Robot adoption: evidence from perceived benefits and industry adoption pressure
Abstract
Recent research suggests that many firms have begun to adopt robots to take over tasks that were previously carried out by humans. However, current study still does not seem to explain the possible reasons and behavioral intentions for this robot adoption preference, and some scholars attribute them only to rising hiring costs, which may lead to a misleading impression, as firms adopting robots do not appear to reduce the wages of their workers. In this paper, we conduct examinations using robot data from the Chinese General Administration of Customs and a range of firm characteristics. The result shows that robot adoption willingness of firms depends more on their perceived benefits from robots and the pressure they feel from their peers than on rising employment costs, and that higher-skilled sectors tend to approach robots proactively, whereas lower-skilled sectors tend to be reactive in the robot adoption process. In addition, firms exhibit different behavioral choices when deciding whether to adopt robots, with those that have advantages in terms of firm size and employee capacity more likely to translate their perceived benefits of robots and industry adoption pressures into robot adoption behavior. This work offers a new explanation for the current phenomenon of robot substitution for human labor in Chinese firms and provide new evidence for technology adoption theory.
First published online 19 February 2025
Keyword : robot adoption, perceived benefits, industry adoption pressures, hiring costs
How to Cite
Mai, Q., Zhang, Q.- nan, Zhang, F., & Ji, F. (2025). Robot adoption: evidence from perceived benefits and industry adoption pressure. Technological and Economic Development of Economy, 1–21. https://doi.org/10.3846/tede.2025.22932
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Abbasi, G. A., Rahim, N. F. A., Wu, H., Iranmanesh, M. & Keong, B. N. C. (2022). Determinants of SME’s social media marketing adoption: Competitive industry as a moderator. SAGE Open, 12(1), 95–110. https://doi.org/10.1177/21582440211067220
Acemoglu, D., & Finkelstein, A. (2008). Input and technology choices in regulated industries: Evidence from the health care sector. Journal of Political Economy, 116(5), 837–880. https://doi.org/10.1086/595014
Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. https://doi.org/10.1257/jep.33.2.3
Acemoglu, D., & Restrepo, P. (2021). Demographics and automation. Review of Economic Studies, 89(1), 1–44. https://doi.org/10.1093/restud/rdab031
Adu-Gyamfi, G., Song, H., Nketiah, E., Obuobi, B., Adjei, M., & Cudjoe, D. (2022). Determinants of adoption intention of battery swap technology for electric vehicles. Energy, 251, Article 123862. https://doi.org/10.1016/j.energy.2022.123862
Alguacil, M., Turco, A. L., & Martínez-Zarzoso, I. (2022). Robot adoption and export performance: Firm-level evidence from Spain. Economic Modelling, 114, Article 105912. https://doi.org/10.1016/j.econmod.2022.105912
Bianchi, C., & Abu Saleh, M. (2020). Investigating SME importer–foreign supplier relationship trust and commitment. Journal of Business Research, 119, 572–584. https://doi.org/10.1016/j.jbusres.2020.07.023
Blanas, S., Gancia G., & Lee, S. Y. (2020). Who is afraid of machines? Economic Policy, 34(100), 627–690. https://doi.org/10.1093/epolic/eiaa005
Bonfiglioli, A., Crinò, R., Fadinger, H., & Gancia, G. (2020). Robot imports and firm-level outcomes (CESifo Working Paper No. 8741). SSRN. https://doi.org/10.2139/ssrn.3744604
Chau, N. T., Deng, H., & Tay, R. (2021). A perception-based model for mobile commerce adoption in Vietnamese small and medium-sized enterprises. Journal of Global Information Management, 29(1), 44–67. https://doi.org/10.4018/JGIM.2021010103
Chen, Q., Yuan, Y., Feng, Y., & Archer, N. (2020). A decision paradox: Benefit vs risk and trust vs distrust for online dating adoption vs non-adoption. Internet Research, 31(1), 341–375. https://doi.org/10.1108/INTR-07-2019-0304
Chong, L.-L., Ong, H.-B., & Tan, S.-H. (2021). Acceptability of mobile stock trading application: A study of young investors in Malaysia. Technology in Society, 64, Article 101497. https://doi.org/10.1016/j.techsoc.2020.101497
Coad, A., Segarra, A., & Teruel, M. (2016). Innovation and firm growth: Does firm age play a role? Research Policy, 45(2), 387–400. https://doi.org/10.1016/j.respol.2015.10.015
