F. Ladstätter et al.
burnout in human services has three dimensions: emotional exhaustion, depersonalization, and lack of personal
accomplishment. Based on these three dimensions, the experience of burnout can be alleviated by the availabili-
ty of personal resources.
Ever since Kobasa [3] introduced the concept of hardiness as an important personality characteristic affecting
the relationship between stressors and strains, many studies showed its relevance for health and performance [4].
Hardiness has been characterized by the three interrelated dimensions (3Cs) of commitment, control, and chal-
lenge. Extant evidence shows that hardy people perform better and stay healthier in the face of stress [5].
2. Method
The Nursing Burnout Scale—Short Form (NBS-SF) was used to evaluate the process of burnout. The survey of-
fers measures of specific job stressors in nursing as antecedents of burnout (16 items), burnout (12 items), har-
diness (12 items), and consequences of burnout (12 items), totaling in 52 items.
3. Results
Cronbach alphas were calculated to ensure the reliability of the NBS-SF scale (Table 1).
We used the k-means cluster analysis to categorize participants on the basis of their mean z-scores on each of
the hardiness dimensions. Clusters that would make sense if obtained according to the existing theoretical back-
ground on the role of hardiness include patterns comprising above average or below average scores on each har-
diness dimension. The mean z-scores of each hardiness dimension by cluster and the percentage of participants
in each cluster are reported in Table 2. Cluster 1 consists of hardy individuals and cluster 2 of non-hardy indi-
viduals. To confirm that the clusters extracted from the data indeed consist of individuals with different charac-
teristics besides their distinct hardiness profile, comparisons between clusters were conducted to determine
whether or not they were higher on variables that have been proposed to be associated with hardiness, using
one-way ANOVA. Mean scores on all measures for both clusters are shown in Table 1.
In order to further improve our understanding of how hardiness produces its effect and, especially, to show
that the relationships between the stressors and strains, as well as the moderating effect of hardiness are nonli-
near, artificial neural networks, a relatively new methodology that was found to be superior to regression in nu-
merous problem domains [6] was used. Specifically, a three-layer feed-forward network was used for the hardi-
ness model approximation. After training and simulation of the network, a linear regression was performed be-
tween the network outputs and the desired outputs for each of the three types of consequences, separately and
combined.
To assess the predictive capacity of the burnout model, a network validation was executed after the training.
All results including t-test statistics and their significance levels are summarized in Table 3. The t-test was used
to compare the predicted outcome of the neural network with the desired outcome.
Visual analyses revealed that the relationships between stressor variables (antecedents), burnout, and conse-
quences were not only nonlinear but also different for hardy and non-hardy individuals. Figure 1 exhibits a
contour plot in which physical consequences are shown as a function of work overload and contact with death
and pain for hardy and non-hardy individuals.
The contour plot graphically illustrates that the stressors role ambiguity and contact with death and pain are
not linearly related to organizational consequences because the surface is not a flat plane but instead a curved
surface. It furthermore reveals the nonlinear influence of hardiness in this relationship because the curved sur-
face displays different shapes for hardy and non-hardy individuals.
4. Discussion
The clusters we identified are suggestive of possible moderational effects on the relation observed between
stressors and strains. Specifically, hardy people who score above average on all hardiness dimensions score be-
low average on all stressors, burnout dimensions and on psychological consequences. Non-hardy individuals
who score below average on all hardiness dimensions score above average on all stressors, burnout dimensions
and on organizational and physical consequences (see Table 1).
The neural network analysis revealed nonlinear relations (Figure 1) between the stressors and strains as well
as a moderational effect of hardiness.
