IDEAS home Printed from https://ideas.repec.org/a/eee/anture/v108y2024ics0160738324000963.html
   My bibliography  Save this article

The use of synthetic data in tourism

Author

Listed:
  • Viglia, Giampaolo
  • Adler, Susanne J.
  • Miltgen, Caroline Lancelot
  • Sarstedt, Marko

Abstract

•Large language models can support stimuli evaluation.•Synthetic data suits early research such as pretests; avoid it for main studies.•Prompt-tuning and fine-tuning improve large language model's responses.

Suggested Citation

  • Viglia, Giampaolo & Adler, Susanne J. & Miltgen, Caroline Lancelot & Sarstedt, Marko, 2024. "The use of synthetic data in tourism," Annals of Tourism Research, Elsevier, vol. 108(C).
  • Handle: RePEc:eee:anture:v:108:y:2024:i:c:s0160738324000963
    DOI: 10.1016/j.annals.2024.103819
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160738324000963
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.annals.2024.103819?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hoffmann, Stefan & Lasarov, Wassili & Dwivedi, Yogesh K., 2024. "AI-empowered scale development: Testing the potential of ChatGPT," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    2. Liu, Yan & Cao, Xinyue & Font, Xavier, 2024. "Nudge pro-environmental contagion: Residents to tourists," Annals of Tourism Research, Elsevier, vol. 105(C).
    3. Peiyao Li & Noah Castelo & Zsolt Katona & Miklos Sarvary, 2024. "Frontiers: Determining the Validity of Large Language Models for Automated Perceptual Analysis," Marketing Science, INFORMS, vol. 43(2), pages 254-266, March.
    4. Arthur Spirling, 2023. "Why open-source generative AI models are an ethical way forward for science," Nature, Nature, vol. 616(7957), pages 413-413, April.
    5. Miao, Li & Yang, Fiona X. & Im, Jinyoung & Zhang, Qiao, 2024. "Flexwork and flextravel," Annals of Tourism Research, Elsevier, vol. 106(C).
    6. Mahuya Adhikary & Atanu Adhikari, 2019. "Micro-modelling of individual tourist’s information-seeking behaviour: a heterogeneity-specific study," Current Issues in Tourism, Taylor & Francis Journals, vol. 22(6), pages 705-728, April.
    7. Tribe, John & Paddison, Brendan, 2024. "Paths from knowledge and theory development to impact," Annals of Tourism Research, Elsevier, vol. 104(C).
    8. Argyle, Lisa P. & Busby, Ethan C. & Fulda, Nancy & Gubler, Joshua R. & Rytting, Christopher & Wingate, David, 2023. "Out of One, Many: Using Language Models to Simulate Human Samples," Political Analysis, Cambridge University Press, vol. 31(3), pages 337-351, July.
    9. S. Hoffmann & W. Lasarov & Y. Dwivedi, 2024. "AI-empowered Scale Development: Testing the Potential of ChatGPT," Post-Print hal-04607717, HAL.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jan Ole Krugmann & Jochen Hartmann, 2024. "Sentiment Analysis in the Age of Generative AI," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 11(1), pages 1-19, December.
    2. Jiangbo Yu & Graeme McKinley, 2024. "Synthetic Participatory Planning of Shared Automated Electric Mobility Systems," Sustainability, MDPI, vol. 16(13), pages 1-32, June.
    3. Cova, Joshua & Schmitz, Luuk, 2024. "A primer for the use of classifier and generative large language models in social science research," OSF Preprints r3qng_v1, Center for Open Science.
    4. Meysam Alizadeh & Maël Kubli & Zeynab Samei & Shirin Dehghani & Mohammadmasiha Zahedivafa & Juan D. Bermeo & Maria Korobeynikova & Fabrizio Gilardi, 2025. "Open-source LLMs for text annotation: a practical guide for model setting and fine-tuning," Journal of Computational Social Science, Springer, vol. 8(1), pages 1-25, February.
    5. Yuan Gao & Dokyun Lee & Gordon Burtch & Sina Fazelpour, 2024. "Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina," Papers 2410.19599, arXiv.org, revised Jan 2025.
    6. Holtdirk, Tobias & Assenmacher, Dennis & Bleier, Arnim & Wagner, Claudia, 2025. "Addressing Systematic Non-response Bias with Supervised Fine-Tuning of Large Language Models: A Case Study on German Voting Behaviour," OSF Preprints udz28_v2, Center for Open Science.
    7. Shuaiyu Chen & T. Clifton Green & Huseyin Gulen & Dexin Zhou, 2024. "What Does ChatGPT Make of Historical Stock Returns? Extrapolation and Miscalibration in LLM Stock Return Forecasts," Papers 2409.11540, arXiv.org.
    8. Daniel Albert & Stephan Billinger, 2024. "Reproducing and Extending Experiments in Behavioral Strategy with Large Language Models," Papers 2410.06932, arXiv.org.
    9. Ning Li & Huaikang Zhou & Mingze Xu, 2024. "From Text to Insight: Leveraging Large Language Models for Performance Evaluation in Management," Papers 2408.05328, arXiv.org.
    10. Navid Ghaffarzadegan & Aritra Majumdar & Ross Williams & Niyousha Hosseinichimeh, 2024. "Generative agent‐based modeling: an introduction and tutorial," System Dynamics Review, System Dynamics Society, vol. 40(1), January.
    11. Jürgensmeier, Lukas & Skiera, Bernd, 2024. "Generative AI for scalable feedback to multimodal exercises," International Journal of Research in Marketing, Elsevier, vol. 41(3), pages 468-488.
    12. Hortense Fong & George Gui, 2024. "Modeling Story Expectations to Understand Engagement: A Generative Framework Using LLMs," Papers 2412.15239, arXiv.org.
    13. Hermann, Erik & Puntoni, Stefano, 2024. "Artificial intelligence and consumer behavior: From predictive to generative AI," Journal of Business Research, Elsevier, vol. 180(C).
    14. von der Heyde, Leah & Haensch, Anna-Carolina & Wenz, Alexander, 2023. "Assessing Bias in LLM-Generated Synthetic Datasets: The Case of German Voter Behavior," SocArXiv 97r8s_v1, Center for Open Science.
    15. Niyousha Hosseinichimeh & Aritra Majumdar & Ross Williams & Navid Ghaffarzadegan, 2024. "From text to map: a system dynamics bot for constructing causal loop diagrams," System Dynamics Review, System Dynamics Society, vol. 40(3), July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:anture:v:108:y:2024:i:c:s0160738324000963. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/annals-of-tourism-research/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.