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Enregistrement W4385415462 · doi:10.47992/ijmts.2581.6012.0281

Goa's Hospitality Industry: A Study on Status, Opportunities, and Challenges

2023· article· en· W4385415462 sur OpenAlexaboutno aff
Nigel Barreto, Sureshramana Mayya

Notice bibliographique

RevueInternational Journal of Management Technology and Social Sciences · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueDiverse Aspects of Tourism Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHospitalityTourismRevenueQuarter (Canadian coin)Hospitality industryPandemicMarketingState (computer science)Coronavirus disease 2019 (COVID-19)BusinessEconomic growthAdvertisingPolitical scienceGeographyEconomicsFinanceLaw

Résumé

récupéré en direct d'OpenAlex

Purpose: This study aims to learn more about the hospitality sector in Goa, India, including its current state, potential, and growth. The review plans to distinguish the variables that add to the progress of the cordiality business in Goa, as well as the difficulties it faces in adjusting to changing times and the effect of the Coronavirus pandemic. The concentrate additionally tries to feature the extraordinary elements of the Goan cordiality industry that put it aside from other traveler objections in India. Methodology: This study employs a secondary research strategy based on a review of previously published articles, reports, and data on the Goan hospitality sector. Google Scholar, ResearchGate, and official Goa Tourism Division publications are used in the study. The information examination centers around key execution markers, for example, inhabitance and room rates in the neighborliness business in Goa. Findings: Despite the challenges posed by the COVID-19 pandemic, this study reveals that Goa's hospitality industry has performed well in recent years. Hotels in Goa saw a significant rise in occupancy rates from 15% to nearly 55% in the final quarter of 2022. Moreover, the typical room rates expanded from Rs 4,500 to nearly Rs 7,000 every evening. Goa saw the greatest increase in hotel demand among India's level II cities, rising by 118% in April. In addition, the study predicts that Goa's hospitality industry will continue to expand in 2023, surpassing pre-pandemic levels of demand and increasing revenue for the state and local governments. Practical Implications: Students, researchers, and policymakers interested in Goa's hospitality industry will benefit greatly from this study's practical implications. The study gives a valuable understanding of the industry's current state and growth and development potential. It also emphasizes the Goan hospitality industry's distinctive characteristics that set it apart from other Indian tourist destinations. The discoveries of this study can help partners in the business, including hoteliers, financial backers, and policymakers, to pursue informed choices and to make the most of the amazing open doors introduced by the development of the travel industry in Goa. Also, the review gives bits of knowledge into the effect of the Coronavirus pandemic on the friendliness business in Goa and the actions that have been taken to adjust to the evolving conditions. Originality/Value: This study gives a canny investigation present status of the Goan Lodging Industry, featuring its advantages, future potential, extraordinary qualities, restrictions, open doors, and qualities for industry advertisers. Even though this report is based on secondary research, it could be improved by having in-person interviews with key stakeholders like hoteliers, tourists, locals, and other industry players. These people would be better able to share their actual experiences and give feedback from the ground up. By providing a more comprehensive understanding of the Goan lodging industry and its overall impact on the Indian tourism industry, such primary research would further enhance this study's value and originality. Policymakers, stakeholders in the industry, and investors interested in the Goan tourism industry who want to make informed decisions may benefit from this study's findings. Moreover, understudies, specialists, and the overall population can profit from this concentrate by acquiring a more profound comprehension of the housing business in Goa and its commitment to the more extensive travel industry. Paper type: Case Study

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)0,002
score de la tête « metaresearch » (Gemma)0,000
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 enseignants0,712
Score d'incertitude au seuil0,475

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0010,001
Communication savante0,0000,000
Science ouverte0,0010,000
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 Opus0,176
Tête enseignante GPT0,412
Écart entre enseignants0,236 · 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

Citations6
Publié2023
Routes d'admission1
Résumé présentoui

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