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Open AccessArticle
How Can High-Tech Manufacturing Achieve High Total Factor Productivity? A Dynamic QCA Under the TOE Framework
by
Juan Lin
Juan Lin *,†,
Mengchao Sun
Mengchao Sun †,
Zhen Peng
Zhen Peng and
Jianying Niu
Jianying Niu
School of Economics and Management, China University of Geosciences (Beijing), Beijing 100083, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Systems 2026, 14(5), 574; https://doi.org/10.3390/systems14050574 (registering DOI)
Submission received: 11 April 2026
/
Revised: 13 May 2026
/
Accepted: 15 May 2026
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Published: 18 May 2026
Abstract
High-tech manufacturing is a technology- and knowledge-intensive strategic industry. Its total factor productivity (TFP) directly impacts national competitiveness and economic quality. In China, despite rapid growth, TFP performance varies across sub-sectors and firms. In this study, TFP was adopted as the central outcome variable to capture the comprehensive production and technological efficiency of high-tech manufacturing firms. The Technology–Organization–Environment (TOE) framework was integrated with Dynamic Qualitative Comparative Analysis (Dynamic QCA) to examine the causal complexity, dynamic evolution, and industrial heterogeneity of TFP, using a sample of Chinese A-share-listed companies from 2015 to 2024. The results showed that high TFP depends on configurations rather than on a single factor. Three configurational paths were identified, including “technology–innovation–scale synergy,” “technology–scale dual core,” and “technology-led productivity optimization.” All paths require a strong technological foundation. Conversely, a lack of technology leads to low total factor productivity across all sectors. Moreover, the effectiveness of these pathways evolves over time. The dual-core pathway serves as a stable baseline model. The synergy pathway is reinforced in fast-iteration sectors. Due to weak innovation support, the productivity optimization pathway declined after 2019. Third, different sectors show distinct patterns. Fast-iteration sectors use synergy to handle rapid technical changes. Slow-iteration sectors use the dual-core model to share R&D risks. Productivity-optimized sectors stagnate because they focus on automation instead of innovation. This work reveals deep patterns in TFP growth and provides theoretical support and practical insight for strategic choices of firms, industry resource allocation, and industrial policy optimization.
Share and Cite
MDPI and ACS Style
Lin, J.; Sun, M.; Peng, Z.; Niu, J.
How Can High-Tech Manufacturing Achieve High Total Factor Productivity? A Dynamic QCA Under the TOE Framework. Systems 2026, 14, 574.
https://doi.org/10.3390/systems14050574
AMA Style
Lin J, Sun M, Peng Z, Niu J.
How Can High-Tech Manufacturing Achieve High Total Factor Productivity? A Dynamic QCA Under the TOE Framework. Systems. 2026; 14(5):574.
https://doi.org/10.3390/systems14050574
Chicago/Turabian Style
Lin, Juan, Mengchao Sun, Zhen Peng, and Jianying Niu.
2026. "How Can High-Tech Manufacturing Achieve High Total Factor Productivity? A Dynamic QCA Under the TOE Framework" Systems 14, no. 5: 574.
https://doi.org/10.3390/systems14050574
APA Style
Lin, J., Sun, M., Peng, Z., & Niu, J.
(2026). How Can High-Tech Manufacturing Achieve High Total Factor Productivity? A Dynamic QCA Under the TOE Framework. Systems, 14(5), 574.
https://doi.org/10.3390/systems14050574
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