Paper Number

1521

Paper Type

SP

Abstract

Advancements in robotics and artificial intelligence are paving the way for retailers to employ physical robots in assisting consumers with their purchase decisions. However, as research has primarily focused on virtual robots (e.g., chatbots) or relied on hypothetical scenarios without actual consumer–robot interaction, our understanding of how consumers interact with physical robots and how the robot design affects consumer interactions is limited. We address this gap by investigating how an emotional (vs. rational) robot design influences consumers’ trust and purchase decisions. Drawing on trust theory and human–robot interaction literature, we propose a lab experiment in which consumers interact with a large language model (LLM)-based robot shopping assistant based on the robot ‘Furhat’ and OpenAI’s GPT-4. With our findings, we aim to contribute to research on consumer–robot interaction by providing novel insights into how and why the design of robot shopping assistants impacts consumers’ shopping behavior.

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Jun 14th, 12:00 AM

Robot Shopping Assistants: How Emotional versus Rational Robot Designs Affect Consumer Trust and Purchase Decisions

Advancements in robotics and artificial intelligence are paving the way for retailers to employ physical robots in assisting consumers with their purchase decisions. However, as research has primarily focused on virtual robots (e.g., chatbots) or relied on hypothetical scenarios without actual consumer–robot interaction, our understanding of how consumers interact with physical robots and how the robot design affects consumer interactions is limited. We address this gap by investigating how an emotional (vs. rational) robot design influences consumers’ trust and purchase decisions. Drawing on trust theory and human–robot interaction literature, we propose a lab experiment in which consumers interact with a large language model (LLM)-based robot shopping assistant based on the robot ‘Furhat’ and OpenAI’s GPT-4. With our findings, we aim to contribute to research on consumer–robot interaction by providing novel insights into how and why the design of robot shopping assistants impacts consumers’ shopping behavior.

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