Predicting the Risk of Employee's Long Term Absenteeism
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F18%3A10240131" target="_blank" >RIV/61989100:27510/18:10240131 - isvavai.cz</a>
Výsledek na webu
<a href="https://www.ekf.vsb.cz/rmfr/en/Conference_proceedings/" target="_blank" >https://www.ekf.vsb.cz/rmfr/en/Conference_proceedings/</a>
DOI - Digital Object Identifier
—
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Predicting the Risk of Employee's Long Term Absenteeism
Popis výsledku v původním jazyce
Absenteeism is becoming an issue with high importance for all companies increasing risks of decreased production, increased overtime work or higher costs for replacements. This work aims to present the analysis of factors which are influencing for absenteeism and estimate a predictive model that tells a likelihood of an employee to leave for a long-term absenteeism event for each employee within an organization. Logistic regression is used to predict individual risk of employee's long term absenteeism leave using eight selected factors as predictors. It is concluded that the model has significant predictive ability with good proportion of variability explained by the model but lower specificity with correct classification of long term absent employees from the whole sample due to large number of unpredictable events possible to cause long term absenteeism.
Název v anglickém jazyce
Predicting the Risk of Employee's Long Term Absenteeism
Popis výsledku anglicky
Absenteeism is becoming an issue with high importance for all companies increasing risks of decreased production, increased overtime work or higher costs for replacements. This work aims to present the analysis of factors which are influencing for absenteeism and estimate a predictive model that tells a likelihood of an employee to leave for a long-term absenteeism event for each employee within an organization. Logistic regression is used to predict individual risk of employee's long term absenteeism leave using eight selected factors as predictors. It is concluded that the model has significant predictive ability with good proportion of variability explained by the model but lower specificity with correct classification of long term absent employees from the whole sample due to large number of unpredictable events possible to cause long term absenteeism.
Klasifikace
Druh
D - Stať ve sborníku
CEP obor
—
OECD FORD obor
50206 - Finance
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2018
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název statě ve sborníku
Managing and Modelling of Financial Risks : 9th international scientific conference : 5th-6th September 2018, Ostrava, Czech Republic : proceedings
ISBN
978-80-248-4225-7
ISSN
2464-6970
e-ISSN
2464-6989
Počet stran výsledku
7
Strana od-do
337-343
Název nakladatele
VŠB - Technical University of Ostrava
Místo vydání
Ostrava
Místo konání akce
Ostrava
Datum konání akce
5. 9. 2018
Typ akce podle státní příslušnosti
WRD - Celosvětová akce
Kód UT WoS článku
—