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Cross-segment validation of customer support for AI-based service robots at luxury, fine-dining, casual, and quick-service restaurants

Yao-Chin Wang (Department of Tourism, Hospitality and Event Management, University of Florida, Gainesville, Florida, USA)
Avraam Papastathopoulos (Dubai Business School, University of Dubai, Dubai, United Arab Emirates)

International Journal of Contemporary Hospitality Management

ISSN: 0959-6119

Article publication date: 21 June 2023

Issue publication date: 29 April 2024

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Abstract

Purpose

With the trend of adopting and studying artificial intelligence (AI) service robots at restaurants, the authors’ understanding of how customers perceive robots differently across restaurant segments remains limited. Therefore, building upon expectancy theory, this study aims to propose a trust-based mechanism to explain customers’ support for AI-based service robots.

Design/methodology/approach

For cross-segment validation, data were collected from online survey participants under the scenarios of experiencing AI service robots in luxury (n = 428), fine-dining (n = 420), casual (n = 409) and quick-service (n = 410) restaurant scenarios.

Findings

In all four segments, trust in technology increased willingness to accept AI service robots, which was then positively related to customers’ support for AI-based service robots. Meanwhile, customers’ AI performance expectancy mediated the relationship between trust in technology and willingness to accept AI service robots. On the other hand, at luxury, fine-dining and casual restaurants, males perceived a stronger positive relationship between trust in technology and AI performance expectancy. No generational differences were found in the four restaurant segments between trust in technology and AI performance expectancy.

Originality/value

To the best of the authors’ knowledge, this study is one of the first attempts in hospitality research to examine cross-segment validation of customers’ responses to AI-based service robots in the luxury, fine-dining, casual and quick-service restaurant segments.

Keywords

Acknowledgements

The authors appreciate funding support from Dr. Rachel J.C. Fu (Chair and Professor, Department of Tourism, Hospitality and Event Management; Director, Eric Friedheim Tourism Institute; racheljuichifu@ufl.edu) at the University of Florida for data collection in this study.

Citation

Wang, Y.-C. and Papastathopoulos, A. (2024), "Cross-segment validation of customer support for AI-based service robots at luxury, fine-dining, casual, and quick-service restaurants", International Journal of Contemporary Hospitality Management, Vol. 36 No. 6, pp. 1744-1765. https://doi.org/10.1108/IJCHM-11-2022-1448

Publisher

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Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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