COVID-19 and Hotel Productivity Changes: An Empirical Analysis Using Malmquist Productivity Index
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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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".