Methodology for measuring the digital maturity of industrial enterprises and its relationship with the performance of automation, innovation activity, and export competitiveness

Abstract

The article proposes a methodology for measuring the digital maturity of industrial enterprises and analyzing its relationship with the performance of automation, innovation activity, and export competitiveness under conditions of the “productivity paradox” and high volatility of the external environment. Based on panel data from 482 enterprises in the manufacturing, mechanical engineering, and chemical industries for 2018–2023, an integrated digital maturity index was developed, including 45 indicators grouped into five clusters (strategy and culture, organizational model, data and analytics, technologies and automation, customer experience). To quantitatively assess the impact of digital maturity on ROA, ROIC, export revenue, and innovation performance, correlation-regression analysis, factor and cluster analysis, SEM modeling, Granger causality tests, and benchmarking methods were applied. Pronounced threshold effects were identified: at the early stages of digitalization, the increase in IT costs is hardly converted into improved margins and export shares, whereas the transition to integrated digital ecosystems is accompanied by a sharp increase in revenue CAGR, EBITDA margin, asset turnover, and labor productivity. It was found that automation of innovation and R&D activities provides the highest growth in the share of new products in revenue and project ROI, while deep digitalization of logistics and compliance reduces the share of logistics costs and the level of claims, simultaneously increasing the average value of export contracts and the foreign customer retention index. It is substantiated that digital maturity represents a dynamic characteristic of an enterprise’s ability to convert data into cash flow, and that the “valley of death” of digitalization is associated with a temporary deterioration in financial performance due to investment pressure and the inefficiency of fragmented automation. The results can be used to adjust corporate digital transformation roadmaps and to develop state support tools aimed at achieving target maturity levels and export KPIs.