Artificial intelligence (AI) for tourism: an European-based study on successful AI tourism start-ups

Abstract

Purpose

The travel and tourism industry (TTI) could benefit the most from artificial intelligence (AI), which could reshape this industry. This study aims to explore the characteristics of tourism AI start-ups, the AI technological domains financed by Venture Capitalists (VCs), and the phases of the supply chain where the AI domains are in high demand.

Design/methodology/approach

This study developed a database of the European AI start-ups operating in the TTI from the Crunchbase database (2005–2020). The authors used start-ups as the unit of analysis as they often foster radical change. The authors complemented quantitative and qualitative methods.

Findings

AI start-ups have been mainly created by male Science, Technology, Engineering and Mathematics graduates between 2015 and 2017. The number of founders and previous study experience in non-start-up companies was positively related to securing a higher amount of funding. European AI start-ups are concentrated in the capital town of major tourism destinations (France, UK and Spain). The AI technological domains that received more funding from VCs were Learning, Communication and Services (i.e. big data, machine learning and natural language processing), indicating a strong interest in AI solutions enabling marketing automation, segmentation and customisation. Furthermore, VC-backed AI solutions focus on the pre-trip and post-trip.

Originality/value

To the best of the authors’ knowledge, this is the first study focussing on digital entrepreneurship, specifically VC-backed AI start-ups operating in the TTI. The authors apply, for the first time, a mixed-method approach in the study of tourism entrepreneurship.

Citation

Filieri, R., D’Amico, E., Destefanis, A., Paolucci, E. and Raguseo, E. (2021), "Artificial intelligence (AI) for tourism: an European-based study on successful AI tourism start-ups", International Journal of Contemporary Hospitality Management, Vol. 33 No. 11, pp. 4099-4125. https://doi.org/10.1108/IJCHM-02-2021-0220