Effects of Service Quality Characteristics and Unmanned Robot Cafes on Perceived value, and Intention to Reuse

This study identified price, location, coffee quality, and physical environment as sub-dimensions of service quality for customers of unmanned robot cafes and examined their effects on perceived value, usefulness, and intention to reuse. The survey was conducted from June 20 to July 20, 2024, using the Naver Office program. A total of 500 questionnaires were distributed, and 292 (58.4%) were returned. After excluding six responses with insufficient data, 286 valid responses (97.9%) were used for the final empirical analysis. A summary and implications of the verification results are presented as follows. First, Price and location were found to significantly influence perceived value, whereas coffee quality and physical environment had no significant effect on perceived value. Second, price, location, coffee quality, and physical environment were analyzed to have a significant influence on usability. Third, price, location, and coffee quality were analyzed to have a significant influence on the intention to reuse, but the physical environment was found to have no significant influence on the intention to reuse. Fourth, it was analyzed that perceived value had a significant effect on usefulness, perceived value had a significant effect on intention to reuse, and usefulness had a significant influence on intention to reuse. Based on these findings, practical implications for unmanned robot cafes are discussed.

Authors

Publication Details

Journal
Journal of Hotel & Resort
Published
2024-12-31
DOI
https://doi.org/10.62532/khrc.2024.12.23.6.251
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Effects of Service Quality Characteristics and Unmanned Robot Cafes on Perceived value, and Intention to Reuse

Oh Joon Lee
Journal of Hotel & Resort
AI in Service Interactions
article

Effects of Service Quality Characteristics and Unmanned Robot Cafes on Perceived value, and Intention to Reuse

Oh Joon Lee
article en

Abstract

This study identified price, location, coffee quality, and physical environment as sub-dimensions of service quality for customers of unmanned robot cafes and examined their effects on perceived value, usefulness, and intention to reuse. The survey was conducted from June 20 to July 20, 2024, using the Naver Office program. A total of 500 questionnaires were distributed, and 292 (58.4%) were returned. After excluding six responses with insufficient data, 286 valid responses (97.9%) were used for the final empirical analysis. A summary and implications of the verification results are presented as follows. First, Price and location were found to significantly influence perceived value, whereas coffee quality and physical environment had no significant effect on perceived value. Second, price, location, coffee quality, and physical environment were analyzed to have a significant influence on usability. Third, price, location, and coffee quality were analyzed to have a significant influence on the intention to reuse, but the physical environment was found to have no significant influence on the intention to reuse. Fourth, it was analyzed that perceived value had a significant effect on usefulness, perceived value had a significant effect on intention to reuse, and usefulness had a significant influence on intention to reuse. Based on these findings, practical implications for unmanned robot cafes are discussed.

Journal of Hotel & Resort
Openalex Percentile: Top 77%
AI in Service Interactions
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.