Proactive Handling of Flight Overbooking: How to Reduce Negative eWOM and the Costs of Bumping Customers
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
This research examines the extent to which proactivity in handling flight overbooking reduces negative electronic word-of-mouth (NeWOM) and the required costs of compensation, thus increasing firm profitability. It answers recent calls to use a multimethod approach (i.e., we include archival data, qualitative interviews, seven experiments, and a Monte Carlo simulation for a total of 10 studies) and to adapt recovery to specific contexts (i.e., airlines) and heterogeneous customers (i.e., voluntary/involuntary bumping or offloading). The preliminary studies indicate that overbooking and offloading are pervasive and that a proactive approach is both feasible and desirable. The experiments show that, compared to the default reactive approach (informing passengers at the gate), a proactive approach (informing them before they leave for the airport) substantially reduces NeWOM and the sought compensation. Further, a very reactive approach (informing them in the plane) significantly increases NeWOM and the sought compensation, especially when offloading occurs involuntarily. We also unveil the mechanism explaining the effects of proactivity on NeWOM, through the serial mediation of justice and betrayal. Finally, the results of a Monte Carlo simulation show that offering reduced compensation through a proactive approach allows more aggressive overbooking, higher capacity utilization, and increased net revenue of up to 1.3%.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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