Trust transfer and the effect of service quality on trust in the healthcare industry
Bibliographic record
Abstract
Purpose – The purpose of this paper is to examine the effect of service quality (interaction, physical environment, and outcome quality) on trust, to investigate the trust transfer in the healthcare industry, to explore the moderating effects of image congruence and switching costs on the trust transfer, and to assess the effect of trust on patients’ willingness of recommendation. Design/methodology/approach – The research model was tested using data collected from 483 inpatients in 15 medium-to-large hospitals in Taiwan. Structure equation modeling with the latent interaction effect was employed to verify and validate the research model. Findings – The outcomes show that interaction quality and outcome quality positively influence patients’ trust in the original hospital. But the effect of environment quality on trust is not significant. Patients’ trust in the original hospital positively affects their trust in its allied hospitals. Furthermore, image congruence positively moderates the trust transfer. However, switching costs do not appear to moderate the trust transfer. The results also confirm that trust in the original hospital and its allied hospitals positively affect patients’ willingness to recommend allied hospitals. Research limitations/implications – Due to the chosen research approach, the 15 hospitals cannot represent all hospitals in Taiwan and the research outcomes may lack generalizability. Practical implications – The research results provide insight into how a hospital can improve and manage patients’ trust and the trust transfer. Originality/value – This study represents one of the few that empirically investigates trust and trust transfer in the healthcare industry and examines the moderating effects of image congruence and switching costs on the trust transfer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".