The antecedents of perceived value in the Airbnb context
Bibliographic record
Abstract
Purpose This study aims to examine the antecedents of perceived value in the Airbnb context using the variables of perceived benefits (i.e. monetary saving, hedonic benefit, novelty and social interaction) and perceived risks (i.e. performance, physical, psychological and time). Design/methodology/approach The study population was Airbnb users in South Korea. This study applied a survey research method using a questionnaire. A link to the survey was sent via e-mail to panel members of a multinational research company. Findings The results revealed the positive influence of monetary saving, hedonic benefit and novelty on perceived value and the negative influence of psychological risk on perceived value. Research limitations/implications The results of this study, which identified the specific factors that influence Airbnb users’ perception of value, can assist Airbnb managers and Airbnb hosts in developing appropriate marketing plans and strategies to enhance the value of their offerings. Originality/value This study provided empirical support to the inclusion of affective factors and risk in determining perceived value. Moreover, while previous Airbnb studies focused on consumers from Western countries (e.g. USA and Canada), this study used a sample of South Korean consumers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".