Customers’ perceptions of hotel AI-enabled voice assistants: does brand matter?

心理学 解释水平理论 背景(考古学) 比例(比率) 感知 口头传述的 独创性 营销 品牌知名度 社会心理学 广告 业务 量子力学 生物 物理 古生物学 神经科学 创造力
作者
Ruiying Cai,Lisa Cain,Hyeongjin Jeon
出处
期刊:International Journal of Contemporary Hospitality Management [Emerald Publishing Limited]
卷期号:34 (8): 2807-2831 被引量:98
标识
DOI:10.1108/ijchm-10-2021-1313
摘要

Purpose Extending the technology acceptance model (TAM) to a new context, the purpose of this paper is to propose an integrative model of the brand of artificial intelligence-enabled voice assistants (AI-EVA) and customers’ perceptions and behavioral intentions of using AI-EVA in hotels. Moderating effects of construal levels and hotel scales were examined. Design/methodology/approach This paper adopted a mixed method approach. A qualitative and phenomenological methodology was adopted in Study 1 to explore hotel customers’ experience with AI-EVA. Study 2 applied experimental design to investigate the effects of the brand of AI-EVA and construal level on customers’ perceptions and behavioral intentions of using AI-EVA. Based on Studies 1 and 2 results, Study 3 examined how the brand of AI-EVA and hotel scale affect customers’ perceptions and behavioral intentions of using AI-EVA during hotel stays. Findings This research found that customers perceive a higher level of perceived usefulness, perceived ease of use and anthropomorphism when AI-EVA is branded (vs off-brand). Perceived usefulness positively affects customers’ intention to use and to spread positive word-of-mouth. Anxiety of using AI-enabled devices and privacy concerns inhibit customers’ intention to use AI-EVA. Anthropomorphism increases customers’ willingness to spread positive word-of-mouth. Construal level moderates the effect of the brand of AI-EVAs on perceived ease of use and anthropomorphism. Hotel scale moderates the effect of brand on perceived usefulness. Originality/value This paper is one of the first attempts to uncover and integrate different factors underlying customers’ perceptions of using AI-EVA in an extended TAM in hotel settings. This paper provides an integrative model extending the TAM to a new context by deploying a mixed-method approach across three studies.
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