The Impact of Service Recovery Actions and Perceived Justice on Customer Satisfaction: Insights from Thailand's Private Hospitals
Bibliographic record
Abstract
The research explores service recovery strategies' impact on customer satisfaction, word-of-mouth (WOM), and revisit intention in private hospitals in Thailand, drawing upon the expectation confirmation theory and social exchange theory. Using a quantitative approach, data was collected via an online questionnaire from service users of private hospitals in Thailand, with 600 usable responses analyzed using Structural Equation Modeling (SEM) and multi-group analysis with SEM. The findings reveal that service recovery actions (SRA) and perceived justice (PJ) significantly influence customer satisfaction with service recovery, WOM, and revisit intentions. Focusing on tangible actions and perceived justice can enhance customer retention in private hospitals. Private hospitals should emphasize tangible actions and perceived justice to enhance customer satisfaction and retention. Additionally, tailoring service recovery efforts based on customers' varying experience levels within the healthcare sector is crucial. The research contributes valuable insights to the healthcare industry's understanding of service recovery strategies, offering guidance for marketing strategy planning and enhancing customer satisfaction and retention in private hospitals in Thailand.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".