Supervisors' interpersonal styles: An integrative perspective and a measure based on self‐determination theory
Bibliographic record
Abstract
Abstract This research addresses recent calls for an alternative integrative framework to apprehend leaders' behaviors and examines the validity ofaquestionnaire anchored in this theoretical approach. Building upon Self‐Determination Theory, we examined a tripartite approach of supervisors' behaviors (supportive, thwarting, and indifferent toward subordinates' psychological needs). The psychometric properties of this Tripartite Measure of Interpersonal Behaviors‐Supervisor (TMIB‐S) were tested through three studies. Results from bifactor exploratory structural equation modeling supported a solution including one global factor and three specific factors reflecting need supportive, thwarting, and indifferent behaviors. This solution was fully invariant across distinct samples of French‐ and English‐speaking employees. Results also supported the criterion‐related and discriminant validity of the TMIB‐S. More specifically, results supported the added‐value of the TMIB‐S, when compared to well‐established measures of leadership (passive leadership, abusive supervision, LMX, and tranformational leadership) in predicting well‐ and ill‐being. Results also highlighted well‐differentiated effects of the different components of supervisory behaviors and showed that supervisors' need indifferent behaviors constitute a key piece in the prediction of employees' health‐related consequences.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".