Award Date

1-1-1999

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Hotel Administration

First Committee Member

Zheng Gu

Number of Pages

88

Abstract

Business failures in the hospitality industry were examined in this study from both microeconomic and macroeconomic perspectives. In the micro-level study, this paper developed a discriminant model for predicting bankruptcy of hospitality firms (lodging and restaurant firms). This model achieved a 92-percent accuracy in classifying the in-sample firms into bankrupt and non-bankrupt groups one year prior to bankruptcy, and a 83-percent accuracy two years prior to bankruptcy. This model also correctly predicted an out-of-sample bankruptcy from one to four years in advance. Additionally, in order to eliminate the industry effect, the study estimated a bankruptcy prediction model for the restaurant industry. This model had a 94-percent classification accuracy one year before the occurrence of bankruptcy. The results of the micro-level study demonstrate that fairly accurate bankruptcy prediction of hospitality firms is possible by using financial ratios and discriminant analysis; In the macro-level study, the impact of macroeconomic conditions on the lodging industry failure rate for each state was investigated. The results suggest that two macroeconomic factors, change in real gross state product and change in disposable personal income, have significant impact on the lodging failure rates. This study is useful for the lodging industry in that it will enable lodging firms to make decisions to reduce the exposure to business failure based on projected economic condition.

Keywords

Business; Failure; Hospitality; Industry; Macroeconomic; Microeconomic; Perspectives; Study

Controlled Subject

Finance; Industrial management; Accounting; Commerce

File Format

pdf

File Size

3481.6 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Permissions

If you are the rightful copyright holder of this dissertation or thesis and wish to have the full text removed from Digital Scholarship@UNLV, please submit a request to digitalscholarship@unlv.edu and include clear identification of the work, preferably with URL.

Identifier

https://doi.org/10.25669/zoxt-jxvq


Share

COinS