Modeling Consumer Ratings: The Trade-Off Between Quality and Consumer Preference

ORCID

France: https://orcid.org/0000-0002-7226-2750; Ghose: https://orcid.org/0000-0003-4940-7277; Karki: https://orcid.org/0009-0001-8672-6261

MSU Affiliation

College of Business; Department of Marketing, Quantitative Analysis and Supply Chain Logistics

Creation Date

2026-03-30

Abstract

Is there such a thing as a good quality product or service or is quality purely a result of the heterogeneity in preferences of different consumers? This article looks into the trade-offs between quality and preference aspects of consumer ratings from a measurement perspective. First, we give an overview of different methods of ratings analysis with a focus on the quality and preference components of these methods. We develop a measurement framework and associated methods to analyze the relative quality and preference components in consumer ratings. This framework is tested using a Monte Carlo simulation on generated data and using an empirical case study analysis of real-world online reviews. The framework shows a good ability to distinguish between quality-based and preference-based ratings and the case study results show good face validity. In particular, an entropy-based measure gives a consistent quality-preference scale and an information-theoretic interpretation. We give suggestions for future academic work on analyzing quality and preference trade-offs in ratings analysis. We give recommendations for managers on how to utilize the framework to improve online review platforms, on whether to analyze consumer ratings using traditional preference-based methods or more quality- or knowledge-based methods, and on how to improve STP (segmentation, targeting, and positioning) strategies.

Publication Date

1-22-2026

Publication Title

European Journal of Operational Research

Publisher

Elsevier

Creative Commons License

Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.

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Digital Object Identifier (DOI)

https://doi.org/10.1016/j.ejor.2026.01.001