Role of affective forecasting in customers’ hotel service experiences
Bibliographic record
Abstract
Purpose Research on the role of affective forecasting in hotel service experiences is in its infancy, and several crucial questions remain unanswered. This study aims to posit that affective forecasting is a significant antecedent of customers’ affective reactions during a hotel stay. The authors investigate how customers’ service quality expectations influence their affective forecasting and how customers’ affective forecasting before an upcoming hotel service experience influences their affective reactions during the hotel service experience. Design/methodology/approach The authors collected data through online questionnaires distributed among 634 US adults who had stayed at a hotel within the past month. Findings The results show that: service quality expectations influence affective forecasting; affective forecasting influences affective reactions; service quality expectations influence perceived service quality, thereby influencing affective reactions and affective reactions and service quality perception influence electronic Word-Of-Mouth intentions. Practical implications The study suggests that hotel managers should identify what hotel performance attributes customers value most and depict how these attributes elicit positive affective reactions in advertising to influence customers’ purchase decisions. Originality/value This is one of the few studies to investigate the antecedents and consequences of affective forecasting in hotel service experiences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".