Artificial Intelligence and Innovation in Tourism SMEs: A Bibliometric and Systematic Review with a Constraint-Enabled Perspective

While Artificial Intelligence (AI) has the potential to spur innovation among tourism small and medium-sized enterprises (SMEs), the mechanisms through which this is likely to occur under resource constraints are less well specified. By synthesizing bibliometric mapping with a systematic literature review (SLR), we examine tourism research from 2020 to 2025 in Scopus-indexed sources, comprising 71 studies identified from an initial search of 387 records. The bibliometric analysis establishes the publication and thematic landscape, while the SLR extracts the theorized and conceptual framing from the academic literature. Combining the bibliometric and SLR contributions, the study develops a constraint-enabled, theory-based framework that hypothesizes how AI capability in tourism SMEs might be associated with innovation outcomes via three proposed mediators: digital self-efficacy, epistemic curiosity, and knowledge integration, which are theorized to be enabled by institutional support and moderated by environmental constraints. Significantly, the study does not empirically test these propositions in this paper, thereby positioning the contribution as a theory-based synthesis and research framework rather than an empirical examination of AI and innovation. The study concludes by outlining propositions and a methodological research agenda to be tested in future tourism investigations in developing and emerging economies.