Cruz-Jesus, F., Pinheiro, A. & Oliveira, T. (2019). Understanding CRM adoption stages: Empirical analysis building on the TOE framework. Computers in Industry, 109, 1–13. https://doi.org/10.1016/j.compind.2019.03.007
Čater, T., Čater, B., Černe, M., Koman, M., & Redek, T. (2021). Industry 4.0 technologies usage: Motives and enablers. Journal of Manufacturing Technology Management, 32(9), 323–345. https://doi.org/10.1108/JMTM-01-2021-0026
Elia, S., Giuffrida, M., Mariani, M. M. & Bresciani, S. (2021). Resources and digital export: An RBV perspective on the role of digital technologies and capabilities in cross-border e-commerce. Journal of Business Research, 132(2), 158–169. https://doi.org/10.1016/j.jbusres.2021.04.010
Erol, S., Jäger, A., Hold, P., Ott, K., & Sihn, W. (2016). Tangible industry 4.0: A scenario-based approach to learning for the future of production. Procedia CIRP, 54, 13–18. https://doi.org/10.1016/j.procir.2016.03.162
Ghobakhloo, M., & Ching, N. T. (2019). Adoption of digital technologies of smart manufacturing in SMEs. Journal of Industrial Information Integration, 16, Article 100107. https://doi.org/10.1016/j.jii.2019.100107
Henao-Ramírez, A. M., & López-Zapata, E. (2022). Analysis of the factors influencing adoption of 3D design digital technologies in Colombian firms. Journal of Enterprise Information Management, 35(2), 429–454. https://doi.org/10.1108/JEIM-10-2020-0416
Hicks, J. (1932). The theory of wages. Macmillan.
Holl, A., & Mariotti, I. (2021). An empirical study of drivers for the adoption of logistics innovation. Industry and Innovation, 29(6), 760–791. https://doi.org/10.1080/13662716.2021.1990020
Huang, G., He, L.-Y., & Lin, X. (2022). Robot adoption and energy performance: Evidence from Chinese industrial firms. Energy Economics, 107, Article 105837. https://doi.org/10.1016/j.eneco.2022.105837
International Federation of Robotics. (2015). https://ifr.org/
Johansson, M., & Olhager, J. (2018). Manufacturing relocation through offshoring and backshoring: The case of Sweden. Journal of Manufacturing Technology Management, 29(4), 637–657. https://doi.org/10.1108/JMTM-01-2017-0006
Koch, M., Manuylov, I., & Smolka, M. (2021). Robots and firms. The Economic Journal, 131(638), 2553–2584. https://doi.org/10.1093/ej/ueab009
Lai, Y., Sun, H., & Ren, J. (2018) Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: An empirical investigation. The International Journal of Logistics Management, 29(2), 676–703. https://doi.org/10.1108/IJLM-06-2017-0153
Lin, C., Xiao, S., & Yin, Z. (2022). How do industrial robots applications affect the quality upgrade of Chinese export trade? Telecommunications Policy, 46(10), Article 102425. https://doi.org/10.1016/j.telpol.2022.102425
Lu, L., Liang, C., Gu, D., Ma, Y., Xie, Y., & Zhao, S. (2021). What advantages of blockchain affect its adoption in the elderly care industry? A study based on the technology-organisation-environment framework. Technology in Society, 67, Article 101786. https://doi.org/10.1016/j.techsoc.2021.101786
Mack, E. A., Miller, S. R., Chang, C.-H., van Fossen, J. A., Cotten, S. R., Savolainen, P. T., & Mann, J. (2021). The politics of new driving technologies: Political ideology and autonomous vehicle adoption. Telematics and Informatics, 61, Article 101604. https://doi.org/10.1016/j.tele.2021.101604
Mamonov, S. & Benbunan-Fich, R. (2020). Unlocking the smart home: Exploring key factors affecting the smart lock adoption intention. Information Technology & People, 34(2), 835–861. https://doi.org/10.1108/ITP-07-2019-0357
Medase, S. K. (2020). Product innovation and employees’ slack time. The moderating role of firm age & size. Journal of Innovation & Knowledge, 5(3), 151–174. https://doi.org/10.1016/j.jik.2019.11.001
Müller, J. M., Buliga, O., & Voigt, K.-I. (2018). Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0. Technological Forecasting and Social Change, 132, 2–17. https://doi.org/10.1016/j.techfore.2017.12.019
National Bureau of Statistics of China. (2015). https://data.stats.gov.cn/
Pillai, R., Sivathanu, B., Mariani, M., Rana, N. P., & Dwivedi, Y. K. (2021). Adoption of AI-empowered industrial robots in auto component manufacturing companies. Production Planning and Control, 33(16), 1517–1533. https://doi.org/10.1080/09537287.2021.1882689
