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Dynamic Evaluation of Product Innovation Knowledge Concerning the Interactive Relationship between Innovative Subjects: A Multi-Objective Optimization Approach

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  • Fanshun Zhang

    (School of Business, Xiangtan University, Xiangtan 411105, China
    These authors contributed to the work equally and should be regarded as co-first authors.)

  • Zhuorui Zhang

    (School of Business, Xiangtan University, Xiangtan 411105, China
    These authors contributed to the work equally and should be regarded as co-first authors.)

  • Quanquan Zhang

    (School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China)

  • Xiaochun Zhu

    (Management Committee of Panyu Campus, Jinan University, Guangzhou 510632, China)

Abstract

Product innovation knowledge, in prior studies, has been subjectively evaluated by a single stakeholder, resulting in a notable bias toward the chosen solution. Specifically, the selected product innovation solution may fail to incorporate the interests and demands of innovation subjects, potentially leading to conflicting innovation solutions and inefficiencies. Recently, many external parties, such as consumers and supply chain partners, have been involved in innovative work to create a substantial amount of the product interactive innovation knowledge (PIIK). The value of PIIK is hard to evaluate since this knowledge has evolved as a dynamic relationship among external parties. Thus, a novel method that integrates dynamic knowledge evolution and multiple stakeholders should be developed to dynamically evaluate the value of PIIK. Specially, the objectives in this paper are the knowledge evaluation scores of different innovative aspects and the ability of a model to identify the optimal solutions that receive the highest score from the innovative subjects. Then, the dynamic characteristic is captured by the participation of new parties, the departure of original parties, and the new knowledge created by the existing parties. To verify the effectiveness of feasibility of this model, case studies based on the innovation of a cell phone were implemented. The results show the following: (i). When the interactive relationship is not considered, parties prefer to choose the solution that fits well with their benefits, but the solution may conflict with other solutions chosen by their partners; (ii). Although the best solution is not separately selected by all parties when the interactive relationship is considered, the solution combined with the satisfactory result presents a better performance on product innovation; (iii). Dynamic characteristic should be considered in evaluation process, especially when the core parties are changed.

Suggested Citation

  • Fanshun Zhang & Zhuorui Zhang & Quanquan Zhang & Xiaochun Zhu, 2023. "Dynamic Evaluation of Product Innovation Knowledge Concerning the Interactive Relationship between Innovative Subjects: A Multi-Objective Optimization Approach," Mathematics, MDPI, vol. 11(9), pages 1-33, April.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:2105-:d:1135833
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    References listed on IDEAS

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    1. Xie, Xuemei & Wang, Hongwei, 2020. "How can open innovation ecosystem modes push product innovation forward? An fsQCA analysis," Journal of Business Research, Elsevier, vol. 108(C), pages 29-41.
    2. Yu, Anyu & Shi, Yu & You, Jianxin & Zhu, Joe, 2021. "Innovation performance evaluation for high-tech companies using a dynamic network data envelopment analysis approach," European Journal of Operational Research, Elsevier, vol. 292(1), pages 199-212.
    3. Chang Liu & Yongfu Feng & Dongtao Lin & Liang Wu & Min Guo, 2020. "Iot based laundry services: an application of big data analytics, intelligent logistics management, and machine learning techniques," International Journal of Production Research, Taylor & Francis Journals, vol. 58(17), pages 5113-5131, September.
    4. Rui Liu & Hui Zhang & Wen-Tsao Pan, 2022. "Artificial-Intelligence-Based Fuzzy Comprehensive Evaluation of Innovative Knowledge Management in Universities," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, April.
    5. Sepehr Ghazinoory & Maghsoud Amiri & Soroush Ghazinoori & Parisa Alizadeh, 2019. "Designing innovation policy mix: a multi-objective decision-making approach," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 28(4), pages 365-385, May.
    6. Mohammed Abdellaoui & Olivier L’Haridon & Horst Zank, 2010. "Separating curvature and elevation: A parametric probability weighting function," Journal of Risk and Uncertainty, Springer, vol. 41(1), pages 39-65, August.
    7. Gerarda Fattoruso & Maria Barbati & Alessio Ishizaka & Massimo Squillante, 2023. "A hybrid AHPSort II and multi-objective portfolio selection method to support quality control in the automotive industry," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 74(1), pages 209-224, January.
    8. Freije, Inmaculada & de la Calle, Alberto & Ugarte, José V., 2022. "Role of supply chain integration in the product innovation capability of servitized manufacturing companies," Technovation, Elsevier, vol. 118(C).
    9. Gangfeng Wang & