Applying Importance-Performance Analysis for Improving Internal Marketing of Hospital Management in Taiwan
Bibliographic record
Abstract
Importance-performance analysis enables management to evaluate and identify the major strengths and weaknesses of a hospital’s key success factors. The author attempts to understand employee expectations and perceptions of hospital internal-marketing and shows the usefulness of the Importance-performance analysis grid in evaluating hospital internal-marketing benefits from employee perspectives in Taiwan. The author identified a list of 18 items from the internal-marketing literature reviews, and each item was rated using a 5-point Likert scale. Responses were obtained from 257 usable questionnaires. The importance-performance grid shows that 4 items fall into the “Keep up the good work” quadrant, 5 items fall into the “Concentrate here” quadrant, 4 items fall into the “Low priority” quadrant, and 5 items fall into the “Possible overkill” quadrant. The findings suggest that an internal-marketing orientation is necessary to better match organizational characteristics and enhance service quality. The results are useful in identifying areas for strategic focus to help hospital managers develop internal-marketing strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".