Heuristics and biases in human–algorithm interaction and hotel revenue management override decision-making

Abstract

Purpose

This study aims to provide insights into human–algorithm interaction in revenue management (RM) decision-making and to uncover the underlying heuristics and biases of overriding systems’ recommendations.

Design/methodology/approach

Following constructivist traditions, 20 in-depth interviews were conducted with revenue optimisers, analysts, managers and directors with vast experience in over 25 markets and working with different RM systems (RMSs) at the property and corporate levels. The hermeneutics approach was used to interpret and make meaning of the participants’ lived experiences and interactions with RMSs.

Findings

The findings explain the nature of the interaction between RM professionals and RMSs, the cognitive mechanism by which the system users judgementally adjust or override its recommendations and the heuristics and biases behind override decisions. Additionally, the findings reveal the individual decision-maker characteristics and organisational factors influencing human–algorithm interactions.

Research limitations/implications

Although the study focused on human–system interaction in hotel RM, it has larger implications for integrating human judgement into computerised systems for optimal decision-making.

Practical implications

The study findings expose human biases in working with RMSs and highlight the influencing factors that can be addressed to achieve effective human–algorithm interactions.

Originality/value

The study offers a holistic framework underpinned by the organisational role and expectation confirmation theories to explain the cognitive mechanisms of human–system interaction in managerial decision-making.

Citation

Mohammed, I. and Denizci Guillet, B. (2024), "Heuristics and biases in human–algorithm interaction and hotel revenue management override decision-making", International Journal of Contemporary Hospitality Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJCHM-02-2024-0288