Predicting hospitality firm failure: mixed sample modelling

Hui Li (College of Tourism and Service Management, Nankai University, Tianjin, China)
Yu-Hui Xu (College of Economics and Management, Hengyang Normal University, Hengyang, China)
Lean Yu (School of Economics and Management, Beijing University of Chemical Technology, Beijing, China)

International Journal of Contemporary Hospitality Management

ISSN: 0959-6119

Publication date: 10 July 2017

Abstract

Purpose

Available information for evaluating the possibility of hospitality firm failure in emerging countries is often deficient. Oversampling can compensate for this but can also yield mixed samples, which limit prediction models’ effectiveness. This research aims to provide a feasible approach to handle possible mixed information caused by oversampling.

Design/methodology/approach

This paper uses mixed sample modelling (MSM) when evaluating the possibility of firm failure on enlarged hospitality firms. The mixed sample is filtered out with a mixed sample index through control of the noisy parameter and outliner parameter and meta-models are used to build MSM models for hospitality firm failure prediction, with performances compared to traditional models.

Findings

The proposed models are helpful in predicting hospitality firm failure in the mixed information situation caused by oversampling, whereas MSM significantly improves the performance of traditional models. Meanwhile, only partial mixed hospitality samples matter in predicting firm failure in both rich- and poor-information situations.

Practical implications

This research is helpful for managers, investors, employees and customers to reduce their hospitality-related risk in the emerging Chinese market. The two-dimensional sample collection strategies, three-step prediction process and five MSM modelling principles are helpful for practice of hospitality firm failure prediction.

Originality/value

This research provides a means of processing mixed hospitality firm samples through the early definition and proposal of MSM, which addresses the ranking information within samples in deficient information environments and improves forecasting accuracy of traditional models. Moreover, it provides empirical evidence for the validation of sample selection and sample pairing strategy in evaluating the possibility of hospitality firm failure.

Keywords

Acknowledgements

This research is partially supported by the National Natural Science Foundation of China (No. 71571167), the Zhejiang Provincial National Science Foundation for Distinguished Young Scholars of China (No. LR13G010001), the National Science Foundation for Distinguished Young Scholars of China (No. 71025005), the Science and Technology Development Project of Hengyang (No. 2015KG63) and the Science Foundation of Hengyang Normal University (No. 15A08).

Citation

Li, H., Xu, Y.-H. and Yu, L. (2017), "Predicting hospitality firm failure: mixed sample modelling", International Journal of Contemporary Hospitality Management, Vol. 29 No. 7, pp. 1770-1792. https://doi.org/10.1108/IJCHM-03-2015-0092

Publisher

:

Emerald Publishing Limited

Copyright © 2017, Emerald Publishing Limited

To read the full version of this content please select one of the options below

You may be able to access this content by logging in via Shibboleth, Open Athens or with your Emerald account.
To rent this content from Deepdyve, please click the button.
If you think you should have access to this content, click the button to contact our support team.