that over the past four decades, sales elasticities have significantly increased in magnitude, whereas share and choice elasticities have remained fairly constant. The authors find that accommodating price endogeneity has a strong (magnitude-increasing) impact on price elasticities. A striking null result is that accounting for heterogeneity does not affect elasticities significantly. The authors also present an analysis that explains the difference between their findings and Tellis's findings, and they indicate which new price elasticity studies are most desirable.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Across AI models, price elasticity estimates (-1.6 to -2.2) are broadly in the same range as brand/SKU-level price elasticity estimates for human shoppers.
“Our price coefficients admit interpretation as price elasticity of demand for a product with small market share; across AI models our elasticity estimates (-1.6 to -2.2) are broadly in the same range as brand/SKU-level price elasticity estimates for human shoppers [4].”
From What Is Your AI Agent Buying? Evaluation, Biases, Model Dependence, & Emerging Implications for Agentic E-Commerce (2025) · cites this paper as [4]
Cites
Hox, J., Moerbeek, M., & van de Schoot, R. (2002) · doi:10.4324/9781410604118 · Cited by 2,419 · see citing works
Robustness issues in multilevel regression analysis
Maas, C. J. M., & Hox, J. J. (2004) · Statistica Neerlandica · doi:10.1046/j.0039-0402.2003.00252.x · Cited by 837 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Babić Rosario, A., Sotgiu, F., De Valck, K., & Bijmolt, T. H. A. (2016) · Journal of Marketing Research · doi:10.1509/jmr.14.0380 · Cited by 1,073 · see citing works
Self-citationThe Role of Big Data and Predictive Analytics in Retailing
Bradlow, E. T., Gangwar, M., Kopalle, P., & Voleti, S. (2017) · Journal of Retailing · doi:10.1016/j.jretai.2016.12.004 · Cited by 516 · see citing works
Breuer, C., Hüffmeier, J., & Hertel, G. (2016) · Journal of Applied Psychology · doi:10.1037/apl0000113 · Cited by 375 · see citing works
Online and Offline Demand and Price Elasticities: Evidence from the Air Travel Industry
Granados, N., Gupta, A., & Kauffman, R. J. (2012) · Information Systems Research · doi:10.1287/isre.1100.0312 · Cited by 189 · see citing works
The dual role of price: Decomposing consumers’ reactions to price
Völckner, F. (2008) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-007-0076-7 · Cited by 160 · see citing works
Drivers of Consumer Adoption of e-Commerce: a Meta-Analysis
Zerbini, C., Bijmolt, T. H. A., Maestripieri, S., & Luceri, B. (2022) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2022.04.003 · Cited by 68 · see citing works
Self-citationThe impact of introducing a customer loyalty program on category sales and profitability
Lin, C., & Bowman, D. (2022) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2021.102769 · Cited by 44 · see citing works
Allouah, A., Besbes, O., Figueroa, J. D., Kanoria, Y., & Kumar, A. (2026) · Proceedings of the ACM Web Conference 2026 · doi:10.1145/3774904.3792943 · Cited by 7 · see citing works
Allouah, A., Besbes, O., Figueroa, J. D., Kanoria, Y., & Kumar, A. (2025) · arXiv
- Authors
- Bijmolt, T. H. A., Van Heerde, H. J., & Pieters, R. G. M.
- Affiliations
- University of Groningen (NL)
- Tilburg University, the Netherlands
- Tilburg University (NL)
- Corresponding author
- Bijmolt, Tammo H.A.
- Pieters, Rik G.M.
- Venue
- Journal of Marketing Research
- Publisher
- SAGE Publications
- Published
- 2005-05
- Volume
- 42
- Issue
- 2
- Pages
- 141-156
- ISSN
- 0022-2437 (ISSN-L)
- 1547-7193
- OpenAlex
- W2153205326
- Cited by
- 440
- References
- 2
- Citations by year
- 2026: 29 · 2025: 22 · 2024: 19 · 2023: 24 · 2022: 21 · 2021: 31 · 2020: 26 · 2019: 19 · 2018: 20 · 2017: 24 · 2016: 20 · 2015: 23 · 2014: 22 · 2013: 20 · 2012: 23
- FWCI
- 45.30
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Marketing Research (publishedVersion)
- Research portal (Tilburg University) (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › Consumer Market Behavior and Pricing
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Economics of Agriculture and Food Markets
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Merger and Competition Analysis
- Keywords
- price elasticity, price endogeneity, price setting
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review