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Predicting the financial distress of Indian hotels: an empirical analysis using Altman scores

Ajay Kumar Patel et al.

International Journal of Business Performance Management2026https://doi.org/10.1504/ijbpm.2026.152306article
AJG 1
Weight
0.50

What the paper says

The purpose of this paper is to assess the financial distress of Indian hotels by using Altman scores (Zo, Zs, and Zem). Further, it also endeavours to identify and suggest the Altman model and variables significant in assessing the financial distress for the hotel industry. The prediction models developed by Edward Altman, i.e., Z scores are applied to analyse possible financial distress conditions for hotels in India. The logistic panel regression model is applied to identify Altman model and variables significant in evaluating the financial distress of hotels. The findings suggest that more than 50% of hotels studied are under distressed condition as per all Altman's model, i.e., Zo, Zem and Zs scores. The Altman financial distress score results were similar to each other and also significantly similar to results identified by key financial indicators. The result revealed that all Altman models were significant, but the Zem model, for non-manufacturing firms in an emerging economy, had the highest number of significant variables.

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https://doi.org/https://doi.org/10.1504/ijbpm.2026.152306

Or copy a formatted citation

@article{ajay2026,
  title        = {{Predicting the financial distress of Indian hotels: an empirical analysis using Altman scores}},
  author       = {Ajay Kumar Patel et al.},
  journal      = {International Journal of Business Performance Management},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1504/ijbpm.2026.152306},
}

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Predicting the financial distress of Indian hotels: an empirical analysis using Altman scores

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.