Frontiers: Determining the Validity of Large Language Models for Automated Perceptual Analysis

Peiyao Li
Peiyao Li
[email protected]
Haas School of Business, University of California, Berkeley, California 94720;
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,
Noah Castelo
Noah Castelo
[email protected]
Alberta School of Business, University of Alberta, Edmonton, Alberta T6G 2R6, Canada;
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,
Zsolt Katona
Corresponding Author
Zsolt Katona
[email protected]
https://orcid.org/0000-0001-8411-6040
Haas School of Business, University of California, Berkeley, California 94720;
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Miklos Sarvary
Miklos Sarvary
[email protected]
https://orcid.org/0000-0002-3301-5917
Columbia Business School, Columbia University, New York, New York 10027
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Peiyao Li

[email protected]

Haas School of Business, University of California, Berkeley, California 94720;

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,

Noah Castelo

[email protected]

Alberta School of Business, University of Alberta, Edmonton, Alberta T6G 2R6, Canada;

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,

Corresponding Author

Zsolt Katona

[email protected]

https://orcid.org/0000-0001-8411-6040

Haas School of Business, University of California, Berkeley, California 94720;

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,

Miklos Sarvary

[email protected]

https://orcid.org/0000-0002-3301-5917

Columbia Business School, Columbia University, New York, New York 10027

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Published Online:25 Jan 2024https://doi.org/10.1287/mksc.2023.0454

Supplemental Material

mksc.2023.0454.sm1.pdf

The replication files for this article are available HERE.

cover image Marketing Science

Volume 43, Issue 2

March-April 2024

Pages 239-468, iii

Article Information

Supplemental Material

Metrics

Information

Received:September 28, 2023
Accepted:December 05, 2023
Published Online:January 25, 2024

Copyright © 2024, INFORMS

Cite as

Peiyao Li, Noah Castelo, Zsolt Katona, Miklos Sarvary (2024) Frontiers: Determining the Validity of Large Language Models for Automated Perceptual Analysis. Marketing Science 43(2):254-266.

https://doi.org/10.1287/mksc.2023.0454

Keywords

Acknowledgments

The authors thank Florian Stahl for providing the car trade-in data for our analysis; participants at the University of Washington Marketing Camp, the Wharton Business & Generative AI Workshop, and the MSI 2023 immersion event for comments; and the anonymous reviewers who helped improve the paper. The last three authors are listed in alphabetical order.