Listing popularity on the peer-to-peer accommodation platform: the heuristic-systematic and uncertainty reduction perspectives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose Listing popularity indicates the public’s interest in a listing on peer-to-peer (P2P) accommodation platforms. Although listing popularity is crucial to the survival and development of the P2P accommodation platform, this issue has received limited attention in the tourism management discipline. Drawing upon the heuristic-systematic model and uncertainty reduction theory, this study aims to examine the impacts of host and property attributes on listing popularity. Design/methodology/approach The model was empirically validated using a data set of 6,828 listings on a popular P2P accommodation platform called Airbnb. This study chooses a hierarchical regression analysis to perform the model validation. Findings The findings reveal that host self-disclosure, host reputation and host identity verification are key host attributes in promoting listing popularity. Meanwhile, property visual description, property photo verification and property visual appeal are important property attributes in facilitating listing popularity. Research limitations/implications The study adds useful insights on understanding on determinants of listing popularity. Future researchers are recommended to empirically verify the underlying psychological mechanism by which host attributes and property attributes influence listing popularity. Practical implications The P2P accommodation platform should promote the listing popularity by taking advantage of the host attributes and providing property attributes. Originality/value First, to the best of the authors’ knowledge, this study is one of the few studies to explore the formation of the listing popularity. Second, this study examines how the host and property attributes promote the listing popularity through the heuristic and systematic information processing modes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 it