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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
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    References listed on IDEAS

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    1. 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).
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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).
    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. 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.
    9. 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).
    10. 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).
    11. 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.
    12. 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.
    13. 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).
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