Artificial Intelligence prompt engineering in tourism and hospitality: a task-technology fit theory on performance, complexity, and user literacy

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 16 prompt types across simple and complex task conditions. Study two extended this with a mixed-factorial within-subjects design, introducing artificial intelligence literacy (low vs. high) as a moderator. The results demonstrate that structured reasoning prompts – particularly expert prompting and chain-of-thought – significantly outperform basic approaches, while task complexity consistently constrains artificial intelligence performance across prompting strategies. Notably, artificial intelligence literacy did not significantly moderate the effectiveness of prompts, suggesting that modern artificial intelligence interfaces democratise access across user groups. These findings extend task-technology fit theory to conversational artificial intelligence contexts and offer actionable implications for organisational prompt libraries and tiered artificial intelligence deployment strategies in tourism and hospitality.