Culturally responsive tourism intelligence: integrating cross-cultural encounters, machine learning and big data forecasting in tourism and hospitality

Tourism and hospitality research increasingly uses machine learning, platform traces and big-data forecasting, yet many analytics studies still treat culture as background context rather than as a source of meaning, behavior and model validity. This conceptual article develops a Culturally Responsive Tourism Intelligence framework by integrating recent peer-reviewed work on cross-cultural tourism encounters, smart tourism technologies, machine learning and big-data forecasting. Using a structured integrative documentary synthesis, the article identifies how cultural encounters generate digital traces, how numerical, textual, visual and behavioral data can be connected without reducing culture to a technical feature, and how predictive models can be assessed through technical, cultural and ethical validity. The synthesis shows that tourism analytics becomes more useful when digital traces are interpreted through cultural meanings, language practices, visitor expectations, stakeholder relations and governance conditions. The proposed framework links cultural encounter, data trace, feature construction, model layer, validation and governance, and decision outcome. The contribution is a theory-building model that moves tourism analytics beyond technical prediction toward culturally valid, explainable and stakeholder-sensitive decision support.