Year: 2025
Type: article
Abstract: The tourism industry is currently experiencing a substantial digital transformation, with Online Travel Agencies (OTAs) committed to integrating innovations such as artificial intelligence (AI) in order to enhance service delivery and personalize user experiences. This study investigates the relationship between the utilization of AI and the technical quality scores of tourism websites, aiming to identify significant associations and variances in the critical conversion phase. An exploratory research design is employed to evaluate the technical quality of three prominent international tourism websites, i.e., Booking.com, Airbnb.com, and Tripadvisor.com. The investigation uses Google Lighthouse, with Performance, Accessibility, Best Practices, and Search Engine Optimization (SEO) as variables analyzed across both desktop and mobile versions, as well as on pages with and without AI functionality. Data analysis is performed using JASP (version 0.19.3), including linear regression analysis to quantify the predictive relationship. The analysis confirms that the Performance variable is the most sensitive to the influence of AI. AI integration demonstrates a significant positive influence on the Performance score of travel websites. The regression model indicates that AI usage explains 78.9% of the variation in the Performance score (R2 = 0.789, p < 0.001), indicating a substantial correlation with technical sustainability. Nevertheless, there remains an ongoing necessity for optimization, particularly with regard to the enhancement of overall performance and improvement of scores for mobile devices. The study acknowledges certain limitations related to the sample size of AI applications and the accessibility of specific AI versions in particular geographic regions.
Source: Sustainability
DOI: 10.3390/su17219879
Institution: Transylvania University of Brașov
Language: English
Topic: Mobile and Web Applications
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4415930873