Getting Everyone on Board: The Role of Inspirational Leadership in Geographically Dispersed Teams
Bibliographic record
Abstract
A rich body of research in the area of leadership has examined the influence of transformational/charismatic forms of leadership on employees' motivation, attitudes, and behaviors. This research is based on the assumption that leaders are able to influence followers based on close, sustained, and personalized contact with them. However, new organizational realities are challenging this assumption. Drawing on the intersections between social identity theory and leadership research, this study highlights the importance of inspirational leaders who, by developing socialized relationships with team members, can foster attitudes that are critical for team effectiveness in geographically dispersed settings. Findings support the role of this form of leadership in dispersed settings. Inspirational leadership emerged as a significant predictor of individuals' trust in team members and commitment to the team. Further, the positive relationship between inspirational leadership and individuals' commitment to the team and trust in team members was strengthened in teams that were more dispersed suggesting that inspirational leaders are important in all contexts but that their importance is underscored in highly dispersed contexts. Finally, shared perceptions of trust and commitment predicted performance at the team level.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".