Strategic Responses to Algorithmic Recommendations: Evidence from Hotel Pricing

Daniel Garcia, Juha Tolvanen, Alexander K. Wagner*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

We study the interaction between algorithmic advice and human decisions using high-resolution hotel-room pricing data. We document that price setting frictions, arising from adjustment costs of human decision makers, induce a conflict of interest with the algorithmic advisor. A model of advice with costly price adjustments shows that, in equilibrium, algorithmic price recommendations are strategically biased and lead to sub- optimal pricing by human decision makers. We quantify the losses from the strategic bias in recommendations using as structural model and estimate the potential benefits that would result from a shift to fully automated algorithmic pricing.
Original languageEnglish
JournalManagement Science
Volume0
Issue number0
Early online dateApr 2024
DOIs
Publication statusPublished - 2024

Keywords

  • AI Advice
  • Algorithmic recommendation
  • human decision
  • Adjustment cost
  • delegation

Fields of Science and Technology Classification 2012

  • 502 Economics

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