Incidences of Variables in Labor Absenteeism: an Analysis of Neural Networks

• Autorzy: Reyner Perez-Campdesuner , Margarita De Miguel-Guzman , Gelmar Garcia-Vidal , Alexander Sanchez-Rodriguez , Rodobaldo Martinez-Vivar
• Języki publikacji: EN

Abstrakty

EN

Labor absenteeism is a factor that affects the good performance of organizations in any part of the world, from the instability that is generated in the functioning of the system. This is evident in the effects on quality, productivity, reaction time, among other aspects. The direct causes by which it occurs are generally known and with greater reinforcement the diseases are located, without distinguishing possible classifications. However, behind these or other causes can be found other possible factors of incidence, such as age or sex. This research seeks to explore, through the application of neural networks, the possible relationship between different variables and their incidence in the levels of absenteeism. To this end, a neural networks model is constructed from the use of a population of more than 12,000 employees, representative of various classification categories. The study allowed the characterization of the influence of the different variables studied, supported in addition to the performance of an ANOVA analysis that allowed to corroborate and clarify the results of the neural network analysis. (original abstract)

Słowa kluczowe

EN

Human Resources Management (HRM); Neural networks; Absence from work; Work-Life Balance (WLB); Variance analysis

PL

Zarządzanie zasobami ludzkimi; Sieci neuronowe; Absencja w pracy; Równowaga praca-życie; Analiza wariancji

• Czasopismo: Management and Production Engineering Review
• Rocznik: 2020
• Tom: 11
• Numer: nr 1
• Strony: 3--12

Twórcy

autor

Reyner Perez-Campdesuner

• Universidad UTE, Ecuador

autor

Margarita De Miguel-Guzman

• Universidad UTE, Ecuador

autor

Gelmar Garcia-Vidal

• Universidad UTE, Ecuador

autor

Alexander Sanchez-Rodriguez

• Universidad UTE, Ecuador

autor

Rodobaldo Martinez-Vivar

• Universidad UTE, Ecuador

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Identyfikatory

DOI

10.24425/mper.2020. 132938