Risk Reduction in Online Flight Reservation: The Role of Information Search
Bibliographic record
Abstract
The air industry is very competitive. Operating costs are high. To survive and earn profits, it is imperative for airlines to save substantial costs. This can come from selling tickets online. However, in many developing countries including Malaysia, a lot of consumers still refuse to buy flights online. Perceived risk of the internet is a key likely reason. To overcome this resistance, reducing perceived risk is crucial. This study examines the influence of perceived risk and risk-relievers on intention to reserve flight online. The two risk-relievers investigated are information type and personal sources of information. Using data collected online, PLS-SEM analysis was conducted to examine the relationship between type of information, personal sources of information, perceived risk, and intention to reserve flight online. Information type is found to relieve perceived risk and increases online reserve intention. Surprisingly, the results for personal sources of information show otherwise. In addition, perceived risk is empirically supported as being multidimensional. The findings suggest the importance of managing information to relieve risk perceptions so that airlines can stimulate higher online reservations. Thus, better profits can be made in spite of a competitive business environment.
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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 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".