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Record W4200526269 · doi:10.24135/hi.v5i2.111

The Great Resignation: stopping the 'bleed'

2021· article· en· W4200526269 on OpenAlexaboutno aff
Oliver Horn

Bibliographic record

VenueHospitality Insights · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCOVID-19 Pandemic Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsForce majeureHospitalityBusinessMindsetNoticeTourismExpatriateHospitality industryGovernment (linguistics)StaffingPublic relationsMarketingManagementPolitical scienceEconomicsLaw

Abstract

fetched live from OpenAlex

As hospitality businesses open up ‘post-pandemic’, the unavailability of qualified staff has become one of the biggest obstacles to businesses’ ability to take maximum advantage of the pent-up desire and need for travel.
 A study published by McKinsey in September 2021 under the headline “Great attrition or great attraction? The choice is yours”1 verbalised and quantified for the first time something that the hospitality industry around the globe is experiencing as businesses start their return to the ‘next normal’. The article explained in detail a mindset that has become commonplace both for employers and employees, and that will be troubling the industry for a while if not properly addressed.
 When Covid first brought the world to a standstill, the hospitality industry was one of the first and worst hit. Business came to a halt; many hotels and restaurants closed or decreased staffing levels as much as possible in order to cut expenses. In the developed world, this was done with the help of government programmes so that employees could access some kind of safety net. In developing countries, these safety nets often did not/do not exist. Many employers were ruthless, simply telling staff that they were no longer needed. ‘Thanks’ to many governments calling Covid-19 a “force majeure”, employers got around paying legally required compensation for terminating employees at short notice. Many of our colleagues, expatriate and local, found themselves literally ‘on the street’ within weeks of the pandemic ravaging the industry.
 Employers’ social responsibility to the communities in which they do business was one of the first victims of the pandemic. The understanding that “our staff is our most valuable asset” turned into pure semantics.
 Today, as these businesses celebrate that they are opening again, there is a surprising level of surprise among the most callous of employers that now they can’t find staff. The industry will have to come up with new ways of working if they want to attract colleagues back – the loss of trust and goodwill will have serious repercussions. To ‘make good’ on their actions, employers need to first understand how much they broke – initial observations show that they have not even started to understand what they did.
 What about people still employed? Shouldn’t they be lucky to still have a job? In the McKinsey study, 40% of participants who were still employed answered that they were at least somewhat likely to leave their job in the next 3–6 months; 64% of these claimed that they are planning to leave without a new job lined up.
 At the core of this is, I believe (and the study suggests), is a general disconnect between what employees are looking for and what employers think that employees are looking for. The pandemic has sent many of us into a survival mode, forcing actions that were purely transactional. Yet the hospitality industry, at its core, depends on people who care for others. Employers need to ask employees questions that show they care and rebuild the trust that has been lost due to their actions when the pandemic hit.
 As a member of a Vietnamese investment group that did exactly the opposite, that held on to employees at substantial cost to the enterprise and with employees at all levels ‘chipping in’ through unpaid leave to help keep everyone employed, I know first-hand that this has built a substantial amount of trust and our levels of attrition are substantially below the market average as other businesses reopen. Asking the right questions, listening to the answers and consistently responding with empathy and tangible action, not words, will be key to our success.
 Corresponding author
 Oliver Horn can be contacted at: Oliver.Horn@ihg.com
 Note
 
 McKinsey & Company, September 8, 2021, study conducted with 4,294 participants in the US, UK, Australia, Singapore and Canada. Available at: https://www.mckinsey.com/business-functions/people-and-organizational-performance/our-insights/great-attrition-or-great-attraction-the-choice-is-yours

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score0.525

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.254
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2021
Admission routes1
Has abstractyes

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