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 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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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 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".