Impact of e-leadership and team dynamics on virtual team performance in a public organization
Bibliographic record
Abstract
Purpose This study examines the effect of various attributes of leadership and teams, modeled as perceived e-leadership and perceived team dynamics on virtual team (VT) performance in a public organization. Design/methodology/approach Using a survey instrument, data were collected from 184 participants involved in a virtual workplace from one of the largest Canadian public organizations. This study uses PLS-SEM software and quantitative methods. Findings This research identified that perceived team dynamics, which includes team member behavior, collaboration and support, has a significant medium effect on VT member performance. However, perceived e-leadership, which includes leaders' trust, leader communication/co-ordination and leader behavior, has a significant small effect on VT performance. Originality/value This study contribute to literature on VTs and VT's performance specially in public organizations. As the existing literature on employee performance has mainly focused on private organizations, and more so on VTs. However, little is known about VTs in public organizations and specifically about their performance.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".