Analysing the dark side of artificial intelligence in tourism and its influence on young consumers’ behaviour
Animesh Kumar Sharma & Rahul Sharma
What the paper says
Purpose The growing adoption of artificial intelligence (AI) in the tourism industry has greatly improved consumer experiences. However, the potential negative repercussions of AI remain unexplored. The Elaboration Likelihood Model (ELM) and the Deception Unified Theory are used in this study to analyse the “dark side” of AI in tourism and its impact on young consumers’ behaviour. The study looks at how AI-driven misinformation, prejudices and manipulative marketing methods affect young travellers’ decision-making processes. Design/methodology/approach A non-probabilistic research approach, specifically employing the snowball sampling technique, was adopted to collect data from young consumers. The acquired data were analysed with SPSS, and the conceptual model was validated using structural equation modelling (SEM) with AMOS software. Findings The study demonstrates that AI-morphed travel content had a substantial influence on consumer decision-making by enhancing perceived authenticity, strengthening confidence in AI-generated information and amplifying cognitive biases in judgement. These effects combine to increase desire to visit AI-promoted destination, but they also lead to post-experience dissatisfaction when AI-morphed content fail to match reality. This study presents empirical evidence for a sequential persuasion-deception pathway in which AI-morphed content puffs up beliefs, impacts behavioural intentions and, ultimately, intensifies the expectation-reality gap. Practical implications This study adds to the existing literature by providing a more nuanced understanding of AI’s deceptive components in tourism and their psychological influence on young customers. Policymakers and industry stakeholders can use these lessons to create responsible AI plans that reduce consumer hazards while retaining technical advances in tourism services. Originality/value The findings highlight the importance of ethical AI implementations in promoting transparency and trust in the tourism industry.
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.50 × 0.4 = 0.20 |
| M · momentum | 0.50 × 0.15 = 0.07 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.