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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.
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3–--12
Opis fizyczny
Bibliogr. 47 poz., rys., tab., wykr.
Twórcy
autor
- Universidad UTE, Faculty of Administrative Science, Ecuador
- Universidad UTE, Faculty of Administrative Science, Ecuador
autor
- Universidad UTE, Faculty of Administrative Science, Ecuador
- Universidad UTE, Faculty of Administrative Science, V´ıa Chone 4 1/2 y Ave. Italia, Santo Domingo, Ecuador
autor
- Universidad UTE, Faculty of Administrative Science, Ecuador
Bibliografia
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- [25] Watanabe S. et al. Factors associated with the prevalence of back pain and work absence in shipyard workers, BMC Musculoskeletal Disorders, 19, 1, 2018.
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- [28] Kausto J. et al., Length of sickness absence and sustained return-to-work in mental disorders and musculoskeletal diseases: A cohort study of public sector employees, Scandin. J. Work, Environ. Health, 43, 4, 358–366, 2017.
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- [30] Vaquero-Alvarez M. et al., ´ Influence of the working conditions on sickness absence due to common diseases, Aten. Prim., 50, 4, 238–246, 2018.
- [31] Queiroz-Lima M.E., Serranheira F., Absenteeism and presenteeism costs from occupational accidents with WRMSDS in a Portuguese hospital, DYNA (Colombia), 83, 196, 27–30, 2016.
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- [33] Van Hasselt M. et al., Prescription Drug Abuse and Workplace Absenteeism: Evidence from the 2008– 2012 National Survey on Drug Use and Health, J. Workplace Behav. Health, 30, 4, 379–392, 2015.
- [34] Bernstrøm V.H., Houkes I., A systematic literature review of the relationship between work hours and sickness absence, Work Stress, 32, 1, 84–104, 2018.
- [35] Deery S. et al., Exploring the relationship between compressed work hours satisfaction and absenteeism in front-line service work, Euro. J. Work Org. Psych., 26, 1, 42–52, 2017.
- [36] García Mainar I. et al., The Effect of Permanent Employment on Absenteeism: Evidence from Labor Reform in Spain, ILR Rev., 71, 2, 525–549, 2018.
- [37] Godøy A., Dale-Olsen H., Spillovers from gatekeeping – Peer effects in absenteeism, J. Public Econ., 167, 190–204, 2018.
- [38] Zaballa E. et al., Incidence of sickness absence by type of employment contract: One year follow-up study in Spanish salaried workers, Archives of Public Health, 74, 1, 2016.
- [39] Mendoza Llanos R., Job satisfaction and organizational culture as predictors of absenteeism, Rev. Med. Chile, 143, 8, 1028–1033, 2015.
- [40] Pekkala J. et al., Occupational class differences in long sickness absence: A register-based study of 2.1 million Finnish women and men in 1996-2013, BMJ Open, 7, 7, 2017.
- [41] Campanini P. et al. Workplace bullying and sickness absenteeism, Epidem. Preven., 37, 1, 8–16, 2013.
- [42] Allisey A. et al., An application of an extended effort-reward imbalance model to police absenteeism behavior, Person. Rev., 45, 4, 663–680, 2016.
- [43] Rabacow F.M. et al., The influence of lifestyle and gender on sickness absence in Brazilian workers, BMC Public Health, 14, 1, 2014.
- [44] Sumanen H. et al., Self-certified sickness absence among young municipal employees-changes from 2002 to 2016 and occupational class differences, Int. J. Environ. Res. Public Health, 14, 10, 2017.
- [45] Angelini E., di Tollo G., Roli A., A neural network approach for credit risk evaluation, The Quart. Rev. Econ. Fin., 48, 4, 733–755, 2008.
- [46] Avila Rondon R.L., Silva da Carvalho A., Infante Hernández, G., Neural Network Modelling and Simulation Of The Scheduling, [in:] Azevedo A. [Ed.] Innovation in Manufacturing Networks, BASYS 2008, IFIP – The Int. Fed. Inf. Proc., vol. 266, 2008, doi: /10.1007/978-0-387-09492-225.
- [47] Zekić-Sušac M., Scitovski R., Has A., Cluster analysis and artificial neural networks in predicting energy efficiency of public buildings as a cost-saving approach, Croatian Review of Economic, Business and Social Statistics, 4, 2, 57–66, 2018.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a8855b26-e84f-4a3b-a951-f86ac175927f