Behavioral Integration at 30: Progress, Pitfalls and Prospective Directions
Bibliographic record
Abstract
It has been nearly 30 years since Donald C. Hambrick published the seminal article entitled “Top Management Groups: A Conceptual Integration and Reconsideration of the ‘Team’ Label” that introduced the concept of behavioral integration. Defined as the degree to which the top management group “engages in mutual and collective interaction” (Hambrick, 1994: 188-189), behavioral integration has emerged as a unifying construct that captures the harmonisation of team processes at the strategic apex of the organisation. The article, which has been cited nearly 1500 times, significantly advanced upper-echelon research by opening up the black box of team process, allowing scholars to better explain the conditions under top management attributes are most likely to explain and predict strategic choice, behavior, and outcomes. The 30th anniversary of the article provides an opportune moment to reflect upon and synthesize the scholarly progress on the concept of behavioral integration, and to stimulate some new directions for future development of the concept.
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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 | 0.057 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.007 | 0.031 |
| Scholarly communication | 0.016 | 0.036 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.008 | 0.022 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".