Reyes, P. M., Li, S., & Visich, J. K. (2016). Determinants of RFID adoption stage and perceived benefits. European Journal of Operational Research, 254(3), 801–812. https://doi.org/10.1016/j.ejor.2016.03.051
Sandberg, S., Sui, S., & Baum, M. (2019). Effects of prior market experiences and firm-specific resources on developed economy SMEs’ export exit from emerging markets: Complementary or compensatory? Journal of Business Research, 98, 489–502. https://doi.org/10.1016/j.jbusres.2018.10.014
Song, J., Chen, Y., & Luan, F. (2023). Air pollution, water pollution, and robots: Is technology the panacea. Journal of Environmental Management, 330, Article 117170. https://doi.org/10.1016/j.jenvman.2022.117170
Swani, K. (2020). To app or not to app: A business-to-business seller’s decision. Industrial Marketing Management, 93, 389–400. https://doi.org/10.1016/j.indmarman.2020.05.033
Veile, J. W., Kiel, D., Müller, J. M. & Voigt, K.-I. (2019). Lessons learned from Industry 4.0 implementation in the German manufacturing industry. Journal of Manufacturing Technology Management, 31(5), 977–997. https://doi.org/10.1108/JMTM-08-2018-0270
Wang, W., Liu, Y. F., & Peng, D. D. (2015). Research on effects of population aging on industrial upgrading. China Industrial Economics, 11, 47–61.
Wang, Y. Q., & Dong, W. (2020). How the rise of robots has affected China’s labor market: Evidence from China’s listed manufacturing firms. Economic Research Journal, 55(10), 159–175.
Wang, L. H., Hu, C. M., & Dong, Z. Q. (2020). Will artificial intelligence technology induce labor income inequality – Model deduction and classification evaluation. China Industrial Economics, 385(4), 97–115.
Wang, E.-Z., Lee, C.-C., & Li, Y. (2022a). Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries. Energy Economics, 105, Article 105748. https://doi.org/10.1016/j.eneco.2021.105748
Wang, X., Liu, L., Liu, J., & Huang, X. (2022b). Understanding the determinants of blockchain technology adoption in the construction industry. Buildings, 12(10), Article 1709. https://doi.org/10.3390/buildings12101709
Wen, H., Lee, C.-C., & Zhou, F. (2021). Green credit policy, credit allocation efficiency and upgrade of energy-intensive enterprises. Energy Economics, 94, Article 105099. https://doi.org/10.1016/j.eneco.2021.105099
Wong, L.-W., Leong, L.-Y., Hew, J.-J., Tan, G. W.-H., & Ooi, K.-B. (2020). Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs. International Journal of Information Management, 52, Article 101997. https://doi.org/10.1016/j.ijinfomgt.2019.08.005
Xue, M., Cao, X., Feng, X., Gu, B., & Zhang, Y. (2022). Is college education less necessary with AI? Evidence from firm-level labor structure changes. Journal of Management Information Systems, 39(3), 865–905. https://doi.org/10.1080/07421222.2022.2096542
Yoon, C., Lim, D., & Park, C. (2020). Factors affecting adoption of smart farms: The case of Korea. Computers in Human Behavior, 108, Article 106309. https://doi.org/10.1016/j.chb.2020.106309
Yoon, S., & Oh, J. (2022). A theory-based approach to the usability of augmented reality technology: A cost-benefit perspective. Technology in Society, 68, Article 101860. https://doi.org/10.1016/j.techsoc.2022.101860
Yu, M. (2015). Processing trade, tariff reductions and firm productivity: Evidence from Chinese firms. The Economic Journal, 125(585), 943–988. https://doi.org/10.1111/ecoj.12127
Zhang, Q., Zhang, F., & Mai, Q. (2022). Robot adoption and green productivity: Curse or Boon. Sustainable Production and Consumption, 34, 1–11. https://doi.org/10.1016/j.spc.2022.08.025
Zhang, F., Zhang, Q., & Wu, H. (2023a). Robot adoption and export performance: Evidence from Chinese industrial firms. Journal of Manufacturing Technology Management, 34(6), 896–916. https://doi.org/10.1108/JMTM-09-2022-0315
Zhang, Q.-n., Zhang, F.-f., & Mai, Q. (2023b). Robot adoption and labor demand: A new interpretation from external competition. Technology in Society, 74, Article 102310. https://doi.org/10.1016/j.techsoc.2023.102310
Zhou, P., Zhang, J., & Jiang, K. (2024). Technological disruption and patent activities: Adoption of robots by Chinese manufacturing firms. R & D Management, 1–23. https://doi.org/10.1111/radm.12701
Zhu, Z. J., Yuan, Y. M., & Jiao, J. J. (2022). Industrial automation and manufacturing innovation behavior. China Industrial Economics, (7), 84–102.
