Newcomers’ motivation profiles: a review and longitudinal investigation
Bibliographic record
Abstract
Anchored in a comprehensive review of person-centred research on work motivation profiles, this study sought to validate theoretical scenarios likely to drive employees’ work motivation. Relying on a longitudinal sample of 865 Canadian employees who started their job within the last six months, we assessed newcomers’ motivational profiles, and their within-sample (generalizability over time) and within-person (profile membership and transitions) stability over a six-month interval. We then assessed associations between these profiles, need supportive and need thwarting work conditions (predictors), and outcomes (turnover intention, emotional exhaustion, job engagement, and performance). Our results revealed five profiles (Weakly Motivated Value-Driven, Self-Determined Value-Driven, Weakly Motivated/Amotivated, Strongly Motivated, and Self-Determined Hedonist), mainly consistent with the proposed scenarios. We also found that participants reporting more need supportive behaviours were more likely to belong to the Strongly Motivated and Self-Determined Hedonist profiles, while those reporting more need thwarting behaviours were more likely to belong to the Weakly Motivated Value-Driven, Self-Determined Value-Driven, and Weakly Motivated/Amotivated profiles. Finally, whereas more self-determined profiles tended to report more adaptive outcomes (i.e. lower emotional exhaustion and turnover intention, and higher job engagement and performance), results also revealed unexpectedly high levels of emotional exhaustion and turnover intention among the Strongly Motivated profile.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".