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This paper establishes a theoretical framework for understanding the connotations of industrial basic capacity. It employs models from economic growth theory to derive indices for assessing industrial basic capacity and exploring the economic correlations among its influencing factors. Additionally, it measures the industrial basic capacity indices of 17 subsectors across 9 major industrial countries from 2000 to 2020 using OECD data. The findings reveal that from 2000 to 2020, the Chinese manufacturing industry has surpassed the United States, becoming the global leader. Specifically, within the 17 subsectors, 9 are globally ranked first, with 7 nearing advanced levels, and only 1 facing relative backwardness. Chinese manufacturing industry’s enhanced basic capacity is attributed to advantages in cost competitiveness and scale. However, significant disparities persist in technological input and industrial linkages with advanced nations. The decline in basic capacity among developed countries stems primarily from diminished value chain profitability due to inadequate investment. Sustainable improvement in industry basic capacity necessitates concurrent advancements in value chain profitability, fixed asset investment, technological levels, industrial linkages, and market scale. Overreliance on cost advantages or advanced technology poses substitution risks. Moreover, this paper underscores the limitations of exclusively relying on current data to assess global industrial basic capacity, advocating for a greater historical perspective. To strengthen the Chinese manufacturing industrial basic capacity within the global value chain, the Chinese manufacturing industry must enhance technological inputs, reduce the operational costs of enterprises, and elevate the degree of openness.

The high-quality development of the industrial software industry is of strategic significance to enhancing the core competitiveness of the manufacturing industry and promoting the high-quality development of China’s industrial economy. By integrating the “capital-technology-environment-human” production factor theory and configuration perspective, this paper constructs a comprehensive analysis framework that drives the total factor productivity (TFP) of the industrial software industry. This paper uses 40 typical industrial software firms in 2018–2020 as case samples and uses fuzzy set Qualitative Comparative Analysis (fsQCA) to empirically explore the influencing factors and complex mechanisms that achieve high-quality development of the industrial software industry. It is found that: (1) a single industrial factor is hardly a necessary condition to drive the industrial software industry; (2) there are four paths to achieving high TFP, which are summarized as “technical-human-environmental” balanced driving type, “capital-human-environmental” balanced driving type, “technical-capital” dual driving type, and “capital” single driving type. There are four driving mechanisms. There are also four not-high TFP configurations with asymmetric characteristics; (3) under certain conditions, the combination of capital factors, technical factors, environmental factors, and human factors can drive TFP in an “all roads lead to Rome”. In this process, the government’s attention plays a more universal role. The study not only expands the application scenarios of fsQCA but also provides decision guidelines for the practice of strategic emerging industrialization represented by the industrial software industry.

To reasonably guide and promote the high-quality development of China’s software industry through policies, and to improve the total factor productivity (TFP) of China’s software industry are the inevitable requirements of the conjunctive development. Previous research mainly used econometric methods to explore the impact of specific variables or factors in different regions on the TFP of the software industry. Here we provide a solution for the path selection to improve the TFP of the software industry. The DEA-Malmquist index analysis method to measure the TFP of the software industry and the fuzzy set qualitative comparative analysis method (fsQCA) are adopted to explore the different promotion paths of provinces based on the relevant data of 29 provinces in China. The results show that 5 path configurations achieve high TFP. Specifically, regions with high TFP in the software industry tend to be those with high enterprise scale, high R&D investment, and high R&D personnel investment. When the scale of enterprises is high, the region should fully consider the degree of R&D investment and the degree of higher education. When the investment of R&D personnel is high, the high education level of human factors and the investment intensity of fixed assets of capital factors should be brought into full play. When the investment in fixed assets and R&D investment are high, the region should fully consider the investment of R&D personnel and the scale of the enterprise.