Adaptive Management of Hybrid Digital Resources of Enterprise Based on Modified Cobba – Douglas Production Function in Conditions of Industry 4.0

The article describes overcoming the conceptual gap between classical theories of economic growth and practical requirements to management of today’s industrial enterprises in context of Industry 4.0. Focus is made on new managerial paradigm necessary to use effectively hybrid digital resources, i.e. integrated assets including material, informational and intellectual components. The goal of the research is to substantiate theoretically and develop methodologically the approach to adaptive management of hybrid digital resources of enterprise based on modified Cobba – Douglas production function providing synergy of traditional and digital production factors in conditions of turbulence and fast technological obsolescence. The author shows evolution of theoretical ideas about economic growth from neo-classical modes relying on exogenous factors to the development of paradigm of exogenous growth and economy of knowledge, where innovation, human capital and information play the key role. Criticism of methodological restrictions in classical range of tools, including standard Cobba – Douglas function in today’s context was provided. Paradigm succession in current Russian investigations that demonstrates backwardness of managerial practices from theoretical trends was empirically confirmed. The article formulated the response to global challenges of reindustrialization and digitalization of Russian economy, which requires integral systemic transformation of mechanisms of in-company resource management. On the basis of provided conclusions new problematic aspects were identified: limited statistic production functions necessary to estimate contribution of digital resources possessing properties of non-exclusiveness, non-competitiveness and network effect; arising of ‘digital gap’ in enterprise between technological potential and managerial culture. Modified Cobba – Douglas production function with a separate hybrid factor, i. e. AI can provide a key solution.

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