The present study aimed to identify artificial intelligence (AI) factors with an emphasis on consumer behavioral patterns in tourism enterprises. To design an AI effectiveness model focused on consumer behavior in Iranian tourism organizations, a developmental mixed-methods approach was employed, comprising a qualitative (pragmatic) phase and a quantitative (positivist) phase. The study was conducted in five sequential stages, beginning with semi-structured interviews with 18 professors, specialists, and experts in information technology, management, and tourism marketing, selected through purposive sampling. Data were collected through a combination of library research, including review of resources, articles, and theses, and field research using grounded theory to identify dimensions of the phenomenon and refine the research questionnaires. Through open, axial, and selective coding, 130 initial concepts were condensed into five main categories: AI-based customer relationship management capabilities, AI-based customer profiling, digital infrastructure and capabilities based on customer behavior, identification of core customer behavioral patterns, and implementation of digital marketing in tourism enterprises. These categories encompass managerial, technological, and behavioral dimensions, enabling understanding of customer needs, behavior prediction, service quality improvement, operational efficiency enhancement, and support for innovative digital marketing strategies. The findings indicate that AI effectiveness depends on the synergy between technological infrastructure, managerial innovation, and customer behavior analysis. The proposed conceptual model provides a comprehensive framework linking AI capabilities to consumer behavioral patterns and outlines the pathway for implementing data-driven strategies to enhance services, competitiveness, and sustainable development in tourism enterprises.
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