Corporate digitalization enhances productivity, yet innovation quality lags behind

A new empirical study uncovers how digital transformation significantly improves capacity utilization rates in manufacturing firms, especially in emerging economies like China, but warns that over-reliance on policy incentives and peer imitation may hinder the quality of innovation. The research offers compelling insights into the intersection of technology, firm performance, and resource allocation in the industrial sector.

Published in the International Journal of Financial Studies, the research"Corporate Digital Transformation and Capacity Utilization Rate: The Functionary Path via Technological Innovation" leverages data from 14,578 observations of Chinese A-share manufacturing firms spanning 2011 to 2020. Using a robust econometric framework grounded in endogenous growth theory, the authors examine how digital technologies impact firms' productive efficiency and the mechanisms through which these impacts are mediated.

How does digital transformation affect capacity utilization?

The study recognizes the challenges of overcapacity in China's manufacturing sector, where firms often struggle with idle equipment, wasted resources, and sluggish innovation. Digital transformation is positioned as a solution to this structural inefficiency. Defined through the integration of core digital technologies, such as artificial intelligence, blockchain, cloud computing, and big data, the study argues that digital transformation acts as a catalyst for optimizing firm-level operations.

Using both benchmark and quantile regression models, the authors find that digital transformation significantly raises the capacity utilization rate (CUR) of manufacturing firms. On average, a 1% increase in digital transformation leads to a 0.2581% rise in CUR. However, the effects are not uniform across all firms. The most substantial gains are realized by firms with moderate baseline capacity utilization. Firms with very low or very high initial CUR show negligible benefits, with the former limited by resource constraints and the latter approaching the limits of their operational capacity.

The study distinguishes between foundational technologies and practical applications, concluding that it is the business-integrated applications of digital tools, such as smart logistics or intelligent manufacturing systems, that drive these improvements. The researchers also deploy alternative dependent variables and validate their results through instrumental variable regressions, Heckman two-stage modeling, and placebo tests. The evidence consistently confirms that digital transformation has a strong, positive effect on resource utilization.

Can digital technology promote both quantity and quality of innovation?

A key innovation in the research design is the decomposition of technological innovation into two dimensions: quantity and quality. The quantity of innovation is measured by R&D expenditure and the number of patents, while quality is assessed through patent knowledge width and citation counts. The study finds that while digital transformation significantly boosts both R&D investments and patent outputs, it fails to improve the quality of innovation.

Two structural distortions are identified as barriers to innovation quality. The first is a phenomenon the authors describe as "double arbitrage," where firms exploit government subsidies and favorable capital market conditions to produce high volumes of low-quality innovation. These firms tend to maximize patent counts to qualify for incentives rather than focus on the development of impactful or technically advanced innovations.

The second factor is the "cohort effect," where firms mimic the innovation strategies of their peers. This herding behavior leads to convergence around similar, often superficial, innovation outputs, diminishing the strategic value of digital transformation. The analysis finds that both double arbitrage and cohort effects significantly dampen the potential of digital tools to foster meaningful technological advancement.

The researchers caution that while digital transformation increases the capacity to innovate, it does not guarantee better innovation outcomes. They argue that unless the incentive structures in policy and market environments are reformed, the qualitative benefits of digital transformation will remain limited.

What are the broader economic and policy implications?

The study explores the broader economic consequences and spillover effects of digital transformation. Improved capacity utilization translates into tangible gains in profitability, measured through return on assets and return on equity, as well as enhanced total factor productivity. These findings indicate that digital transformation does not merely optimize operations, it also strengthens overall firm competitiveness and resilience.

More intriguingly, the study uncovers delayed but significant spillover effects within industries. While the digital transformation of one firm does not immediately affect the capacity utilization rates of its peers, the impact becomes positive and statistically significant in the third year. This lag reflects the time required for knowledge diffusion, supply chain coordination, and peer benchmarking to take effect. It also suggests that digital transformation could be a long-term driver of systemic industrial upgrades, provided the ecosystem is conducive.

From a policy perspective, the authors offer a series of actionable recommendations. First, they call for increased financial support for digital transformation, especially for small and medium-sized enterprises. These firms often lack the initial resources to invest in advanced technologies but stand to gain the most from operational efficiencies.

The researchers argue for a shift from quantity-based to quality-oriented innovation assessment. Government subsidies and market evaluations should prioritize technological depth, practical relevance, and long-term scalability over raw patent counts.

The study advocates for tiered policy frameworks that recognize the heterogeneous needs of different firms. While smaller firms require foundational infrastructure and digital literacy training, larger enterprises should be incentivized to act as innovation hubs that drive collaborative transformation across supply chains.