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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




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


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.


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.


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



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.



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