Bibliographic record
Abstract
ABSTRACT Upward influencers, employees who are more favorably perceived by their supervisors than their peers and subordinates, are predicted by economic and accounting theories and are found to be ubiquitous in many organizations. Despite their prevalence, the role of upward influencers in teams remains underexplored. This paper fills this void by using proprietary data from a service‐providing organization that allows for the identification of upward influencers based on its 360‐degree person evaluation. We find an inverted U‐shaped relationship between the fraction of upward influencers in a team and team performance. In cross‐sectional analyses, we show that this relationship is driven by conditions when the need for collaboration and information sharing is high and when managers are less experienced. Additional tests exploring the mechanisms for the role of upward influencers in teams suggest that they impair team horizontal relationships through lowering the willingness to communicate, share knowledge, and offer mutual assistance among team members. Yet, teams with upward influencers build better vertical relationships with supervisors, which, in return, is associated with supervisors allocating more of their time to provide team members with feedback and guidance. Taken together, this study contributes to the understanding of upward influencers in teams.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".