In the rapidly evolving digital economy, Digitalized Innovation Capabilities (DICs) have emerged as a pivotal enabler of sustainable competitive advantage for enterprises, fundamentally reshaping innovation management paradigms. Despite growing scholarly attention to digital innovation concepts, boundaries, and impacts, a significant theoretical and empirical gap persists regarding the precise definition, rigorous measurement, and systematic validation of DICs. This limitation impedes a holistic understanding of their antecedents and outcomes, hindering both academic discourse and practical implementation. To address this critical gap, this study synthesizes insights from Resource-Based View (RBV) and Dynamic Capabilities (DCs) logic to reconceptualize DICs as a firm’s capabilities to empower its innovation processes through the development and application of digital technologies. This integrative framework transcends conventional RBV’s static resource focus and DCs’ process-oriented limitations. Through systematic literature analysis and empirical validation, we deconstruct DICs into three interdependent dimensions: Openness Digitalized Innovation Capabilities (O-DICs), which enhance the depth, breadth, and efficiency of information exchange, expand channels for acquiring external resources, improve organizational learning, and ultimately increase market acceptance of innovations through digital technology development and application. This transcends traditional Open Innovation (OI) by enabling broader participation, deeper insights, and faster knowledge flows; Affordance Digitalized Innovation Capabilities (A-DICs), leveraging digital technologies to provide diverse possibilities and functional support for innovation activities across different contexts. Rooted in the concept of affordances, A-DICs help firms overcome barriers arising from bounded rationality, cognitive diversity, institutional differences, and cultural diversity. They enable context-sensitive adaptation, enhance innovation legitimacy, and expand innovation boundaries by facilitating resource integration across physical and cyber spaces, cross-industry collaboration, and scenario-specific application of technologies; Generativity Digitalized Innovation Capabilities (G-DICs), utilizing digital technologies to stimulate spontaneous, diverse, and unpredictable positive changes within the innovation process, driving it towards greater efficiency and sustainability. Capitalizing on inherent digital generativity, G-DICs foster emergent innovation, accelerate the iteration and diffusion of innovative outputs, and expand the value network by enabling diverse actor participation and collaboration. Methodologically, this research employs a mixed-methods approach to develop and validate the DICs Scale. Initial constructs derived from semi-structured interviews with industry experts and managers underwent exploratory factor analysis(EFA), yielding are fined item pool. Subsequent confirmatory factor analysis(CFA) with large-scale survey data confirmed the scale’s robustness, demonstrating high reliability, and discriminant validity across manufacturing and service sectors. Regression analyses further established that all three DICs dimensions exert statistically significant positive effects on innovation performance. This research makes significant theoretical contributions. It systematically defines the theoretical connotation of DICs and proposes a novel three-dimensional framework (O-DICs, A-DICs, G-DICs), moving beyond the limitations of the DCs framework in explaining how digital technologies empower the innovation process and revealing the micro-foundations of innovation capabilities in the digital context. Crucially, it develops and rigorously validates the DICs Scale, providing a scientifically reliable and effective measurement tool to empirically assess DICs, addressing a critical gap in the field. Furthermore, it empirically verifies the significant positive impact of DICs on innovation performance, offering clear guidance for managers seeking to enhance innovation outcomes and formulate digital innovation strategies. Notwithstanding these contributions, limitations include reliance on cross-sectional data and sectoral concentration. Future research should pursue longitudinal designs to track capability evolution and examine sector-specific moderators.