Top management team means-ends diversity and competitive dynamics
Bibliographic record
Abstract
We examine how top management team (TMT) members' disagreement about strategic means and ends – means-ends diversity (MED) – affects firms' propensity to take competitive action in the context of fungible versus non-fungible resources. Theorizing in part from TMT diversity literature, we contribute to competitive dynamics and upper echelons research by demonstrating how top team MED shapes competitive outcomes. Contrary to common assumptions, our results suggest that such diversity can inhibit rather than promote competitive propensity. Importantly, we argue that firm resource profiles are pivotal in moderating this relationship. To be specific, we find that multipurpose fungible resources like slack augment this suppression, whereas non-fungible strategic investments galvanize action and do the opposite. Moreover, we find that too weak and too great a propensity for competitive action diminishes firm performance. Theoretical contributions and research implications are discussed. • Top management team means-ends diversity (MED) inhibits competitive propensity (CP). • Fungible slack resources augment the suppression of MED to CP. • By contrast, committed strategic investments reduce the suppression, and hence galvanize CP. • CP has an inverted-U-shaped effect on firm performance.
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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 | 0.002 | 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".