Culture Creation and Change: Making Sense of the Past to Inform Future Research Agendas
Bibliographic record
Abstract
This review presents comprehensive analyses of extant research on culture creation and change. We use the framework of culture creation and change ( Kim & Toh, 2019 ), which consists of three unique perspectives, to understand past research on the antecedents of cultures. The basis of the functionality perspective is that environmental changes shape cultures, and thus, the created cultures enable an organization to address the demands of its environments effectively. In contrast, the leadership perspective argues that leaders have disproportional influence on cultures, and when exercising such influence, they are often unsuccessful at creating functional cultures. The leadership perspective comprises two subperspectives—the leader-trait and cultural transfer perspectives. The leader-trait perspective argues that when creating cultures, leaders often overlook the functionality of cultures but rely heavily on their traits. The cultural transfer perspective suggests that leaders often recreate the cultures that they have experienced in the past. Building on this framework, we review 74 studies in 68 articles across multiple disciplines to widen our understanding of culture creation and change. We then present agendas for future research guided by a four-stage model and a theory of coordinated actions for creating functional cultures. Finally, we discuss methodological limitations in past studies and offer possible solutions.
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 | 0.043 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.026 | 0.071 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 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".