Stuck in the Past? Leader Past Cultural Experience and Its Influences on Group Culture and Outcome
Bibliographic record
Abstract
The extant research on the antecedents of cultures posits that cultures result from internal and external changes (the functionality perspective of cultures) or leader idiosyncrasies (the leadership perspective of cultures). The current research seeks to integrate the two perspectives to propose another important, yet neglected, antecedent of cultures: a leader’s past cultural experience. Specifically, we theorize that group leaders transfer cultures from their former groups to the current groups, essentially enacting cultures on the basis of their past cultural experiences. Two studies, one in the field (Study 1) and one in the laboratory (Study 2), find that the current groups’ levels of cultural tightness are predicted by leaders’ experience with cultural tightness in their former groups in which they were followers. In addition, the transferred cultural tightness from the leaders’ former groups to the current groups in turn influences negative (counterproductive work behavior) and positive (promotive and prohibitive voice) forms of group deviance. The theoretical and managerial implications for the leadership and the culture literatures are discussed.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".