Robot adoption and enterprise R&D manipulation: Evidence from China

Zhongsheng Zhou, Zhuo Li, Shanzhong Du and June Cao

Technological Forecasting and Social Change, 2024, vol. 200, issue C

Abstract: Robot adoption has profoundly affected economies and societies as part of the continuous evolution of technology and associated industrial transformations. We use the country-industry-year industrial robots dataset published by the International Federation of Robotics, and refer to the “Bartik instrumental variable” method to construct the robot adoption index of listed companies in China's manufacturing industry. Through empirical tests, we find that robot adoption significantly inhibits enterprise research and development (R&D) manipulation, and the findings remain unchanged during a series of robustness tests. Based on information asymmetry and principal-agent theory, we propose that robot adoption inhibits enterprises' R&D manipulation through information, human, and governance effects. Furthermore, high media attention, low-intensity regional tax administration, the academic experience of CEOs, and high-quality internal controls are conducive to the adoption of robots to suppress R&D manipulation. Moreover, digital transformation and robot adoption play complementary roles in inhibiting R&D manipulation. Finally, we verify that robot adoption can improve enterprises' production efficiency and reduce enterprise fraud. Overall, we enrich the research on robot adoption and enterprise R&D manipulation and provide experience for preventing enterprise R&D manipulation and promoting industrial robots to better serve the high-quality development of the real economy.

Keywords: Robot adoption; R&D manipulation; Information effect; Human effect; Governance effect (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:200:y:2024:i:c:s0040162523008193

DOI: 10.1016/j.techfore.2023.123134

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