IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i14p6181-d1438769.html
   My bibliography  Save this article

Smart Manufacturing and Enterprise Breakthrough Innovation: Co-Existence Test of “U-Shaped” and Inverted “U-Shaped” Relationships in Chinese Listed Companies

Author

Listed:
  • Hui Guang

    (Business School, North Minzu University, Yinchuan 750021, China)

  • Ying Liu

    (Business School, North Minzu University, Yinchuan 750021, China)

  • Jiao Feng

    (Digital Economy and Smart Management Institute, Ningxia University, Yinchuan 750021, China)

  • Nan Wang

    (Labor and Human Resources School, Renmin University of China, Beijing 100872, China)

Abstract

This study, using the Technology Acceptance Model and Innovation Diffusion Theory, utilizes datasets from A-share manufacturing companies listed on China’s stock exchange from 2010 to 2022 to examine the impact of smart manufacturing on the dimensions of enterprise breakthrough innovation and the moderating role of service-oriented transformation. The findings reveal a “U-shaped” relationship between smart manufacturing and the width of breakthrough innovation, and an inverted “U-shaped” relationship between smart manufacturing and the depth of breakthrough innovation. Furthermore, enterprises’ service-oriented transformation positively moderates these relationships. This study is limited by its focus on Chinese listed companies, which may restrict the generalizability of the results to other regions. Future research should consider a broader sample, to validate and extend these findings. Nevertheless, the research findings provide a theoretical basis and practical insights for enterprises’ intelligent transformation and service transformation, promoting enterprise breakthrough innovation.

