Abstract
Contrary to the prevailing view that automation complements skilled labor in advanced economies, this study examines the unintended consequences of robot adoption in emerging markets. Drawing on strategic human capital theory, we exploit China's manufacturing sector as a natural experiment to identify the causal impact of industrial intelligence on firm-level innovation. Using a unique dataset of more than 90,000 public and private firms from the National Enterprise Innovation Database (2008–2014), we uncover a counterintuitive result: robot adoption significantly reduces firm innovation, which is notably more pronounced for private-owned firms and those with weaker innovation capabilities. Mediation analyses further show that this inhibitory effect operates through the degradation of firms' talent structures, as robots substitute for the technical middle layer essential for R&D absorption and knowledge integration. Moreover, two structural constraints moderate and amplify this effect. Path dependence leads firms to deploy robots to expand low-value, scale-oriented production, while high costs of imported equipment crowd out investments in human capital. Together, these findings reveal the challenges emerging economies face in converting hardware investment into innovation-driven growth without parallel talent upgrading.
Recommended Citation
Sun, W., Wu, Z., & Fang, C. (2026). Hardware Without Humanware: Robot Adoption, Talent Structure Degradation, and Firm Innovation. Asia Pacific Journal of Management Springer.
The definitive version is available at https://doi.org/10.1007/s10490-026-10135-8
Department(s)
Business and Information Technology
Keywords and Phrases
Emerging economies; Firm innovation; Path dependence; Robot adoption; Strategic human capital; Talent structure degradation
International Standard Serial Number (ISSN)
1572-9958; 0217-4561
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2026 Springer, All rights reserved.
Publication Date
01 Jan 2026

Comments
National Natural Science Foundation of China, Grant 72404049