Abstract
The aim of the current study was to investigate the mediating effect of an ethical climate on the relationship between organizational justice and workplace stress. The quantitative survey research design was used and cross-sectional data was collected. Structured questionnaires were distributed. The population of the study was nurses from the healthcare professionals working in the healthcare organizations in Qassim region. There are total of 5542 nurses working in different public and private healthcare organizations. Convenience sampling technique was used for selecting sample size. Total 510 completed questionnaires were collected back and used in the study for data analysis. For data analysis, AMOS-SEM was used. Measurement and structural models were developed and tested in the current study. Reliabilities and validities were investigated through the measurement model, while for hypotheses testing structural model was developed. Findings of the study indicated that the scale used in the study was found reliable and valid. Factor loadings, average variance extracted, and construct reliability met the threshold level/standard criteria. The structural model also revealed that ethical climate mediated the relationship between organizational justice and stress. This is the original work and contribution to the body of knowledge by extending the literature on justice, stress, and ethical climate. The presence of justice and ethical climate made it possible for employees to better manage their stress.
Highlights
Partial least squares structural equation modeling (PLS-SEM), known as PLS Path Modeling, is one of the most widely used methods of multivariate data analysis among business and social science scholars
We discuss three different commercial “stand alone” PLS-SEM applications with graphical user interface, namely SmartPLS (Ringle et al, 2015), WarpPLS (Kock, 2017) and ADANCO (Henseler & Dijkstra, 2015), which are currently available on the market
The software provides additional algorithms that are useful for understanding and modelling composite-based models, such as advanced bootstrapping (Aguirre-Urreta & Rönkkö, 2018; Hair et al, 2022), confirmatory tetrad analysis (Gudergan et al, 2008), importance-performance map analysis (Ringle & Sarstedt, 2016), predictive power assessment using PLSpredict (Shmueli et al, 2016; Shmueli et al, 2019), predictive model comparison based on information criteria such as BIC (Chin et al, 2020; Liengaard et al, 2021; Sharma et al, 2019a, 2019b), multi-group analysis based on bootstrapping and permutation (Cheah et al, 2020; Chin & Dibbern, 2010; Hair et al, 2018b), latent class segmentation using finite mixture PLS (Hahn et al, 2002; Sarstedt et al, 2011), and prediction-oriented segmentation (Becker et al, 2013)
Summary
Partial least squares structural equation modeling (PLS-SEM), known as PLS Path Modeling, is one of the most widely used methods of multivariate data analysis among business and social science scholars. We discuss three different commercial “stand alone” PLS-SEM applications with graphical user interface, namely SmartPLS (Ringle et al, 2015), WarpPLS (Kock, 2017) and ADANCO (Henseler & Dijkstra, 2015), which are currently available on the market (see Table 1).
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