Modelling SMEs failure in the hospitality industry of Malaysia
The contribution of Small and medium-sized enterprises (SMEs) in the hospitality industry is essential as businesses in this industry are dominated by SME operators. However, the failure rate among SMEs is relatively high, and the situation worsens when COVID-19 adversely affects SMEs. Therefore, pr...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | eng eng |
| Published: |
2022
|
| Subjects: | |
| Online Access: | https://etd.uum.edu.my/11138/1/s900360_01.pdf https://etd.uum.edu.my/11138/2/s900360_02.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The contribution of Small and medium-sized enterprises (SMEs) in the hospitality industry is essential as businesses in this industry are dominated by SME operators. However, the failure rate among SMEs is relatively high, and the situation worsens when COVID-19 adversely affects SMEs. Therefore, predicting the failure of SMEs in the hospitality industry can have a significant impact on the economy as an effective early warning signal. This study developed four models, namely multiple discriminant analysis, logistic regression, hazard and artificial neural network by using financial, non-financial, governance and macroeconomic variables to predict the failure of SMEs in the hospitality industry for the periods of one-year, two-year and three-year prior to failure. The sample consists of 350, 444 and 474 SMEs for the respective one-year, two-year and three-year period prior to failure for the period 2000 to 2016, where each sample comprises 50 percent failed and 50 percent non-failed SMEs. The findings of the financial variables show that inefficient, unprofitable, illiquid and highly indebted SMEs are more likely to fail. In terms of non-financial variables, the results indicate that younger SMEs are more prone to failure. For governance variables, number of directors and gender of directors in the boardroom, as well as ownership concentration are found significant. Moreover, lending rate and inflation rate are found to be associated with failure among SMEs. The results show that the artificial neural network model leads to a higher predictive accuracy rate compared to multiple discriminant analysis, logistic regression and hazard models. Financial institutions and creditors could use the models to screen out failing SMEs, while the National SMEs Development Council could use the models to improve its existing policies for the benefit of SMEs in the hospitality industry. |
|---|