AI駆動型マーケティングのメカニズムを持続可能な観光成果に結び付けるデスティネーションレベルの概念枠組み

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a theoretical foundation and testable propositions for empirical research on AI and sustainable tourism.

🏢実務担当者:Offers an operational roadmap for integrating AI marketing with sustainability measures, including specific levers for operators and authorities.

🏛政策担当者:Highlights regulatory and governance mechanisms to ensure AI-driven tourism aligns with sustainability and equity.

The tourism and hospitality industry faces growing pressure to reconcile marketing-driven growth with environmental sustainability. Artificial intelligence and related digital technologies are transforming how destinations are marketed and how visitor demand is managed. Yet, the field lacks an integrated theoretical account of how these marketing innovations can systematically advance sustainability goals. This paper addresses that gap by proposing a conceptual framework that maps AI-driven marketing mechanisms onto sustainable development outcomes across four interconnected pathways: smart operations and resource optimisation, environmental intelligence and impact monitoring, AI-powered demand shaping and immersive marketing, and governance, ethics, and systemic transformation. Drawing on a multidisciplinary synthesis of the marketing, digital innovation, and sustainability literatures, the framework advances eight testable propositions linking specific AI marketing capabilities, including personalised sustainable recommendations, predictive analytics for overtourism prevention, smart pricing, and immersive behaviour nudging, to SDG-aligned outcomes. Three causal mediators (efficiency, information, and behaviour change) and key moderating conditions (governance capacity, data quality, and equity) are identified, and each pathway is anchored in an established theory spanning the resource and capability based reasoning, technology adoption, planned behaviour, and institutional perspectives. The paper also confronts rebound effects, algorithmic bias, privacy risks, and digital divides, specifying actor-specific levers: operators earmarking verified efficiency savings for clean energy, destination authorities enforcing carbon and visitor caps, regulators mandating privacy-by-design and bias audits, and development agencies channelling targeted finance and data access to SMEs. These give managers, firms, and policymakers an operational roadmap for AI-driven marketing that balances commercial goals with climate, biodiversity, and social inclusion.

• semanticscholar https://link.springer.com/content/pdf/10.1007/s44282-026-00528-x.pdffirst seen 2026-09-04 05:49:54