Understanding AI-Generated Experiments in Tourism: Replications Using GPT Simulations
The present work explores whether the generative pre-trained transformers (GPT) can complement empirical research in tourism as the GPT extends beyond commercial applications. In particular, we utilized OpenAI’s Python API to interact with the GPT-3.5-turbo. Using GPT as a special subject, we coined AI-generative study (AGS) to validate key findings of 16 scenario-based experiments published in leading journals of tourism and hospitality in two studies. This research contributes to the literature by delineating a new methodology that opens a forum for discussion on alternative means of conducting tourism research. Future studies could also utilize GPT and the ability of generative AI for tourism research in terms of pilot-/pre-testing and cross-validation. In conclusion, we recommend that GPT-generated results should serve primarily as preliminary findings and must be corroborated by data from actual human participants, thus providing converging evidence to support the corresponding research conclusions.
Authors
- Xiling Xiong (ORCID: https://orcid.org/0000-0003-1541-1569)
- IpKin Anthony Wong (ORCID: https://orcid.org/0000-0003-4597-2228)
- GuoQiong Ivanka Huang (ORCID: https://orcid.org/0000-0001-9232-6822)
- Yixuan Peng
Institutions
- Hong Kong Polytechnic University (HK)
- University of Macau (MO)
- Southwestern University of Finance and Economics (CN)
Publication Details
- Journal
- Journal of Travel Research
- Published
- 2024-09-05
- DOI
- https://doi.org/10.1177/00472875241275945
- Citations
- 23
- Primary Topic
- Explainable Artificial Intelligence (XAI)
- Type
- article
- Field-Weighted Citation Impact
- 7.99
Funders
- National Natural Science Foundation of China