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

The Great Resignation: stopping the 'bleed'

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

Notice bibliographique

RevueHospitality Insights · 2021
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueCOVID-19 Pandemic Impacts
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésForce majeureHospitalityBusinessMindsetNoticeTourismExpatriateHospitality industryGovernment (linguistics)StaffingPublic relationsMarketingManagementPolitical scienceEconomicsLaw

Résumé

récupéré en direct d'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

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,752
Score d'incertitude au seuil0,525

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,051
Tête enseignante GPT0,254
Écart entre enseignants0,203 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations3
Publié2021
Routes d'admission1
Résumé présentoui

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