When cool hospitality brand meets AI: exploring the matching effect of service agents and brand images on brand attitude

款待 适度 品牌管理 服务(商务) 品牌知名度 营销 广告 感觉 酒店业 匹配(统计) 业务 心理学 独创性 品牌资产 社会心理学 旅游 政治学 数学 统计 创造力 法学
作者
Yun Liu,Xingyuan Wang,Heyu Qin
出处
期刊:International Journal of Contemporary Hospitality Management [Emerald Publishing Limited]
卷期号:36 (7): 2367-2384 被引量:13
标识
DOI:10.1108/ijchm-04-2023-0516
摘要

Purpose This paper aims to explore the matching effect of hospitality brand image (cool vs non-cool) and service agents (Artificial intelligence [AI] vs human staff) on brand attitude, with a focus on assessing the role of feeling right as a mediator and service failure as a moderator. Design/methodology/approach This paper tested the hypotheses through three experiments and a Supplementary Material experiment, which collectively involved 835 participants. Findings The results indicated that the adoption of AI by cool brands can foster the right feeling and enhance consumers’ positive brand attitudes. In contrast, employing human staff did not lead to improved brand attitudes toward non-cool brands. Furthermore, the study found that service failure moderated the matching effect between service agents and cool brand images on brand attitude. The matching effect was observed under successful service conditions, but it disappeared when service failure occurred. Practical implications The findings offer practical guidance for hospitality companies in choosing service agents based on brand image. Cool brands can swiftly transition to AI, reinforcing their modern, cutting-edge image. Traditional brands may delay AI adoption or integrate it strategically with human staff. Originality/value To the best of the authors’ knowledge, this paper represents one of the first studies to address the issue of selecting the optimal service agent based on hospitality brand image. More importantly, it introduces the concept of a cool hospitality brand image as a boundary condition in the framework of AI research, providing novel insights into consumers’ ambivalent responses to AI observed in previous studies.
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