JOURNAL ARTICLE
Hostelquality: A methodology for assessing the quality of hostels.
Published In: Tourism & Hospitality Research, 2024, v. 24, n. 1. P. 34 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: de Moura Pavão Farias, Daniela; Valença, Marilia Nunes; Sobral, Marcos Felipe Falcão; Bezerra Ribeiro, Ana Regina 3 of 3
Abstract
This article focuses on the development and application of HOSTELQUALITY, a new methodology designed specifically to evaluate the quality of hostels, addressing the inadequacy of conventional hotel metrics for this sector. HOSTELQUALITY comprises 29 questions across ten dimensions—environment, leadership, security, cleanliness, location, social atmosphere, equipment, staff, price, and customers—and provides a five-level classification from Deficient to Excellent, along with graphical radar outputs. Applied to ten hostels in three Brazilian tourist cities, the method revealed that half of the hostels achieved a good rating, none reached excellence, and common weaknesses included staff training and environmental practices. The study suggests HOSTELQUALITY can aid hostel managers, travelers, institutions, and governments by offering a consistent, interpretable quality assessment tool, while noting limitations such as sample size and reliance on single respondents. Future research is encouraged to refine the methodology by exploring dimension correlations, incorporating behavioral and cultural variables, and integrating artificial intelligence for enhanced evaluation.
Additional Information
- Source:Tourism & Hospitality Research. 2024/01, Vol. 24, Issue 1, p34
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2024
- ISSN:1467-3584
- DOI:10.1177/14673584221133668
- Accession Number:174564486
- Copyright Statement:Copyright of Tourism & Hospitality Research is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Looking to go deeper into this topic? Look for more articles on EBSCOhost.