Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/135758
Title: Prediction of tourism zombie companies using artificial intelligence algorithms and accounting data
Authors: Sánchez Medina, Agustín Jesús 
Blázquez Santana, Félix 
Cerviño Cortínez, Daniel Laureano 
Pellejero Silva, Mónica Avelina 
UNESCO Clasification: 531290 Economía sectorial: turismo
5303 Contabilidad económica
3325 Tecnología de las telecomunicaciones
Keywords: Zombie firms
Tourism SMEs
Artificial intelligence
Machine learning
Prediction
Issue Date: 2025
Journal: European Journal of Tourism Research 
Abstract: This paper addresses the problem of the prediction of zombie companies. Despite their relevance, financial issues represent an area scarcely considered in relation to the tourism industry and this topic has so far remained totally unexplored. Zombie firms cause great damage to the sector in which they compete. However, there is no consensus regarding what actions should be applied to them are not due to the negative consequences of maintaining or liquidating them. In view of this, the key issue is to prevent them from entering this state. It would therefore be useful to have a methodology for predicting years in advance when a company will become a zombie firm. For this purpose, this article has used different artificial intelligence algorithms applied to easily obtained accountant data. Thus, 78.4% of correct predictions have been utilizing a dataset of 356 Spanish small and medium-sized enterprises in the tourism sector.
URI: http://hdl.handle.net/10553/135758
ISSN: 1314-0817
DOI: 10.54055/ejtr.v39i.3648
Source: European Journal of Tourism Research, 39, 3906.
Appears in Collections:Artículos
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