Presentation Schedule
From Attributes to Values: A Network-Driven Meta-Analysis of Hospitality Service Variables (97748)
Session Chair: Chin-Feng Lin
Thursday, 26 March 2026 14:05
Session: Session 3
Room: Room 705 (7F)
Presentation Type: Oral Presentation
This study, incorporating Means-End Chain (MEC) theory and social network analysis, applies variable extraction within a meta-analytic framework to explore service variables in the tourism and leisure literature. A total of 136 hotel-related and 46 restaurant-related journal articles indexed in the Journal Citation Reports (JCR) from 2014 to 2023 were analyzed. Based on Voyant Tools and content analysis, 34 hotel-related and 32 restaurant-related attribute and consequence variables were initially identified and validated by a panel of 12 MEC experts, resulting in 29 and 28 confirmed variables, respectively. Value variables adopted the List of Values (LOV). The findings reveal 1,844 and 700 service variable instances in the hotel and restaurant papers, respectively. Frequently cited hotel attributes included "smart services," "excellent service," and "star rating," while key consequences were "recommendation willingness," "trust," and "satisfaction." For restaurants, top attributes were "smart services," "excellent service," and "appraise grading," with "satisfaction," "positivity," and "trust" as primary consequences. The most cited value variables across both sectors were "security," "excitement," and "fun and enjoyment of life." Social network analysis was used to visualize the attribute–consequence–value (A-C-V) linkages, offering insights into the perceived meaning and significance of service variables. The study contributes a structured synthesis for improving variable identification in hospitality service research.
Authors:
Chin-Feng Lin, National Pingtung University, Taiwan
Yen-I Chu, National Pingtung University, Taiwan
About the Presenter(s)
Professor Chin-Feng Lin is a University Professor/Principal Lecturer at National Pingtung University in Taiwan
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