Top Management Team Compensation, Strategic Positioning, and Firms’ Competitive Effectiveness
Bibliographic record
Abstract
In this study, we investigate how the compensation structure of the top management team (TMT) affects the firm’s competitive effectiveness under different strategies. We delineate the TMT compensation structure along two dimensions - (1) the size of the CEO pay slice as a tournament incentive that motivates individual effort from each of the CEO's direct reports and (2) the degree of pay dispersion among the CEO's top team that affects inclination of the team to collaborate and coordinate. We adopt an innovative measure of firm competitive effectiveness that uses Data Envelopment Analysis (DEA) to determine a firm's relative efficiency in converting resources into revenues compared to the industry leader (Demerjian, Lev and McVay 2012). Using Miles and Snow's (1978, 2003) strategic typology to classify firms into Prospectors, Defenders and Analyzers, we find that the association between CEO pay slice and competitive effectiveness is more positive for firms following the Prospector strategy than for Analyzers. We also find that higher pay dispersion among the CEO's top team appears to be more harmful for both Prospectors and Defenders than for Analyzers.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".