Measuring Overall Customer Experience in a Hospitality Collaborative Consumption Context
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
Measuring and managing customer experience is increasingly becoming a priority in the experience-laden hospitality context. With the growing desire of consumers for living more authentic experiences, the collaborative consumption (CC) model has gained significant popularity in this industry. However, to date, the underlying structure of customer experience in a hospitality CC context has not been uncovered and a generalizable quantitative measure is yet to be developed. To fill this gap, this research aims to develop and validate a scale for measuring customer experience in a hospitality CC context. Based on a sample collected from Airbnb customers, results yield a 17-item five-dimensional scale (Platform brand experience, Accommodation sensory experience, Social experience with the host, Platform responsive capacity, and Quality of interaction with the host). This work demonstrates that providing an excellent customer experience in a CC context stems from a customer-centred approach from all the involved parties. Theoretical and managerial implications are also presented, along with relevant research avenues.
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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.002 |
| Open science | 0.000 | 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