Managerial Style – A Literature Review and Research Agenda
Bibliographic record
Abstract
This literature review provides an overview of existing studies in the area of managerial style and its effect on firms’ strategic decisions and performance. It highlights which managers’ characteristics have been considered as determinants for managerial style so far and provides potential avenues for future research. After analyzing the content of all articles that were published in seven top-tier journals in the area of finance and banking between 2000 and 2016, the articles on managerial style were included in this literature review and categorized according to the main manager characteristic of investigation. The paper illustrates how similar characteristics are measured differently, and how different measurements of manager’s influence the managerial style−firm strategy relationship differentially. We provide avenues for future research in the area of managerial style, that is, future research may investigate board member’s characteristics at a more aggregated level (board level). Also, future research may shed more light on the argumentation of whether managers’ individual style influences the firm’s corporate decision or whether managers endogenously choose the firm due to their individual characteristics that match with the firm’s strategy and vice versa. This study is interesting for firms that aim to find a manager or director who fits well to its own strategy. Although there is a rapidly growing literature on managerial style, there is yet no literature review that analysis research themes and strings on managerial style in finance journals.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".