Effect of Trust Transfer on Purchasing Intention in Airbnb: A Perceived Value Perspective
Bibliographic record
Abstract
Today the sharing economy has rapidly developed into one of the most popular business models in the e-commerce space. Among them, Airbnb generated huge revenue in the second quarter of 2016, and since 2008 its value has risen to more than $30 billion. It is expected to generate $350 billion in revenue by 2025, and Airbnb is recognized as the largest accommodation-sharing company for people using the sharing economy. Therefore, this study aimed to investigate whether trust transfer in Airbnb affects product purchase intention. Specifically, this study refers to the Trust Building Model (TBM) based on previous studies and considers factors such as benefits (perceived reputation, perceived site quality, perceived information quality) and cost (risk of perceived web use). The effects of these factors on the transfer of trust were analyzed. As a result, all factors except the risk of perceived web use, which is an attribute of cost, had a significant effect on trust transition. Based on these results, this study suggests both theoretical and practical implications along with directions for future research.
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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 | 0.003 | 0.016 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".