Host–Tourist Relationship Quality in Evaluating B&B: The Impacts of Personality Traits and Emotional Labor

With the development of Taiwanese society, the tourist B&B industry has become particularly important, marking the origin of the significant development of the tourism industry. This study focuses on the quality of host–tourist relationships in B&Bs. It is proposed that tourists “discover” the emotions and feelings of B&B hosts through service contact processes. Although researchers have pointed out that frontline service employee personality traits affect the quality of interactions and satisfaction from the consumers’ point of view, very few studies have investigated the relationships between tourists and B&B hosts, the latter playing a double role—both as a host and a service worker. Data were collected from 422 tourists who had utilized B&B services. A quantitative analysis of the questionnaires was conducted through descriptive statistics, K-means clustering, one-way ANOVA and structural equation modeling (SEM), in order to determine the relationships among the three sets of variables. The results of this study reveal that the personality traits of B&B hosts directly affect their emotional labor and the quality of their relationships with tourists. However, the emotional labor of B&B hosts is found not to affect the quality of relationships; in this respect, our findings go counter to those of previous studies.

Authors

Institutions

Publication Details

Journal
Tourism and Hospitality
Published
2025-03-22
DOI
https://doi.org/10.3390/tourhosp6020050
Citations
2
Primary Topic
Emotional Labor in Professions
Type
article
Field-Weighted Citation Impact
8.40
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Host–Tourist Relationship Quality in Evaluating B&B: The Impacts of Personality Traits and Emotional Labor

Shao-De Liu, Shih-Yen Lin, Wei-Ning Winnie Chang
2 citations
Tourism and Hospitality
Emotional Labor in Professions
8.40
article

Host–Tourist Relationship Quality in Evaluating B&B: The Impacts of Personality Traits and Emotional Labor

Shao-De Liu, Shih-Yen Lin, Wei-Ning Winnie Chang
article en
2 citations

Abstract

With the development of Taiwanese society, the tourist B&B industry has become particularly important, marking the origin of the significant development of the tourism industry. This study focuses on the quality of host–tourist relationships in B&Bs. It is proposed that tourists “discover” the emotions and feelings of B&B hosts through service contact processes. Although researchers have pointed out that frontline service employee personality traits affect the quality of interactions and satisfaction from the consumers’ point of view, very few studies have investigated the relationships between tourists and B&B hosts, the latter playing a double role—both as a host and a service worker. Data were collected from 422 tourists who had utilized B&B services. A quantitative analysis of the questionnaires was conducted through descriptive statistics, K-means clustering, one-way ANOVA and structural equation modeling (SEM), in order to determine the relationships among the three sets of variables. The results of this study reveal that the personality traits of B&B hosts directly affect their emotional labor and the quality of their relationships with tourists. However, the emotional labor of B&B hosts is found not to affect the quality of relationships; in this respect, our findings go counter to those of previous studies.

Tourism and HospitalityVol. 6(2)
National Chi Nan University (TW)
Openalex Percentile: Top 4%
Emotional Labor in Professions
8.40
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.