A preliminary adaptation and validation of the Overwork Climate Scale by Mazzetti, Schaufeli, Guglielmi and Depolo
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Uniwersytet Gdański / University of Gdańsk, Gdańsk, Poland (Instytut Psychologii / Institute of Psychology)
Andrzej Piotrowski   

Uniwersytet Gdański, Instytut Psychologii, ul. Jana Bażyńskiego 4, 80-309 Gdańsk
Online publication date: 2019-03-22
Med Pr 2019;70(2):177–188
Background: The aim of the research was to perform the initial adaptation and validation of the Overwork Climate Scale (OWCS) developed by Mazzetti, Schaufeli, Guglielmi and Depolo. The scale was designed to measure 2 dimensions: overwork endorsement and lacking overwork rewards. Material and Methods: The study was conducted on 2 samples of 580 and 103 workers respectively. A confirmatory factor analysis was conducted to test the OWCS structure. The validity of the scale was tested by correlation analysis using criterion variables including the perceived frequency of organizational stressors, job satisfaction, burnout and work engagement. The reliability of the Polish version of the OWCS was estimated by computing 2 internal consistency measures – Cronbach’s α and McDonald’s ω. Results: The result of the confirmatory factor analysis showed that the 2-factor model adequately represents the data and has a substantially better fit index than the 1-factor model. The reliability of both measured dimensions, i.e., overwork endorsement (α = 0.80, ω = 0.85) and lacking overwork rewards (α = 0.74, ω = 0.79) was found to be satisfactory, with the overall overwork climate (α = 0.82, ω = 0.86) also measured reliably. As expected, it was shown that as the perception of the overwork climate increases, there is a higher frequency of organizational stressors declared, the organization is evaluated as more oppressive, and higher loss of control over work is reported. Conclusions: The results of preliminary research using the Polish version of the OWCS indicate that the tool has good psychometric properties, which encourages further research on its full validation, including the development of a statistical standard. Med Pr. 2019;70(2):177–88