COVID-19 and Hotel Productivity Changes: An Empirical Analysis Using Malmquist Productivity Index
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 investigates the impact of COVID-19 on hotel productivity change using the Malmquist Productivity Index (MPI). For 26 U.S. hotel brands, productivity changes over 10 quarters from the first quarter of 2018 to the second quarter of 2020 were analyzed. After the COVID-19 outbreak, the investigated hotels’ productivity deteriorated. Decomposition revealed that, whereas technical efficiency change (EC) improved, technological change (TC) regressed, resulting in deterioration of the MPI. The investigated hotels’ EC-related practices included enhanced cleaning operations, partnering with a hygiene brand, cutting the workforce, and pay cuts. Practices related to TC included the adoption of new hygiene technology and setting a new standard at the organizational level through the formation of a global council and accreditation related to disinfection and hygiene. Our results show that though U.S. hotels are trying to improve their productivity by efficiently utilizing resources, frontier technology’s regress is decreasing productivity. Our results support the importance of investment in technology for productivity management. This research provides empirical evidence for the need for hotels to pursue technological advances to overcome the pandemic.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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