Understanding Online Reviews in All-Inclusive Hotels Servicescape: A Fuzzy Set Approach

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AQIRAGFCP" target="_blank" >RIV/00216208:11320/23:QIRAGFCP - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146989974&doi=10.1080%2f1528008X.2023.2167761&partnerID=40&md5=c24b2c58cebda040261930f7c22ace77" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85146989974&doi=10.1080%2f1528008X.2023.2167761&partnerID=40&md5=c24b2c58cebda040261930f7c22ace77</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1080/1528008X.2023.2167761" target="_blank" >10.1080/1528008X.2023.2167761</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Understanding Online Reviews in All-Inclusive Hotels Servicescape: A Fuzzy Set Approach

Popis výsledku v původním jazyce

"The concept of all-inclusive (AI) hotel service is complex and multidimensional; grounded on the SERVQUAL model, this research aims to identify the main components of AI service that moderate online ratings. Aside from the dimensions comprised in the SERVQUAL model, this research discusses price, place, and experiences as essential components in AI hotel ratings. This research was applied in the Algarve, a mature sun and sea destination with more than thirty all-inclusive hotels in operation since 2008. A fuzzy set qualitative analysis (fsQCA) approach is used from 6742 validated reviews of four- and five-star all-inclusive hotels retrieved from TripAdvisor between August 2003 and April 2019. Single constructs in the AI ecosystem have no impact as a rating enhancer on TripAdvisor, with two or more constructs necessary to trigger the goal. This research also contributes by applying a fuzzy set approach to the extracted user-generated content (UGC) regarding AI hotels on TripAdvisor and revealing the necessary and sufficient conditions through the identified constructs that most contribute to high online ratings. © 2023 Taylor & Francis Group, LLC."

Název v anglickém jazyce

Understanding Online Reviews in All-Inclusive Hotels Servicescape: A Fuzzy Set Approach

Popis výsledku anglicky

"The concept of all-inclusive (AI) hotel service is complex and multidimensional; grounded on the SERVQUAL model, this research aims to identify the main components of AI service that moderate online ratings. Aside from the dimensions comprised in the SERVQUAL model, this research discusses price, place, and experiences as essential components in AI hotel ratings. This research was applied in the Algarve, a mature sun and sea destination with more than thirty all-inclusive hotels in operation since 2008. A fuzzy set qualitative analysis (fsQCA) approach is used from 6742 validated reviews of four- and five-star all-inclusive hotels retrieved from TripAdvisor between August 2003 and April 2019. Single constructs in the AI ecosystem have no impact as a rating enhancer on TripAdvisor, with two or more constructs necessary to trigger the goal. This research also contributes by applying a fuzzy set approach to the extracted user-generated content (UGC) regarding AI hotels on TripAdvisor and revealing the necessary and sufficient conditions through the identified constructs that most contribute to high online ratings. © 2023 Taylor & Francis Group, LLC."

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

"Journal of Quality Assurance in Hospitality and Tourism"

ISSN

1528-008X

e-ISSN

—

Svazek periodika

""

Číslo periodika v rámci svazku

2023

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

28

Strana od-do

1-28

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85146989974