Macro-environment Influences on Health Service Strategy in Saudi Private Sector Hospitals: An Empirical Investigation
Bibliographic record
Abstract
The rapid changes in the Saudi private sector hospital environment have exerted significant pressures on the hospitals to pay attention to marketing strategies in general and health service strategy in particular. Therefore this research investigates the influence macro environment factors have on the health service strategy made by the hospital managers. This study proposes and tests a four factor macro environment model that explains the considerable variation in health service strategy in the hospitals. These factors include political/legal (P), economic (E), social/cultural (S), and technology (T). In order to explore this issue, a triangulation method was used to collect primary data through a questionnaire, which was administered in the private sector hospitals in the Western Region in Kingdom of Saudi Arabia (KSA) and, via in-depth semi- structured interviews with hospital managers and experts in the health services in KSA. All Saudi general private sector hospitals in Western Region were targeted in this research rather than a representative sample of these hospitals. A purposive sampling strategy was used to choose the participants in this research. In total, 120 senior managers (including general managers, administrative managers, medical managers, public relation managers, nursing manager, and out patients clinic managers) participated in this study. The results confirm significant differences in the influence of macro environment factors on health service strategy. Furthermore, the results show that the hospitals might benefit further by placing more emphasis on an integrated health service strategy and recognising the macro environment influences on their hospitals. The results also highlight several implications for future research in health services marketing and fill in several gaps in the existing literature on health services marketing.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".