
We build on a novel model of personality [PersDyn] that captures three sources of individual differences (here applied to neuroticism): (1) one's baseline level of behavior, affect, and cognitions (baseline); (2) the extent to which people experience different neuroticism levels (variability); and (3) the swiftness with which they return to their neuroticism baseline once they deviated from it (attractor strength). To illustrate the model, we apply the PersDyn model to the study of the relationship between neuroticism and emotional exhaustion. In the first study, we conducted a 5-day experience sampling study on 89 employees who reported on their level of state neuroticism six times per day. We found that higher levels of baseline neuroticism and variability were related to increased emotional exhaustion. Furthermore, we found an interaction effect between baseline and attractor strength: people with a high baseline and high attractor strength tend to experience a high degree of emotional exhaustion, whereas people with low levels of baseline neuroticism are less likely to suffer from exhaustion if their attractor strength is high. In the second study, we conducted a laboratory experiment on 163 participants, in which we manipulated state neuroticism via short movie clips. Although the PersDyn parameters were not related to post-experiment emotional exhaustion, the interaction effect between baseline and attractor strength was replicated. It is concluded that a dynamic approach to neuroticism is important in understanding emotional exhaustion.
DIMENSIONS, burnout, emotional exhaustion, DENSITY DISTRIBUTIONS, Social Sciences, dynamics, BF1-990, LIFE, PSYCHOLOGY, JOB-PERFORMANCE, personality, 5-FACTOR MODEL, BURNOUT, Psychology, neuroticism, TRAITS, BEHAVIOR, SITUATION
DIMENSIONS, burnout, emotional exhaustion, DENSITY DISTRIBUTIONS, Social Sciences, dynamics, BF1-990, LIFE, PSYCHOLOGY, JOB-PERFORMANCE, personality, 5-FACTOR MODEL, BURNOUT, Psychology, neuroticism, TRAITS, BEHAVIOR, SITUATION
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| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