Acemoglu, D., & Finkelstein, A. (2008). Input and technology choices in regulated industries: Evidence from the health care sector. Journal of Political Economy, 116(5), 837–880. https://doi.org/10.1086/595014
Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. https://doi.org/10.1257/jep.33.2.3
Acemoglu, D., & Restrepo, P. (2021). Demographics and automation. Review of Economic Studies, 89(1), 1–44. https://doi.org/10.1093/restud/rdab031
Adu-Gyamfi, G., Song, H., Nketiah, E., Obuobi, B., Adjei, M., & Cudjoe, D. (2022). Determinants of adoption intention of battery swap technology for electric vehicles. Energy, 251, Article 123862. https://doi.org/10.1016/j.energy.2022.123862
Alguacil, M., Turco, A. L., & Martínez-Zarzoso, I. (2022). Robot adoption and export performance: Firm-level evidence from Spain. Economic Modelling, 114, Article 105912. https://doi.org/10.1016/j.econmod.2022.105912
Bianchi, C., & Abu Saleh, M. (2020). Investigating SME importer–foreign supplier relationship trust and commitment. Journal of Business Research, 119, 572–584. https://doi.org/10.1016/j.jbusres.2020.07.023
Blanas, S., Gancia G., & Lee, S. Y. (2020). Who is afraid of machines? Economic Policy, 34(100), 627–690. https://doi.org/10.1093/epolic/eiaa005
Bonfiglioli, A., Crinò, R., Fadinger, H., & Gancia, G. (2020). Robot imports and firm-level outcomes (CESifo Working Paper No. 8741). SSRN. https://doi.org/10.2139/ssrn.3744604
Chau, N. T., Deng, H., & Tay, R. (2021). A perception-based model for mobile commerce adoption in Vietnamese small and medium-sized enterprises. Journal of Global Information Management, 29(1), 44–67. https://doi.org/10.4018/JGIM.2021010103
Chen, Q., Yuan, Y., Feng, Y., & Archer, N. (2020). A decision paradox: Benefit vs risk and trust vs distrust for online dating adoption vs non-adoption. Internet Research, 31(1), 341–375. https://doi.org/10.1108/INTR-07-2019-0304
Chong, L.-L., Ong, H.-B., & Tan, S.-H. (2021). Acceptability of mobile stock trading application: A study of young investors in Malaysia. Technology in Society, 64, Article 101497. https://doi.org/10.1016/j.techsoc.2020.101497
Coad, A., Segarra, A., & Teruel, M. (2016). Innovation and firm growth: Does firm age play a role? Research Policy, 45(2), 387–400. https://doi.org/10.1016/j.respol.2015.10.015
Cruz-Jesus, F., Pinheiro, A. & Oliveira, T. (2019). Understanding CRM adoption stages: Empirical analysis building on the TOE framework. Computers in Industry, 109, 1–13. https://doi.org/10.1016/j.compind.2019.03.007
Čater, T., Čater, B., Černe, M., Koman, M., & Redek, T. (2021). Industry 4.0 technologies usage: Motives and enablers. Journal of Manufacturing Technology Management, 32(9), 323–345. https://doi.org/10.1108/JMTM-01-2021-0026
Elia, S., Giuffrida, M., Mariani, M. M. & Bresciani, S. (2021). Resources and digital export: An RBV perspective on the role of digital technologies and capabilities in cross-border e-commerce. Journal of Business Research, 132(2), 158–169. https://doi.org/10.1016/j.jbusres.2021.04.010
Erol, S., Jäger, A., Hold, P., Ott, K., & Sihn, W. (2016). Tangible industry 4.0: A scenario-based approach to learning for the future of production. Procedia CIRP, 54, 13–18. https://doi.org/10.1016/j.procir.2016.03.162
Ghobakhloo, M., & Ching, N. T. (2019). Adoption of digital technologies of smart manufacturing in SMEs. Journal of Industrial Information Integration, 16, Article 100107. https://doi.org/10.1016/j.jii.2019.100107
Henao-Ramírez, A. M., & López-Zapata, E. (2022). Analysis of the factors influencing adoption of 3D design digital technologies in Colombian firms. Journal of Enterprise Information Management, 35(2), 429–454. https://doi.org/10.1108/JEIM-10-2020-0416
Hicks, J. (1932). The theory of wages. Macmillan.
