Is My Team as Passionate as Me? Effects of Perceived Dissimilarity of Occupational Passion-Intensity on Key Outcomes Among Working Adults
Bibliographic record
Abstract
This dissertation explores the impact of perceived self-to-team dissimilarity in occupational passion-intensity on individual-level outcomes among workgroup members in organizational contexts. Grounded in need-to-belong theory, moralization theory, and equity theory, the outcomes examined include job satisfaction, turnover intentions, involvement in task-conflict, and psychological safety. Additionally, results via three methods for measuring self-to-team similarity and/or dissimilarity were compared. Two self-report online questionnaires separated by one week were administered to 530 adult workers from the US, Canada, and the UK, across a wide variety of industries. Based on a final sample of N = 483, structural equation models—each corresponding to one method for measuring self-to-team (dis)similarity—were retained after minimal respecifications of correlated residuals. Local fit and global fit were acceptable for each model (CFI > .950, RMSEA ≤ .052). Findings revealed that the effects of perceived self-to-team (dis)similarity were statistically significant—though the size of those effects were somewhat small across this dissertation’s models, which controlled for one’s occupational passion-intensity and perceived self-to-team general similarity and/or dissimilarity. Further, in comparing the effects via the three methods for measuring self-to-team similarity and/or dissimilarity, results showed small differences between the direct similarity versus the direct dissimilarity method, except for the differential effects on psychological safety which were much stronger using the direct dissimilarity method. Locally estimated scatterplot smoothing (LOESS) curves showed the direct similarity method produced relatively straight relationships with all outcome variables, whereas the direct dissimilarity method produced noticeably curved relationships (resembling a quadratic function) with job satisfaction and turnover intentions. Furthermore, the indirect method produced stronger effects than either of the direct methods, though other mathematical techniques for comparing these two types may yield different conclusions. Implications are discussed for theory on occupational passion, as well as praxis on how to staff and train workgroups.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".