| Literature DB >> 31500365 |
Zubair Akram1, Yan Li2, Umair Akram3.
Abstract
This study represents an important step towards understanding why supervisors behave abusively towards their subordinates. Building on the conservation of resources theory, this study investigates the impact of abusive supervision on counterproductive work behaviors (CWBs) from a stress perspective. Furthermore, job demands play a significant moderating effect, and emotional exhaustion has a mediating effect on the relationship between abusive supervision and CWBs. A time-lagged design was utilized to collect the data and a total of 350 supervisors-subordinates' dyads are collected from Chinese manufacturing firms. The findings indicate that subordinates' emotional exhaustion mediates the relationship between abusive supervision and CWBs only when subordinates are involved in a high frequency of job demands. Additionally, emotional exhaustion and abusive supervision were significantly moderated by job demands. However, the extant literature has provided that abusive supervision has detrimental effects on employees work behavior. The findings of this study provide new empirical and theoretical insights into the stress perspectives. Finally, implications for managers and related theories are discussed, along with the boundaries and future opportunities of this study.Entities:
Keywords: China; abusive supervision; conservation of resources theory; counterproductive work behavior; emotional exhaustion; job demands
Mesh:
Year: 2019 PMID: 31500365 PMCID: PMC6765885 DOI: 10.3390/ijerph16183300
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1A moderated-mediation model.
Results of CFA for all exogenous and endogenous factors.
| Model |
|
| TLI | CFI | RMSEA |
|---|---|---|---|---|---|
| The base line model (Four-factor model) | 129.16 | 80 | 0.94 | 0.96 | 0.032 |
| (Three-factor model-1:) | 210.77 | 83 | 0.91 | 0.93 | 0.147 |
| Abusive supervision and job demands combined | |||||
| (Three-factor model-2:) | 201.54 | 83 | 0.93 | 0.91 | 0.118 |
| Abusive supervision and emotional exhaustion combined | |||||
| (Three-factor model-3:) | 185.29 | 83 | 0.82 | 0.85 | 0.193 |
| Emotional exhaustion and CWB combined | |||||
| (Three-factor model-4:) | 334.65 | 83 | 0.92 | 0.94 | 0.192 |
| Job demands and emotional exhaustion combined | |||||
| (Two-factor model-1:) | 928.64 | 85 | 0.88 | 0.76 | 0.233 |
| Abusive supervision, Job demands and emotional exhaustion combined | |||||
| (Two-factor model-2:) | 334.98 | 85 | 0.86 | 0.59 | 0.254 |
| Abusive supervision and Job demands combined, CWB and emotional exhaustion | |||||
| (One-factor model-1:) | 433.88 | 62 | 0.42 | 0.52 | 0.287 |
N = 350, Acronyms: CFA = Confirmatory Factor Analysis, RMSEA = Root Mean Square Error of Approximation, TLI = Tucker Lewis index, CFI = Comparative fit index.
Mean, Correlation, Standard Deviation for all original variables.