Holl, A., & Mariotti, I. (2021). An empirical study of drivers for the adoption of logistics innovation. Industry and Innovation, 29(6), 760–791. https://doi.org/10.1080/13662716.2021.1990020
Huang, G., He, L.-Y., & Lin, X. (2022). Robot adoption and energy performance: Evidence from Chinese industrial firms. Energy Economics, 107, Article 105837. https://doi.org/10.1016/j.eneco.2022.105837
International Federation of Robotics. (2015). https://ifr.org/
Johansson, M., & Olhager, J. (2018). Manufacturing relocation through offshoring and backshoring: The case of Sweden. Journal of Manufacturing Technology Management, 29(4), 637–657. https://doi.org/10.1108/JMTM-01-2017-0006
Koch, M., Manuylov, I., & Smolka, M. (2021). Robots and firms. The Economic Journal, 131(638), 2553–2584. https://doi.org/10.1093/ej/ueab009
Lai, Y., Sun, H., & Ren, J. (2018) Understanding the determinants of big data analytics (BDA) adoption in logistics and supply chain management: An empirical investigation. The International Journal of Logistics Management, 29(2), 676–703. https://doi.org/10.1108/IJLM-06-2017-0153
Lin, C., Xiao, S., & Yin, Z. (2022). How do industrial robots applications affect the quality upgrade of Chinese export trade? Telecommunications Policy, 46(10), Article 102425. https://doi.org/10.1016/j.telpol.2022.102425
Lu, L., Liang, C., Gu, D., Ma, Y., Xie, Y., & Zhao, S. (2021). What advantages of blockchain affect its adoption in the elderly care industry? A study based on the technology-organisation-environment framework. Technology in Society, 67, Article 101786. https://doi.org/10.1016/j.techsoc.2021.101786
Mack, E. A., Miller, S. R., Chang, C.-H., van Fossen, J. A., Cotten, S. R., Savolainen, P. T., & Mann, J. (2021). The politics of new driving technologies: Political ideology and autonomous vehicle adoption. Telematics and Informatics, 61, Article 101604. https://doi.org/10.1016/j.tele.2021.101604
Mamonov, S. & Benbunan-Fich, R. (2020). Unlocking the smart home: Exploring key factors affecting the smart lock adoption intention. Information Technology & People, 34(2), 835–861. https://doi.org/10.1108/ITP-07-2019-0357
Medase, S. K. (2020). Product innovation and employees’ slack time. The moderating role of firm age & size. Journal of Innovation & Knowledge, 5(3), 151–174. https://doi.org/10.1016/j.jik.2019.11.001
Müller, J. M., Buliga, O., & Voigt, K.-I. (2018). Fortune favors the prepared: How SMEs approach business model innovations in Industry 4.0. Technological Forecasting and Social Change, 132, 2–17. https://doi.org/10.1016/j.techfore.2017.12.019
National Bureau of Statistics of China. (2015). https://data.stats.gov.cn/
Pillai, R., Sivathanu, B., Mariani, M., Rana, N. P., & Dwivedi, Y. K. (2021). Adoption of AI-empowered industrial robots in auto component manufacturing companies. Production Planning and Control, 33(16), 1517–1533. https://doi.org/10.1080/09537287.2021.1882689
Reyes, P. M., Li, S., & Visich, J. K. (2016). Determinants of RFID adoption stage and perceived benefits. European Journal of Operational Research, 254(3), 801–812. https://doi.org/10.1016/j.ejor.2016.03.051
Sandberg, S., Sui, S., & Baum, M. (2019). Effects of prior market experiences and firm-specific resources on developed economy SMEs’ export exit from emerging markets: Complementary or compensatory? Journal of Business Research, 98, 489–502. https://doi.org/10.1016/j.jbusres.2018.10.014
Song, J., Chen, Y., & Luan, F. (2023). Air pollution, water pollution, and robots: Is technology the panacea. Journal of Environmental Management, 330, Article 117170. https://doi.org/10.1016/j.jenvman.2022.117170
Swani, K. (2020). To app or not to app: A business-to-business seller’s decision. Industrial Marketing Management, 93, 389–400. https://doi.org/10.1016/j.indmarman.2020.05.033
Veile, J. W., Kiel, D., Müller, J. M. & Voigt, K.-I. (2019). Lessons learned from Industry 4.0 implementation in the German manufacturing industry. Journal of Manufacturing Technology Management, 31(5), 977–997. https://doi.org/10.1108/JMTM-08-2018-0270
Wang, W., Liu, Y. F., & Peng, D. D. (2015). Research on effects of population aging on industrial upgrading. China Industrial Economics, 11, 47–61.
