Strategic decision‐making in the healthcare industry: the effects of physician executives on decision outcomes
Bibliographic record
Abstract
Purpose Research on strategic decision‐making has emphasized the importance of team decision‐making as it brings the benefits of synergy. Literature on healthcare is silent on the role of professional doctors in the strategic decision‐making process and their impact on decision outcomes. The purpose of the present paper is to empirically examine the outcomes of decisions when physician executives were involved in strategic decision‐making process in healthcare organizations. Design/methodology/approach Using a structured survey instrument, this paper gathered data from 361 senior executives from 109 hospitals in USA and analyzed the data using regression techniques on whether the presence of physicians in strategic decision‐making processes enhanced decision quality, commitment, and understanding. Findings Results showed the presence of professional doctors in the decision‐making process enhances commitment and decision quality in healthcare organizations. Research limitations/implications Only the healthcare industry was considered. Self‐report measures may have some inherent social desirability bias. Practical implications This study contributes to both practicing managers as well as to strategic management literature. This study suggests that healthcare administrators need to engage physician executives in strategic decision‐making to have successful decision outcomes. Originality/value To the extent strategic decision‐making process is similar in other industries, the findings can be generalizable across other industries.
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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 | 0.009 | 0.054 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".