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TRUVACE RECORD VERSION
record: TRV-2026-2649
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T16:56:36.729649Z
status: published
lens: g_space
sector: business
headline: Artificial Intelligence prompt engineering in tourism and hospitality: a task-technology fit theory on performance, complexity, and user literacy
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: structured reasoning prompts – particularly expert prompting and chain-of-thought – significantly outperform basic approaches | artificial intelligence literacy did not significantly moderate the effectiveness of prompts
problem_reading: (none)
problem_evidence: (none)
quick_read: Two experiments tested how different ways of prompting generative AI perform on marketing and management tasks in tourism and hospitality. Structured reasoning prompts, particularly expert prompting and chain-of-thought, significantly outperformed basic prompts.

Task complexity limited AI performance across every prompting strategy tested. Prompt quality helped, but harder tasks still produced weaker results.

The authors also tested whether a user's AI literacy, low or high, changed how well prompts worked. It did not significantly, which they read as a sign that modern AI interfaces open access to different user groups. They suggest organisations build prompt libraries and tier their AI deployment.
limitation: 
tag: Evidence-backed gain
key_points: Study one used a 16 × 2 factorial design, comparing 16 prompt types across simple and complex task conditions. | Study two used a mixed-factorial within-subjects design and added AI literacy (low vs. high) as a moderator. | Expert prompting and chain-of-thought significantly outperformed basic prompts. | Task complexity constrained AI performance consistently across prompting strategies. | AI literacy did not significantly moderate prompt effectiveness. | The authors recommend organisational prompt libraries and tiered AI deployment in tourism and hospitality.
rundown: The research applies task-technology fit theory to conversational AI in tourism and hospitality. The authors say there was little empirical evidence on how prompt strategies perform across diverse marketing and management tasks.

Study one compared 16 prompt types on simple and complex tasks. Study two had participants work within subjects and compared low and high AI literacy groups to see whether user skill shaped results.

The authors conclude that the findings extend task-technology fit theory and support shared prompt libraries and tiered deployment, where task difficulty guides which tools and prompts are used.
sources:
- peer_reviewed | Tourism Recreation Research | https://doi.org/10.1080/02508281.2026.2727979 | 2026-09-24
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