IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2024i16p7018-d1457246.html
   My bibliography  Save this article

Knowledge Transfer within Enterprises from the Perspective of Innovation Quality Management: A Decision Analysis Based on the Stackelberg Game

Author

Listed:
  • Shumei Wang

    (School of Computer and Information Engineering, Harbin University of Commerce, Harbin 150028, China)

  • Ming Sun

    (College of Computer and Control Engineering, Qiqihar University, Qiqihar 161006, China)

  • Yaoqun Xu

    (Institute of System Engineering, Harbin University of Commerce, Harbin 150028, China)

Abstract

It is of great significance to study the effectiveness of knowledge transfer between the new and the veteran employees within enterprises for promoting sustainable innovation from the perspective of innovation quality management. However, few studies have examined the impact of innovation quality on the effectiveness of knowledge transfer between the new and veteran employees. In addition, knowledge of how reward and punishment incentives affect the effectiveness of knowledge transfer in innovation quality management remains insufficient. Since the amount of knowledge transfer is an important aspect of the effectiveness of knowledge transfer, this paper constructs a Stackelberg game model with an innovation-quality-oriented threshold of the knowledge transfer amount and investigates the amount of knowledge transfer between new and veteran employees in the collaborative innovation of research and development (R&D) projects within enterprises. A case study was used to reveal that the innovation-quality-oriented threshold for the knowledge transfer amount can effectively promote the amount of knowledge transfer between the new and the veteran employees in collaborative innovation. Moreover, reward is more effective than punishment in promoting the amount of knowledge transfer to exceed the innovation-quality-oriented threshold. This study enriches the theories of knowledge transfer games under quality management. By virtue of end-to-end project management strategies, modern multimedia technologies, and reward incentives this study can be used to conduct quality control during project execution, to promote knowledge retention in R&D projects, the innovation quality of projects, and the achievement of the Sustainable Development Goals (SDGs). The research methodology employed in this paper was limited to a case study, and the data utilized are not empirical data.

Suggested Citation

  • Shumei Wang & Ming Sun & Yaoqun Xu, 2024. "Knowledge Transfer within Enterprises from the Perspective of Innovation Quality Management: A Decision Analysis Based on the Stackelberg Game," Sustainability, MDPI, vol. 16(16), pages 1-26, August.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:16:p:7018-:d:1457246
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/16/7018/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/16/7018/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhu, Hong-Miao & Zhang, Sheng-Tai & Jin, Zhen, 2016. "The effects of online social networks on tacit knowledge transmission," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 441(C), pages 192-198.
    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. Wang, Sixin & Mei, Jun & Xia, Dan & Yang, Zhanying & Hu, Junhao, 2022. "Finite-time optimal feedback control mechanism for knowledge transmission in complex networks via model predictive control," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    2. Zhu, He & Ma, Jing, 2018. "Knowledge diffusion in complex networks by considering time-varying information channels," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 494(C), pages 225-235.
    3. Evangelos Ioannidis & Nikos Varsakelis & Ioannis Antoniou, 2021. "Intelligent Agents in Co-Evolving Knowledge Networks," Mathematics, MDPI, vol. 9(1), pages 1-17, January.
    4. Liao, Shi-Gen & Yi, Shu-Ping, 2021. "Modeling and analyzing knowledge transmission process considering free-riding behavior of knowledge acquisition: A waterborne disease approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 569(C).
    5. Liao, Shi-Gen & Yi, Shu-Ping, 2021. "Modeling and analysis knowledge transmission process in complex networks by considering internalization mechanism," Chaos, Solitons & Fractals, Elsevier, vol. 143(C).
    6. Wang, Haiying & Wang, Jun & Ding, Liting & Wei, Wei, 2017. "Knowledge transmission model with consideration of self-learning mechanism in complex networks," Applied Mathematics and Computation, Elsevier, vol. 304(C), pages 83-92.

    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:16:p:7018-:d:1457246. 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.