Robot adoption and enterprise R&D manipulation: evidence from China
Robot adoption and enterprise R&D manipulation: evidence from China
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.
Zhou, Zhongshegn
944e69bf-9f8e-4f77-8ffc-86778ded8392
Li, Zhuo
8f5d1624-d9d0-443b-82b7-e002cd91a019
Du, Shanzhong
b9ea09e6-5fbc-435f-b208-a495c66ec8e8
Cao, June
af0d62ff-d54c-412f-a152-cc04c63c7290
29 December 2023
Zhou, Zhongshegn
944e69bf-9f8e-4f77-8ffc-86778ded8392
Li, Zhuo
8f5d1624-d9d0-443b-82b7-e002cd91a019
Du, Shanzhong
b9ea09e6-5fbc-435f-b208-a495c66ec8e8
Cao, June
af0d62ff-d54c-412f-a152-cc04c63c7290
Zhou, Zhongshegn, Li, Zhuo, Du, Shanzhong and Cao, June
(2023)
Robot adoption and enterprise R&D manipulation: evidence from China.
Technological Forecasting and Social Change, 200, [123134].
(doi:10.1016/j.techfore.2023.123134).
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.
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22 Robot adoption and enterprise R&D manipulation Evidence from China
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Accepted/In Press date: 16 December 2023
e-pub ahead of print date: 29 December 2023
Published date: 29 December 2023
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Local EPrints ID: 501355
URI: http://eprints.soton.ac.uk/id/eprint/501355
ISSN: 0040-1625
PURE UUID: 205bb4f2-3a0f-497d-86dd-96f771dbaa23
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Date deposited: 29 May 2025 16:50
Last modified: 22 Aug 2025 02:49
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Author:
Zhongshegn Zhou
Author:
Zhuo Li
Author:
Shanzhong Du
Author:
June Cao
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