ABSTRACT
The purpose of this two-wave longitudinal study was to examine the associations between work engagement and workaholism to better understand the psychological mechanisms underpinning high levels of work investment. These associations were examined in a sample of 514 employees using latent change models, allowing us to obtain a direct and explicit estimate of change occurring in both constructs over a 3-year period. These analyses relied on a bifactor representation of work engagement and workaholism, allowing us to properly disaggregate the global and specific levels of both constructs in the estimation of these longitudinal associations. To further enrich our theoretical understanding of the mechanisms at play in these relations, we also considered associations between these two constructs and employees’ levels of harmonious and obsessive work passion, two other facets of heavy work investment. Our results revealed the longitudinal independence of employees’ global levels work engagement and workaholism, showing that longitudinal associations between these two constructs occurred at the specific, rather than global, level. Harmonious work passion was only found to be associated to global and specific components of work engagement, whereas obsessive work passion was found to be associated with global and specific components of both work engagement and workaholism.
Acknowledgements
The first author was supported by a Horizon Postdoctoral Fellowship from Concordia University in the preparation of the manuscript. Preparation of this paper was also supported by a grant from the Social Sciences and Humanities Research Council of Canada (435-2018-0368). The last author was supported by the Academy of Finland grant 320241 and 308351.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Notes
1 When comparing participants on all measures as a function of the number of time points completed, no significant differences (all ps > .08) emerged between participants who completed one or two time points. However, attrition is less concerning under the missing at random (MAR) assumption which is robust to attrition-related differences on all key study variables and allows the probability of missingness of any variable to be conditioned on all latent and observed variables included in the model (Enders, Citation2010).