E-WOM and airline e-ticket purchasing intention: Mediating effect of online passenger trust
Bibliographic record
Abstract
The continues growth of online network is visible and obvious which extend the impact of electronic word of mouth (E-WOM) on different online platforms that has dramatically increased. Subsequently consumer buying decision will be affected as well. Online trust is a significant factor here due to its role in influencing airline e-ticket purchasing intention. The purpose of this research is to investigate the role of online trust on mediating the relationship between e-WOM and airline eticket purchasing intention. The online research questionnaire survey technique was used in this research to examine the dimensions on E-WOM, online trust, and purchasing intention on airline e-ticket purchasing intention towards 311 respondents. Purposive sampling techniques was used in this research and structural equation modeling was used to test the research hypotheses. The study results confirm that E-WOM has an impact on online trust and airline e-ticket purchasing intention. The findings of this research provide valuable information to future researchers and airline companies' marketers and managers who employ on booking system, also it is useful for individuals to pay more attention on the credibility of E-WOM and whether or not they should trust the source.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".