Star Light, but Why Not So Bright? A Process Model of How Incumbents Influence Star Newcomer Performance
Bibliographic record
Abstract
Star performers do not always sustain their performance edge after moving to new organizations. We offer an important explanation for whether star performers may flourish or flounder when they join a new team. Integrating insights from attribution theory and social comparison research, we present a process model explaining how incumbents make sense of star newcomer status. We propose that incumbents determine whether the star newcomer attained their star status through internal factors (e.g., ability, grit) or external factors (e.g., luck, affiliation), which gives rise to one of four distinct emotions: optimism, inspiration, shame, or envy. Incumbents who feel optimism or inspiration attempt to enhance their status through learning or helping behaviors that subsequently facilitates star newcomer performance, whereas incumbents who feel shame or envy seek to protect their status through withdrawal or destructive behaviors that hinder star newcomer performance. We further propose that the effect of incumbent behaviors on star newcomer performance is particularly likely when there is a high need for task-related interactions and complementary skills among team members. Our theoretical framework helps to advance the star newcomer literature by examining the largely overlooked influence of incumbents and highlighting the cognitive process that precedes their behavior towards star newcomers.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".