AI-Enabled Service Innovation in Data-Scarce Emerging Tourism Destinations: A Literature Review

Research on smart tourism destinations and data-driven service innovation tends to assume abundant data, mature infrastructure, and capable institutions, conditions that emerging destinations rarely meet. This study examines how data-driven service innovation supported by artificial intelligence (AI) can support the transition of emerging destinations towards smart destinations under data scarcity. A PRISMA 2020 systematic review of records exported from Scopus, Web of Science, and ScienceDirect yielded a corpus of 219 papers, analysed with bibliometric indicators, VOSviewer, Bibliometrix, and dictionary-based content coding, and complemented by a documentary analysis of Riohacha (Colombia) as an illustrative case. Output grows at 18.7% per year. Within the corpus, studies linking data and AI to service innovation (43) and studies of innovation under scarcity (25) develop largely in parallel. Only seven combine both, a co-occurrence close to that expected under independence (lift = 0.96), and none adopts the destination scale. Regarding the dimensions and design principles: The study proposes a layered model that takes scarcity as its starting point and articulates five dimensions, namely scarcity conditions, collaborative data governance, the minimum viable data ecosystem, frugal and augmentative AI capabilities, and co-created smart services, cut across by inclusive co-creation and a learning loop. Seven design principles (data sufficiency, source bricolage, frugal and augmentative AI, collaborative data governance, inclusive and culturally relevant co-creation, incremental scaling, and continuous learning) guide its instantiation.