Existing digital transformation (DT) frameworks treat technology as a passive tool, failing to explain AI-driven autonomous adaptation, agentic co-configuration and ecosystem orchestration. This paper aims to reconceptualize DT in hospitality by introducing Digital Transformation 2.0 (DT 2.0), distinguishing it from DT 1.0 and proposing a research agenda for artificial intelligence (AI)-orchestrated service ecosystems.
Through systematic synthesis of management, information systems and hospitality literature, the study develops a conceptual framework grounded in dynamic capabilities theory and service-dominant logic.
Grounded in dynamic capabilities theory and service-dominant logic, DT 2.0 is defined by three necessary and sufficient conditions: cognitive augmentation (AI that autonomously adapts strategies through continuous learning rather than executing pre-programmed rules), human–AI co-configuration (AI as operant resources that actively cocreate service design) and ecosystem-mediated co-creation (intelligent coordination enabling real-time value co-creation across organizational boundaries). Competitive advantage theory shifts from process efficiency (DT 1.0) to ecosystem position, algorithmic learning speed and human–AI complementarity (DT 2.0).
This research advances DT theory by positioning cross-functional integration and governance as foundational DT 2.0 capabilities, theorizing a shift from fragmented, department-centric stacks to enterprise architectures that unify data, processes and decision rights across hospitality functions. The framework provides hospitality executive strategies for transitioning to AI-orchestrated ecosystems, embedding ethical governance, leveraging algorithmic learning and navigating ecosystem coordination challenges.
This paper addresses the gap that prior frameworks cannot capture AI’s autonomous, agentic and ecosystem-orchestrating capabilities. It introduces a theoretically grounded DT 2.0 definition specifying necessary and sufficient conditions that distinguish it from rule-based DT 1.0, positions CDR as a dynamic capability linking ethical AI governance to competitive advantage and offers a research agenda for the AI era.
