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The notion of cyber-physical convergence, which indicates the pervasive integration of digital technologies in manufacturing, has rapidly gained prominence around the world because of its potential to accelerate economic productivity gains. A question of significance but relatively little empirical scrutiny is how cyber-physical convergence is characterised and realised in innovation generation, innovation adoption and innovation policy. These issues are addressed in this doctoral thesis, resulting in three journal-format papers. The first paper characterises and operationalises the notion of cyber-physical convergence, and then measures the growth and trajectories of scholarly research associated with the notion by employing text-mining and bibliometric approaches. The second paper is an inductive case study on the relative influences of geographical and non-geographical proximity factors in the adoption of digital technologies by small- and medium-sized automotive and aerospace firms (SMEs) co-localised in North West England. The third paper is an integrative literature review of three industrial policy initiatives - Germany's Industrie 4.0, Smart Manufacturing in the U.S. and the High Value Manufacturing in the U.K. - to analyse the framing and execution of policy vision to support specific types of technologies underpinning convergence. There are three main contributions of this thesis: first, it systematically delineates the cyber-physical convergence research domain into five data-centric capabilities - namely, Monitoring, Analytics, Modelling and Simulation, Transmission and Security - and sheds light on national research performance indicators for the period 2010-2019; second, it provides micro-level evidence indicating that geographical proximity among automotive and aerospace SMEs and also other types of economic actors is not the leading factor in technology adoption, rather institutional and cognitive (knowledge) factors play a more prominent role; and third, it offers insights pertaining to policy design and implementation, with Industrie 4.0 being more explicit and comprehensive than Smart Manufacturing and High Value Manufacturing in its framing, usage and implementation of a vision to support specific types of technologies. These contributions allow deriving policy and managerial implications regarding: the significance of data in manufacturing and funding data-centric capabilities, including questions of trade-offs between funding specific capabilities; the importance of interactions with non-localised actors for the purpose of technology adoption; and the consideration and systematic execution of policy vision to support and accelerate the realisation of cyber-physical convergence.