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GPT and CLT: The impact of ChatGPT's level of abstraction on consumer recommendations

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  • Kirshner, Samuel N.

Abstract

This study explores how ChatGPT interprets information through the lens of Construal Level Theory (CLT). The findings show that ChatGPT exhibits an abstraction bias, generating responses consistent with a high-level construal. This abstraction bias results in ChatGPT prioritising high-level construal features (e.g., desirability) over low-level construal features (e.g., feasibility) in consumer evaluation scenarios. Thus, ChatGPT recommendations differ significantly from traditional results based on human decision-making. Applying CLT concepts to large language models provides essential insights into how consumer behaviour may evolve with the increasing prevalence and capability of AI and offers many promising avenues for future research.

Suggested Citation

  • Kirshner, Samuel N., 2024. "GPT and CLT: The impact of ChatGPT's level of abstraction on consumer recommendations," Journal of Retailing and Consumer Services, Elsevier, vol. 76(C).
    • Handle: RePEc:eee:joreco:v:76:y:2024:i:c:s0969698923003314
    DOI: 10.1016/j.jretconser.2023.103580
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    Cited by:

    1. Markovitch, Dmitri G. & Stough, Rusty A. & Huang, Dongling, 2024. "Consumer reactions to chatbot versus human service: An investigation in the role of outcome valence and perceived empathy," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).
    2. Hermann, Erik & Puntoni, Stefano, 2024. "Artificial intelligence and consumer behavior: From predictive to generative AI," Journal of Business Research, Elsevier, vol. 180(C).

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