Customer loyalty in the hotel industry: the interplay of justice perceptions and satisfaction as a mediator: evidence from Nepal
Bibliographic record
Abstract
This study examines the influence of distributive, interactional and procedural justice on customer loyalty following service failure in the hotel industry, with customer satisfaction as a mediator. Recognizing service failure as a critical issue impacting satisfaction and loyalty, this study is based on a deductive research approach in which data were collected via structured questionnaires from 481 hotel customers in Far Western Nepal who had experienced a service failure within the preceding 6 months. The study employs a convenience sampling technique within a descriptive and correlational research design. Better and earlier integration of cognitive–affective–behavioral (CAB) model and social exchange theory, the results revealed that interactional justice exerts the strongest effect on customer loyalty, followed by distributive justice, while procedural justice has the least impact. Furthermore, customer satisfaction partially mediates the relationship between all three dimensions of justice and customer loyalty. The findings suggest that hotels in Nepal may prioritize interactional and distributive justice emphasizing empathy, clear communication and discount and rebate in their service recovery strategies to enhance customer satisfaction and retention. This research addresses a gap in the literature concerning service recovery, providing valuable insights for the local hotel industry in the Western part of Nepal. This study adds value in understanding justice theory within service failure contexts in a developing country.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".