Measuring consumers' willingness to pay with utility-based recommendation systems

Michael Scholz, Verena Dorner, Markus Franz, Oliver Hinz

Publication: Scientific journalJournal articlepeer-review


Our paper addresses two gaps in research on recommendation systems: first, leveraging them to predict consumers' willingness to pay; second, estimating non-linear utility functions – which are generally held to provide better approximations of consumers' preference structures than linear functions – at a reasonable level of cognitive consumer effort. We develop an approach to simultaneously estimate exponential utility functions and willingness to pay at a low level of cognitive consumer effort. The empirical evaluation of our new recommendation system's utility and willingness to pay estimates with the estimates of a system based on linear utility functions indicates that exponential utility functions are better suited for predicting optimal recommendation ranks for products. Linear utility functions perform better in estimating consumers' willingness to pay. Based on our experimental data set, we show how retailers can use these willingness to pay estimates for profit-maximizing pricing decisions.
Original languageEnglish
Pages (from-to)60 - 71
JournalDecision Support Systems (DSS)
Publication statusPublished - 2015

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 502050 Business informatics

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