Getting Ahead While Getting Along: Followership as a Key Ingredient for Shared Leadership and Reducing Team Conflict
Bibliographic record
Abstract
Followership and leadership provide two distinct but complementary sets of behaviors that jointly contribute to positive team dynamics. Yet, followership is rarely measured in shared leadership research. Using a prospective design with a sample of leaderless project teams, we examined the interdependence of leadership and followership and how these leader-follower dynamics relate to relationship conflict at the dyadic and team level. Supporting the reciprocity of leader-follower dynamics, social relations analyses revealed that uniquely rating a teammate higher on effective leadership was associated with being rated higher by that same person on effective followership. Additionally, team members with a reputation as an effective leader also tended to be viewed as an effective follower. As expected, team levels of leadership were tightly linked to team levels of followership. Connecting these results to relationship conflict at the dyadic level, we found that uniquely rating someone as an effective follower or an effective leader would decrease the likelihood of experiencing interpersonal conflict with that person and that having a reputation for effective followership or effective leadership relates negatively to being viewed as a conflict hub within the team. Finally, effective followership was significantly negatively related to team levels of conflict, but we did not find a significant relation between effective leadership and relationship conflict at the team level. Our results highlight that followership is not only a necessary ingredient for high levels of shared leadership to exist within a team, but it underpins more functional team interactions.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".