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Regression trees for hospitality data analysis

Mike Tsionas (Montpellier Business School, Montpellier, France and Lancaster University Management School, Lancaster, UK)
A. George Assaf (Isenberg School of Management, University of Massachusetts-Amherst, Amherst, Massachusetts, USA)

International Journal of Contemporary Hospitality Management

ISSN: 0959-6119

Article publication date: 3 January 2023

52

Abstract

Purpose

The purpose of this note is to describe the concept of regression trees (RTs) for hospitality data analysis.

Design/methodology/approach

RT is an effective non-parametric predicting modelling approach that would free researchers from the need to force a certain functional form. The method does not require normalization or scaling of data.

Findings

The authors illustrate how RTs can be used to find a model that would result in the best prediction.

Research limitations/implications

A common challenge facing hospitality researchers is to estimate a regression model with the correct specification. RTs can help researchers identify the best explanatory model for prediction.

Originality/value

This paper describes the concept of RTs for the modelling of hospitality data.

Keywords

Citation

Tsionas, M. and Assaf, A.G. (2023), "Regression trees for hospitality data analysis", International Journal of Contemporary Hospitality Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJCHM-06-2022-0705

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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