Bibliographic record
Abstract
Purpose The purpose of this paper is to develop and test a conceptual model of the relationships among the constructs of “service quality”, “emotional satisfaction”, and “behavioural intention” in the hospitality industry. Design/methodology/approach The study utilises a review of the literature to propose a conceptual model that postulates that: service quality is positively related to consumers' emotions; service quality is positively related to behavioural intentions; and consumers' emotions are positively related to behavioural intentions. Moreover, the model postulates that emotional satisfaction partially mediates the effect of service quality on behavioural intentions. The model is tested in an empirical study with data from a survey among 200 Canadian travellers. Findings All the hypothesised relationships are supported. The results confirm that service quality exerts both direct and indirect effects (through emotional satisfaction) on behavioural intentions. Research limitations/implications Future research should focus on the role of emotional satisfaction in service experience in a variety of settings. Originality/value The research provides valuable insights into the role of emotional satisfaction in the hotel service experience. Emotional satisfaction makes a significant contribution to the prediction of behavioural intentions (such as loyalty, word of mouth, and willingness to pay more).
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".