thesis_Daniela Noethen_print final - Jacobs University

Preventing Knowledge Loss When Employees Expect to Leave
second step, we introduced the independent variables turnover intention and expected
involuntary turnover as well as PSS at the individual level to test Hypotheses 1 to 3 (Model
2).
To test Hypothesis 2b, i.e., if the relationship between expected involuntary turnover is
more strongly negative than the relationship between turnover intention and knowledge
sharing, we performed a contrast analysis for the respective coefficients in Model 2 (Enders &
Tofighi, 2007; Hox, 2002). A contrast is a composite hypothesis on a set of parameters that
postulates, for example, that parameters are equal. Contrasts are tested, automatically within
HLM 6.08, with an asymptotic chi-square test (Hox, 2002; Raudenbush & Bryk, 2002;
Tabachnick & Fidell, 2001). In our analysis, we tested the null hypothesis represented by the
contrast 1 * coefficient for turnover intention + (-1) * coefficient for expected involuntary
turnover = 0.
To test Hypothesis 4, we created two alternative models to test the interaction between
turnover intention and PSS (Model 3a), and expected involuntary turnover and PSS (Model
3b), respectively, by adding the respective interaction terms to Model 2. Following the
recommendation by Echambadi and Hess (2007), we did not mean-center the constituent parts
of the interaction term before entering it into the cross-product, as this procedure does not
mitigate the collinearity problem as assumed by many researchers. To further probe
significant interactions, we conducted simple slope tests for three values of supervisor
support: 1 SD above the mean, the mean, and 1 SD below the mean (Aiken & West, 1991;
Bauer & Curran, 2005; Preacher, Curran, & Bauer, 2006). To this end, we used the online tool
“Simple Intercepts, Simple Slopes, and Regions of Significance in HLM 2-way Interactions”
designed by Preacher, Curran, and Bauer, which is described in their article from 2006. The
interactions were then graphed accordingly.
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