Workaholic and work engaged employees: Dead ringers or worlds apart?
Bibliographic record
Abstract
Building on Deci and Ryan's Self-Determination Theory and Meijman and Mulder's Effort-Recovery Model, the present study examined the nature, antecedents, and consequences of working hard (i.e., workaholism and work engagement) in a Dutch convenience sample of 1,246 employees. A confirmatory factor analysis showed that workaholism and work engagement were two largely independent concepts. Crossing these two concepts yielded four types of workers: workaholic employees, engaged employees, engaged workaholics, and nonworkaholic/nonengaged employees. MANOVA and subsequent ANOVAs were used to compare these four groups regarding their motivation, working hours, and levels of burnout. As expected, study results revealed that workaholic employees were driven by controlled motivation, whereas engaged employees were driven by autonomous motivation. Engaged workaholics were driven by both controlled and autonomous motivation. In addition, the results revealed that engaged workaholics spent most time on working. Unlike workaholic employees, engaged workaholics did not experience the highest levels of burnout, suggesting that high engagement may buffer the adverse consequences of workaholism. The present study emphasizes the importance of differentiating among at least three categories of employees who work hard: workaholic employees, engaged employees, and-for the first time-engaged workaholics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".