Examining the Influence of Service Recovery Satisfaction on Destination Loyalty: A Structural Equation Modelling
Bibliographic record
Abstract
Total tourists’ holiday experiences to a certain extent are affected by their experiences staying at a hotel. Although service providers strive for “doing it right” the first time, but service failures are inevitable in the hospitality industry. Most service providers usually undertake to recover the service failure, through efforts such as giving explanation, compensations and apology, when service failures occur. The main aim of this research is to explore the drivers of service recovery satisfaction among foreign tourists. Therefore, the primary focus of this research is to empirically test the performance of a pre-developed service satisfaction measurement in the hotel industry. The paper proposes a structural model of service recovery satisfaction on destination loyalty. Randomly-selected respondents from the population of international tourists departing from international airports were selected to be involved in the study. Initially, exploratory factor analysis (EFA) was performed to test the factorial validity of constructs. Confirmatory Factor Analysis (CFA), using AMOS, was used to test the goodness of the proposed hypothesised model designed to measure the performance of the identified factors as being attributed of service recovery satisfaction and destination loyalty. The results supported the proposed model: service recovery satisfaction has a significant influence on destination loyalty.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".