then step two regresses the independent variable (i.e., LMX) on the mediator variable (i.e., distributive justice; Figure 21). If this relationship is significant, then step three is employed. In step three (Figure 22), the mediator is entered into the regression
equation first predicting the outcome variable. Then, the independent variable is entered into the regression equation. Full mediation exists if the relationship between the independent variable and dependent variable is non-significant. Partial mediation exists if the relation between the independent variable and the dependent variable is reduced in magnitude from step 1. An example of these three steps is provided in Table 15 for Hypothesis 6a.
This process was used for each of the outcome variables in turn for distributive justice and then for procedural justice.
The Mediation Effect of Distributive Justice
In the first step of the mediator analysis (Table 16), LMX was significantly related to pre-manipulation reports of job satisfaction (R^ = .35, g < .01), intention to quit (Rf = .23, g < .01), organizational commitment (R^ = .40, g < .01), in-role performance (Rf = .07, g < .01), OCBI (R^ = . 10, g < .01), and OCBO (R^ = .03, g < .05). LMX was unrelated to either dimension of ORB or an ORB composite. In the second regression model (Table 17), LMX was positively related to distributive justice (Rf = .30, g < .01).
In step three of the mediation analyses (Table 18), distributive justice was entered into the regression model first, followed by LMX. Table 19 shows the
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the inclusion of distributive justice. Distributive justice was unrelated to OCBO, therefore no mediation could exist. There were substantial reductions in the amount of variance accounted for by LMX in job satisfaction, intention to quit, and
organizational commitment after the inclusion of distributive justice. There were more modest reductions in the variance accounted for by LMX in performance and OCBI after the inclusion of distributive justice. These results provide partial support for Hypothesis 6.
The Mediation Effect of Procedural Justice
The first step of the mediator analysis for procedural justice was identical to that for distributive justice (Table 20). In the second regression model (Table 21), LMX was positively related to procedural justice (R“ = .28, p < .01).
In step three of the mediation analyses (Table 22), procedural justice was entered into the regression model first, followed by LMX. Table 23 shows the
reduction in variance accounted for be LMX in the outcome variables before and after the inclusion of procedural justice. Procedural justice was unrelated to performance and OCBO, therefore no mediation could exist. There were substantial reductions in the amount of variance accounted for by LMX in job satisfaction, intention to quit, and organizational commitment after the inclusion of procedural justice. There was a more modest reduction in the variance accounted for by LMX in OCBI after the inclusion of procedural justice. These results provide partial support for Hypothesis 7. Testing the Overall Model with Pre-Manipulation Data
Structural equation modeling was used to test the accuracy of the entire model. The value to this approach is that while statistically significant results may be found as
101 hypothesized, structural equation modeling allows for the testing of alternative
models as well as deriving empirically driven modifications. Since the parameters of interest in the model are the relationships between the latent variables and prior research has provided evidence of the psychometric properties of the scales used to measure these latent variables, a single indicator model was used for most variables to test the full model. Using single indicators increases the subjects to degrees-of-
freedom ratio, which provides more power to examine the structural relationships in the model. For each variable, the path from the indicator to the latent variable
(lambda) was set to the square root of the scale reliability. The error variance was set equal to the variance of the scale multiplied by one minus the reliability (Hayduk,
1987; Jbrsekog & Sorbom, 1989). This strategy to examine structural models in organizational behavior research is common (e.g., Bauer et al., 2001; Wayne et al.,
1997).
Structural equation modeling also allowed a test of the idea that both
distributive and procedural justice perceptions were influenced by an overall fairness judgment (Lind, 2001). Therefore, “overall fairness” is presented as a latent variable
that influences both distributive and procedural justice perceptions. Theoretically, this approach indicates that perceptions of distributive and procedural justice lead to an overall sense of fairness for individuals. It is this overall fairness perception that becomes a heuristic that influences perceptions of attitudes and behaviors (Lind, 2001).
Figure 23 represents the full, hypothesized model. This model did not fit the data well (Table 24). However, modification indices for this model suggested that the
102 path between dispositional trust and trust in leader be eliminated. This change also made theoretical sense since the average tenure of the relationship between members and leaders was over one year. In dyadic relationships of this length, it is likely that person specific information would be much more salient that one’s disposition to trust others. This change did not result in a better fitting model (Table 24). Since it is more likely that in the presence of general injustice (i.e., no specific person is responsible for the injustice), individuals are more likely to withhold helpful behaviors from the organization rather than co-workers, and more likely to engage in behaviors harmful to a “faceless” organization than to co-workers, Model 4 eliminated both OCBO and ORBI. Additionally, overall fairness did not have a statistically significant relationship with either OCBO of ORBI Model 4 fit the data moderately well.
The next model (Model 5) tested added paths between outcome variables that have shown consistent relationships in prior research. Therefore, job satisfaction, intention to quit and organizational commitment were allowed to co-vary, and a path between performance and OCBI was added to the model (Figure 25). This model fit
the data very well.
The last two models tested were based on the strong positive relationship between trust in supervisor and LMX. Rather than the hypothesized model in which trust in supervisor was related to LMX and then LMX was related to overall fairness, a direct path between trust in supervisor and overall fairness was added (Figure 24). This model did not fit the data well. The final model tested reflected the elimination of dispositional trust, OCBO, and ORBI from the hypothesized model. This final model also reflects the addition of paths between job satisfaction and intention to quit and
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