Wang, Y. Q., & Dong, W. (2020). How the rise of robots has affected China’s labor market: Evidence from China’s listed manufacturing firms. Economic Research Journal, 55(10), 159–175.
Wang, L. H., Hu, C. M., & Dong, Z. Q. (2020). Will artificial intelligence technology induce labor income inequality – Model deduction and classification evaluation. China Industrial Economics, 385(4), 97–115.
Wang, E.-Z., Lee, C.-C., & Li, Y. (2022a). Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries. Energy Economics, 105, Article 105748. https://doi.org/10.1016/j.eneco.2021.105748
Wang, X., Liu, L., Liu, J., & Huang, X. (2022b). Understanding the determinants of blockchain technology adoption in the construction industry. Buildings, 12(10), Article 1709. https://doi.org/10.3390/buildings12101709
Wen, H., Lee, C.-C., & Zhou, F. (2021). Green credit policy, credit allocation efficiency and upgrade of energy-intensive enterprises. Energy Economics, 94, Article 105099. https://doi.org/10.1016/j.eneco.2021.105099
Wong, L.-W., Leong, L.-Y., Hew, J.-J., Tan, G. W.-H., & Ooi, K.-B. (2020). Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs. International Journal of Information Management, 52, Article 101997. https://doi.org/10.1016/j.ijinfomgt.2019.08.005
Xue, M., Cao, X., Feng, X., Gu, B., & Zhang, Y. (2022). Is college education less necessary with AI? Evidence from firm-level labor structure changes. Journal of Management Information Systems, 39(3), 865–905. https://doi.org/10.1080/07421222.2022.2096542
Yoon, C., Lim, D., & Park, C. (2020). Factors affecting adoption of smart farms: The case of Korea. Computers in Human Behavior, 108, Article 106309. https://doi.org/10.1016/j.chb.2020.106309
Yoon, S., & Oh, J. (2022). A theory-based approach to the usability of augmented reality technology: A cost-benefit perspective. Technology in Society, 68, Article 101860. https://doi.org/10.1016/j.techsoc.2022.101860
Yu, M. (2015). Processing trade, tariff reductions and firm productivity: Evidence from Chinese firms. The Economic Journal, 125(585), 943–988. https://doi.org/10.1111/ecoj.12127
Zhang, Q., Zhang, F., & Mai, Q. (2022). Robot adoption and green productivity: Curse or Boon. Sustainable Production and Consumption, 34, 1–11. https://doi.org/10.1016/j.spc.2022.08.025
Zhang, F., Zhang, Q., & Wu, H. (2023a). Robot adoption and export performance: Evidence from Chinese industrial firms. Journal of Manufacturing Technology Management, 34(6), 896–916. https://doi.org/10.1108/JMTM-09-2022-0315
Zhang, Q.-n., Zhang, F.-f., & Mai, Q. (2023b). Robot adoption and labor demand: A new interpretation from external competition. Technology in Society, 74, Article 102310. https://doi.org/10.1016/j.techsoc.2023.102310
Zhou, P., Zhang, J., & Jiang, K. (2024). Technological disruption and patent activities: Adoption of robots by Chinese manufacturing firms. R & D Management, 1–23. https://doi.org/10.1111/radm.12701
Zhu, Z. J., Yuan, Y. M., & Jiao, J. J. (2022). Industrial automation and manufacturing innovation behavior. China Industrial Economics, (7), 84–102.