Exploring nexus among sensory marketing and repurchase intention: Application of S-O-R Model
Bibliographic record
Abstract
The primary objective of the present research is to examine the impact of touch and gustatory stimuli on airline image and repurchase intention. Furthermore, the present research examines the moderating role of price fairness and mediation impact of airline image. The researchers gather data from the customers of PIA and Airblue travelling abroad from Islamabad International Airport, Karachi International airport and Lahore International airport by employing multi-stage sampling technique. Total of 576 questionnaires was distributed among the respondents, and the response rate was 68.9%. For the analysis of data received, the researcher employed PLS-SEM. The finding of the study confirmed the significant impact of touch and gustatory stimuli on airline image and repurchase intention. Findings of the study revealed the mediating role of airline image among touch, gustatory and repurchase intention was significant as well. At the end, perceived price fairness also moderated the relationship of airline image and repurchase intention. The present study fills the gap of limited studies conducted in the past regarding sensory stimuli in the context of the airline industry, the role of airline image as a mediator under SOR model and moderating impact of perceived price. Findings of the present study are helpful for policymakers and practitioners of the airline industry in Pakistan to develop the strategy by which they can retain their customers on international routes.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".