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Artificial Intelligence prompt engineering in tourism and hospitality: a task-technology fit theory on performance, complexity, and user literacy
- Truvace record
- TRV-2026-2649raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Controlled or validated comparison
- Sources
- 1 sourcePeer-reviewed
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Despite the rapid integration of generative artificial intelligence into tourism and hospitality operations, there is a critical gap in the empirical framework for evaluating how different artificial intelligence prompt engineering strategies perform across diverse marketing and management tasks, and examining how user characteristics shape these outcomes. This study addresses this gap through task-technology fit theory across two complementary experiments. Study one employed a 16 × 2 factorial design, assessing…
The gain
In two experiments on tourism and hospitality marketing and management tasks, structured reasoning prompts, especially expert prompting and chain-of-thought, significantly outperformed basic prompts, and user AI literacy did not significantly change how well prompts worked.
Sources
1 on file- Tourism Recreation Researchdoi:10.1080/02508281.2026.2727979
Cite this record
v1Truvace Impact Record TRV-2026-2649, v1: “Artificial Intelligence prompt engineering in tourism and hospitality: a task-technology fit theory on performance, complexity, and user literacy.” Truvace, 2026-11-12. /record/TRV-2026-2649 (accessed on the day you copy it). sha256 8a64da79eae580aa…
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