The robot color effect on consumer responses: the moderating role of ambient lighting

业务 广告 营销 机器人 心理学 计算机科学 人工智能
作者
Faye Feier Chen,Huiling Huang,Anna S. Mattila
出处
期刊:International Journal of Contemporary Hospitality Management [Emerald Publishing Limited]
卷期号:37 (8): 2694-2715 被引量:2
标识
DOI:10.1108/ijchm-08-2024-1124
摘要

Purpose Colors possess inherent meanings and profoundly influence consumer psychology and behaviors. Although service robots are increasingly common in the hospitality industry, there is limited understanding of how consumers react to different robot colors. To bridge this gap, this study aims to use the color-in-context theory to examine the effects of the robot’s color on consumer responses, specifically focusing on perceived esthetics and consumer liking. In addition, it explores the interaction between robot color and servicescape elements such as ambient lighting. Design/methodology/approach Two experiments were conducted in a simulated restaurant setting. Study 1 examined consumer perceptions of white and metallic robots, while Study 2 analyzed how the robot’s color and the restaurant’s ambient lighting interact, focusing on perceived esthetics as a mediator. Findings Results indicate that white robots are perceived as warmer than metallic ones. In addition, consumer attitudes are more positive when a robot’s perceived social warmth matches the ambient lighting’s visual warmth. Specifically, white robots in warm lighting and metallic robots in cool lighting are preferred, with this congruency enhancing esthetic appreciation. Practical implications For hospitality managers, selecting a robot color scheme that aligns with the establishment’s existing servicescape cues, including ambient lighting, is crucial. This is especially pertinent in settings such as restaurants and bars, where varying lighting tones (e.g. warm versus cool) craft distinct atmospheres. Originality/value To the best of the authors’ knowledge, this study is among the first to demonstrate how the robot’s color affects consumer perceptions and evaluations within the hospitality context, providing valuable insights for integrating service robots effectively.
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