Suggested Citation

  • Hui Guang & Ying Liu & Jiao Feng & Nan Wang, 2024. "Smart Manufacturing and Enterprise Breakthrough Innovation: Co-Existence Test of “U-Shaped” and Inverted “U-Shaped” Relationships in Chinese Listed Companies," Sustainability, MDPI, vol. 16(14), pages 1-19, July.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:14:p:6181-:d:1438769
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/14/6181/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/16/14/6181/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Qiliang Wang & Qingquan Jiang & Hongxia Yu, 2023. "Analysis of the Influence of Entrepreneurial Apprehension and Entrepreneurial Strategic Orientation on Breakthrough Innovation," Sustainability, MDPI, vol. 15(9), pages 1-14, April.
    2. Marianna Makri & Michael A. Hitt & Peter J. Lane, 2010. "Complementary technologies, knowledge relatedness, and invention outcomes in high technology mergers and acquisitions," Strategic Management Journal, Wiley Blackwell, vol. 31(6), pages 602-628, June.
    3. Maliyamu Abudureheman & Qingzhe Jiang & Jiong Gong & Abulaiti Yiming, 2023. "Estimating and Decomposing the TFP Growth of Service-Oriented Manufacturing in China: A Translogarithmic Stochastic Frontier Approach," Sustainability, MDPI, vol. 15(7), pages 1-20, March.
    4. Lipeng Sun & Nur Ashikin Mohd Saat, 2023. "How Does Intelligent Manufacturing Affect the ESG Performance of Manufacturing Firms? Evidence from China," Sustainability, MDPI, vol. 15(4), pages 1-20, February.
    5. Takahashi, Carlos Kazunari & Figueiredo, Júlio César Bastos de & Scornavacca, Eusebio, 2024. "Investigating the diffusion of innovation: A comprehensive study of successive diffusion processes through analysis of search trends, patent records, and academic publications," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    6. Yi Zhang & Yue Qian & Ying Huang & Ying Guo & Guangquan Zhang & Jie Lu, 2017. "An entropy-based indicator system for measuring the potential of patents in technological innovation: rejecting moderation," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1925-1946, June.
    7. Marco Ardolino & Mario Rapaccini & Nicola Saccani & Paolo Gaiardelli & Giovanni Crespi & Carlo Ruggeri, 2018. "The role of digital technologies for the service transformation of industrial companies," International Journal of Production Research, Taylor & Francis Journals, vol. 56(6), pages 2116-2132, March.
    8. Liu, Jun & Chang, Huihong & Forrest, Jeffrey Yi-Lin & Yang, Baohua, 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    9. Jia Zhou & Aifang Guo & Yutao Chen & Jin Chen, 2022. "Original Innovation through Inter-Organizational Collaboration: Empirical Evidence from University-Focused Alliance Portfolio in China," Sustainability, MDPI, vol. 14(10), pages 1-18, May.
    10. Fontana, Roberto & Nuvolari, Alessandro & Shimizu, Hiroshi & Vezzulli, Andrea, 2013. "Reassessing patent propensity: Evidence from a dataset of R&D awards, 1977–2004," Research Policy, Elsevier, vol. 42(10), pages 1780-1792.
    11. Johnson, Prince Chacko & Laurell, Christofer & Ots, Mart & Sandström, Christian, 2022. "Digital innovation and the effects of artificial intelligence on firms’ research and development – Automation or augmentation, exploration or exploitation?," Technological Forecasting and Social Change, Elsevier, vol. 179(C).
    12. Lee, Chien-Chiang & Qin, Shuai & Li, Yaya, 2022. "Does industrial robot application promote green technology innovation in the manufacturing industry?," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    13. Talavera Fabra, Irene & Ghobadian, Abby & Troise, Ciro & Bresciani, Stefano, 2023. "Antecedents of successful diffusion of breakthrough innovations past the formative phase: Perceptions of innovation-engaged practitioners," Technovation, Elsevier, vol. 127(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lin, Boqiang & Xu, Chongchong, 2024. "The effects of industrial robots on firm energy intensity: From the perspective of technological innovation and electrification," Technological Forecasting and Social Change, Elsevier, vol. 203(C).
    2. Yang, Siying & Liu, Fengshuo, 2024. "Impact of industrial intelligence on green total factor productivity: The indispensability of the environmental system," Ecological Economics, Elsevier, vol. 216(C).
    3. Arias-Pérez, José & Vélez-Jaramillo, Juan, 2022. "Ignoring the three-way interaction of digital orientation, Not-invented-here syndrome and employee's artificial intelligence awareness in digital innovation performance: A recipe for failure," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    4. Zhang, Weike & Zeng, Ming, 2024. "Is artificial intelligence a curse or a blessing for enterprise energy intensity? Evidence from China," Energy Economics, Elsevier, vol. 134(C).
    5. Mariani, Manuel Sebastian & Medo, Matúš & Lafond, François, 2019. "Early identification of important patents: Design and validation of citation network metrics," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 644-654.
    6. Ding, Tao & Li, Hao & Liu, Li & Feng, Kui, 2024. "An inquiry into the nexus between artificial intelligence and energy poverty in the light of global evidence," Energy Economics, Elsevier, vol. 136(C).
    7. Oduro, Stephen & De Nisco, Alessandro & Mainolfi, Giada, 2023. "Do digital technologies pay off? A meta-analytic review of the digital technologies/firm performance nexus," Technovation, Elsevier, vol. 128(C).
    8. McCarthy, Killian J & Aalbers, Hendrik Leendert, 2022. "Alliance-to-acquisition transitions: The technological performance implications of acquiring one's alliance partners," Research Policy, Elsevier, vol. 51(6).
    9. Lin, Boqiang & Xu, Chongchong, 2024. "Enhancing energy-environmental performance through industrial intelligence: Insights from Chinese prefectural-level cities," Applied Energy, Elsevier, vol. 365(C).
    10. Jing Rong & Wei Wang & Haijun Zhang, 2024. "Does artificial intelligence improve energy productivity in China's industrial sector? Empirical evidence based on the spatial moderation model," Energy & Environment, , vol. 35(8), pages 4026-4048, December.
    11. Zhang, Wei & Ye, Shiqi & Mangla, Sachin Kumar & Emrouznejad, Ali & Song, Malin, 2024. "Smart platforming in automotive manufacturing for NetZero: Intelligentization, green technology, and innovation dynamics," International Journal of Production Economics, Elsevier, vol. 274(C).
    12. Wang, Hua & Liao, Lingtao & Wu, Ji (George), 2023. "Robot adoption and firm's capacity utilization: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    13. Sjödin, David & Parida, Vinit & Palmié, Maximilian & Wincent, Joakim, 2021. "How AI capabilities enable business model innovation: Scaling AI through co-evolutionary processes and feedback loops," Journal of Business Research, Elsevier, vol. 134(C), pages 574-587.
    14. Tang, Maogang & Liu, Yinlin & Hu, Fengxia & Wu, Baijun, 2023. "Effect of digital transformation on enterprises' green innovation: Empirical evidence from listed companies in China," Energy Economics, Elsevier, vol. 128(C).
    15. Zhang, Aili & Zhu, Han & Sun, Xinyu, 2024. "Manufacturing intelligentization and technological innovation: Perspectives on intra-industry impacts and inter-industry technology spillovers," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    16. Zhou, Wei & Zhuang, Yan & Chen, Yan, 2024. "How does artificial intelligence affect pollutant emissions by improving energy efficiency and developing green technology," Energy Economics, Elsevier, vol. 131(C).
    17. Manajit Chakraborty & Maksym Byshkin & Fabio Crestani, 2020. "Patent citation network analysis: A perspective from descriptive statistics and ERGMs," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-28, December.
    18. Kunkel, S. & Neuhäusler, P. & Matthess, M. & Dachrodt, M.F., 2023. "Industry 4.0 and energy in manufacturing sectors in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    19. Fındık, Derya & Tansel, Aysit, 2013. "Resources on the stage: a firm level analysis of the ict adoption in Turkey," MPRA Paper 65956, University Library of Munich, Germany, revised 05 Aug 2014.
    20. Avimanyu Datta, 2016. "Antecedents To Radical Innovations: A Longitudinal Look At Firms In The Information Technology Industry By Aggregation Of Patents," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 20(07), pages 1-31, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:16:y:2024:i:14:p:6181-:d:1438769. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.