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| Can Industrial Intelligence Improve the Quality of Information Disclosure? Evidence from Manufacturing Listed Firms in China |
| TU Manman, CAO Chunfang, DU Shanzhong
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School of Economics & Management, South China Normal University;
School of Business, Sun Yat-sen University;
International School of Business & Finance, Sun Yat-sen University |
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Abstract With the extensive adoption of industrial intelligence, technologies such as industrial robots are rapidly permeating various manufacturing processes and driving enterprises' strategic upgrading. According to data from the International Federation of Robotics, China continues to lead the global market, with 290,258 new industrial robots installed in 2022, accounting for more than 50% of the global total, and maintaining a compound annual growth rate of 32.55% over the past decade. Although previous studies indicate that industrial robots significantly enhance firms' productivity and flexibility, profoundly reshaping labor markets, its influence on corporate information disclosure quality and the underlying mechanisms remains underexplored. Given that high-quality information disclosure is essential for effective capital market functioning, investigating this issue has both theoretical and practical significance.
We measure the quality of management earnings forecasts from accuracy and precision, and construct firm-level industrial intelligence adoption based on the identification of related construction-in-progress and R&D expenditures projects from financial statement disclosures. Using a panel of manufacturing listed firms in Chinese A-share markets from 2011 to 2022, our results show that higher industrial intelligence adoption significantly improves the accuracy and precision of management earnings forecasts. This positive effect is primarily driven by reduced uncertainty in production costs associated with automated production, and it is more pronounced in regions with higher statutory minimum wages, greater high-skilled labor supply, and firms with higher inventory ratios. Additionally, industrial intelligence reduces income volatility through greater production controllability, especially in firms experiencing higher product demand volatility and more diversified product portfolios. Further heterogeneity analysis indicates that the positive impact of industrial intelligence adoption is significantly stronger in labor-intensive firms or those facing intense market competition.
This study contributes to the literature in three main ways. First, it extends the literature on the economic consequences of industrial intelligence adoption by focusing on the quality of management earnings forecasts. While prior research has primarily examined how robot exposure affects firms' labor decisions and cost management behaviors, this paper investigates how industrial intelligence adoption improves cost controllability and reduces income volatility, thereby enhancing disclosure quality. Second, it enriches the set of determinants of management earnings forecasts' quality. Existing studies have emphasized external factors such as litigation risk and internal governance mechanisms like internal controls, which mainly influence intentional bias in forecasts. In contrast, this study highlights that forecast quality also depends on the quantity and reliability of information available to managers. It shows that industrial intelligence provides managers with more precise and stable operational data, thereby improving the accuracy and precision of earnings forecasts. Third, the study introduces a replicable, fine-grained metric of firm-level industrial intelligence investment derived from financial statement disclosures, offering a new template for researchers interested in tracing technological adoption.
The policy implications of this study are as follows: Corporate managers should promote the deep integration of intelligent manufacturing and accounting information systems to ensure more timely and accurate access to information, thereby improving decision-making efficiency. Firms should adopt intelligent strategies tailored to their labor structures and competitive conditions. Labor-intensive firms should proactively adopt automation to mitigate labor-market uncertainties, while highly competitive firms can leverage intelligent systems to enhance forecasting and disclosure quality. For policymakers, targeted policy support should be implemented based on regional industrial characteristics and market conditions. On one hand, improving talent policies and support services can help ensure an adequate supply of skilled labor for “human-machine matching.” On the other hand, fiscal and financial incentives should be provided to firms in highly competitive sectors. Finally, when formulating labor policies such as minimum wage standards, governments should carefully consider the “human-machine substitution effect” driven by rising labor costs, in order to strike a balance between labor protection and industrial intelligence adoption, and to achieve coordinated progress in intelligent upgrading and employment stability.
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Received: 25 September 2024
Published: 02 October 2025
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