TY - JOUR

T1 - Using LLMs in sensory service research

T2 - initial insights and perspectives

AU - Imschloss, Monika

AU - Sarstedt, Marko

AU - Adler, Susanne J.

AU - Cheah, Jun Hwa

N1 - Publisher Copyright: © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

PY - 2025

Y1 - 2025

N2 - Researchers have started using large language models (LLMs), such as OpenAI's GPT, to generate synthetic datasets designed to mimic human response behavior. Several studies have systematically compared LLM-generated data with human samples in order to explore LLMs’ ability to mimic consumer decision-making. Extending prior findings, our research sets out to explore how GPT-4o responds to sensory information, and to evaluate its ability to grasp crossmodal correspondences as well as multisensory congruence–as commonly encountered in service settings. Our results indicate that while GPT-4o identifies and describes sensory stimuli accurately, it often fails to replicate the associative meanings and interpretations that humans derive from these stimuli, especially in stand-alone assessments. Our research therefore underscores the need for further exploration of the conditions under which LLMs reliably mirror human responses to sensory stimuli, and the implications of using LLMs in research on sensory-rich service settings.

AB - Researchers have started using large language models (LLMs), such as OpenAI's GPT, to generate synthetic datasets designed to mimic human response behavior. Several studies have systematically compared LLM-generated data with human samples in order to explore LLMs’ ability to mimic consumer decision-making. Extending prior findings, our research sets out to explore how GPT-4o responds to sensory information, and to evaluate its ability to grasp crossmodal correspondences as well as multisensory congruence–as commonly encountered in service settings. Our results indicate that while GPT-4o identifies and describes sensory stimuli accurately, it often fails to replicate the associative meanings and interpretations that humans derive from these stimuli, especially in stand-alone assessments. Our research therefore underscores the need for further exploration of the conditions under which LLMs reliably mirror human responses to sensory stimuli, and the implications of using LLMs in research on sensory-rich service settings.

KW - Generative artificial intelligence

KW - large language models

KW - sensory marketing

KW - service research

KW - servicescape

KW - Management studies

UR - http://www.scopus.com/inward/record.url?scp=105002056612&partnerID=8YFLogxK

U2 - 10.1080/02642069.2025.2479723

DO - 10.1080/02642069.2025.2479723

M3 - Journal articles

AN - SCOPUS:105002056612

JO - Service Industries Journal

JF - Service Industries Journal

SN - 0264-2069

ER -