Booking hotels online: can scarcity messages mitigate the effect of a mediocre aggregated eWOM valence?
Bibliographic record
Abstract
Purpose This paper aims to examine the interplay of scarcity message type and aggregated electronic word-of-mouth (eWOM) valence in influencing consumer intentions in response to online hotel price promotions. Design/methodology/approach Two experimental studies were conducted, manipulating scarcity message type (limited-quantity vs. limited-time) and aggregated eWOM valence (positive vs. mediocre). Study 1 focused on budget hotels and Study 2 on midscale hotels. Data came from Amazon Mechanical Turk. Findings A positive aggregated eWOM valence always inspired greater confidence than a mediocre one. For midscale hotels, limited-quantity scarcity messages were more effective. However, the type of scarcity did not matter for budget hotels. Moreover, limited-quantity cues consistently worked better than limited-time cues in the mediocre aggregated eWOM valence condition. Practical implications A positive aggregated eWOM valence is obviously preferred to a mediocre one. That said, if a hotel ends up with a mediocre aggregated eWOM valence, it should use limited-quantity scarcity messages to tilt the balance in its favor. Originality/value This work responds to the call for research on the effect of online scarcity messages in tandem with eWOM. Also, it is the earliest attempt to reveal how scarcity messages fare differently for various hotel categories.
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How this classification was reachedexpand
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 | 0.004 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".