PATRONES DE SUPERVIVENCIA PARA LA GESTIÓN DE LOS CENTROS ESPECIALES DE EMPLEO
Sheltered Employment Centers (CEEs) are profitable firms that contract workers with disability to prepare them for incorporation in society. Due to their growth, competitiveness in the labor market and work in the framework of the social economy, these companies have attracted some interest recently...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Facultad de Ciencias Sociales y Jurídicas, Universidad de Jaén
2015-07-01
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| Series: | Revista de Estudios Empresariales. Segunda Época |
| Subjects: | |
| Online Access: | http://revistaselectronicas.ujaen.es/index.php/REE/article/view/2214/2047 |
| Summary: | Sheltered Employment Centers (CEEs) are profitable firms that contract workers with disability to prepare them for incorporation in society. Due to their growth, competitiveness in the labor market and work in the framework of the social economy, these companies have attracted some interest recently. However, these enterprises have to face great challenges because they have to be competitive in the market in order to guarantee their survival.
The objective of this paper is to analyze the survival of the Sheltered Employment Centers, to ascertain the key variables that condition their continuity in the market, or otherwise, their business failure. The initial sample is the total number of CEEs in Spain, 1.668 firms. The financial statements of all were extracted from 2013, the last period available. Then, the all CEEs were classified in tree groups (healthy, doubtful and distressed), according to their Altman Z’-score. A method of artificial intelligence (algorithm C 4.5) was used in order to obtain the basic patterns of each of the groups.
The main contribution of this study is that we can know which CEEs survive with the ratios of debts over net sales, return on assets, and quick ratio and which one will have more difficulties to stay in the market. Moreover, the artificial intelligence methodology used is a new approach compared to traditional statistical techniques. |
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| ISSN: | 0213-8964 1988-9046 |