Toward an Improved Understanding of Work Motivation Profiles
Bibliographic record
Abstract
The present research proposes an improved understanding of work motivation by identifying employees’ motivational profiles while taking into account the dual global and specific nature of work motivation proposed by self‐determination theory (SDT). To document the construct validity of these latent profiles, we relied on the circumplex model of employees’ well‐being to investigate whether they differed in terms of burnout, work satisfaction, and work addiction. Results from analyses conducted among a sample of 955 employees revealed five distinct profiles characterized by differing levels of global and specific forms of motivation: Intrinsically Motivated, Poorly Motivated, Driven, Conflicted, and Self‐Determined. Lower levels of burnout and work satisfaction were associated with profiles characterized by higher global levels of self‐determination and more autonomous forms of motivation, matching theoretical expectations. Interestingly, work addiction was highest in the Driven profile and lowest in the Self‐Determined profile, suggesting that autonomous forms of motivation are not always able to buffer the adverse effects of controlled forms of motivation. Our results also suggest that the specific qualities of work motivations are just as important as the global levels of self‐determination in the identification of work motivation profiles.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".