| Variables | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 9 | 9 | 10 | 11 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Education | |||||||||||
| 2 | Age | −0.19 * | ||||||||||
| 3 | Gender | 0.08 | 0.05 | |||||||||
| 4 | Tenure | 0.17 | 0.08 | 0.05 | ||||||||
| 5 | Organizations level | 0.32 ** | 0.02 ** | 0.04 | 0.16 * | |||||||
| 6 | PA | 0.32 ** | 0.05 | 0.16 * | 0.04 | 0.07 | 0.91 | |||||
| 7 | NA | −0.17 * | 0.07 | −0.05 | 0.01 | −0.04 | 0.12 | 0.94 | ||||
| 8 | AS | −0.51 | 0.22 ** | 0.45 | 0.36 | −0.21 | −0.11 | 0.48 | 0.89 | |||
| 9 | JD | 0.14 * | −0.16 * | 0.21 | 0.3 | 0.12 | −0.01 | 0.46 | 0.34 | 0.86 | ||
| 10 | EE | 0.23 | −0.09 | 0.34 | 0.01 | −0.19 | 0.65 | −0.03 | 0.22 ** | 0.08 | 0.90 | |
| 11 | CWB | −0.15 * | 0.12 | 0.14 * | 0.19 | 0.01 | 0.12 | 0.16 * | 0.13 ** | 0.22 | 0.18 *** | 0.96 |
| M | 2.38 | 2.9 | 0.04 | 0.22 | 0.32 | 2.29 | 4.19 | 4.29 | 3.65 | 2.74 | 2.55 | |
| SD | 5.61 | 6.52 | 0.89 | 0.96 | 0.51 | 1.21 | 4.54 | 6.52 | 1.13 | 4.76 | 0.18 | |
N = 350; * p < 0.05, ** p < 0.01, *** p < 0.001; Bold values in diagonal are Cronbach’s alpha value of each variable. Acronyms: PA = Positive affect, NA = Negative Affect, AS = Abusive supervision, JD = job demand, EE = emotional exhaustion, CWB = counterproductive work behavior. M = Mean, SD = Standard Deviation.
Results of hypotheses testing.
| Variables | Emotional Exhaustion | CWB | ||
|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | Model 4 | |
|
| ||||
| Education | −0.09 | −0.01 | −0.05 | −0.02 |
| Age | −0.01 | −0.19 | −0.18 | −0.18 |
| Gender | −0.02 | −0.03 | −0.05 | −0.04 |
| Tenure | 0.01 | −0.01 | −0.01 | 0.01 |
| Organizational level | 0.11 | 0.54 | 0.53 * | 0.50 |
| Positive affectivity | −0.04 | −0.09 | −0.14 | 0.12 |
| Negative affectivity | 0.23 ** | 0.28 ** | 0.35 ** | 0.29 ** |
|
| ||||
| Abusive supervision | 0.22 ** | 0.02 | 0.11 * | 0.23 |
|
| ||||
| Job demands | 0.03 | 0.24 ** | 0.18 ** | 0.18 ** |
|
| ||||
| Abusive supervision × Job demands | 0.29 ** | 0.12 ** | 0.13 ** | |
|
| ||||
| Emotional exhaustion | 0.27 ** | 0.18 ** | ||
|
| 0.08 | 0.11 | 0.14 | 0.15 |
| ∆ | 0.02 | 0.03 | 0.01 | 0.04 |
|
| 3.76 *** | 11.79 ** | 3.16 ** | 6.00 * |
| ∆ | 3.76 *** | 8.03 ** | 3.16 ** | 2.84 ** |
N = 350; * p < 0.05 (two-tailed), ** p < 0.01 (two-tailed), *** p < 0.001 (two-tailed).
Figure 2Moderating effect of abusive supervision and job demands (JD) on emotional exhaustion.
The results of moderated path analysis.
| Variables | AS→EE→CWB | ||||
|---|---|---|---|---|---|
| Moderator: Job Demands | Stage | Effect | |||
| First | Second | Direct | Indirect | Total | |
|
|
|
| ( | ( | |
| Simple path for high job demands | 0.26 ** | 0.31 ** | 0.29 ** | 0.08 ** | 0.37 ** |
| Simple path for low job demands | 0.09 ** | 0.25 ** | 0.11 ** | 0.02 | 0.13 ** |
| Differences | 0.17 ** | 0.06 ** | 0.18 ** | 0.06 ** | 0.24 ** |
N = 350, * p < 0.05 (two-tailed), ** p < 0.01 (two-tailed); PMX = Path from AS to EE; PYM = Path from EE to CWB; PYX = Path from AS to CWB. Low job demands refer to one standard deviation below the mean of job demands; High moderator refers to one standard deviation above the mean of job demands. Tests of differences between the indirect and total effects were based on bias-corrected confidence intervals derived from bootstrap estimates.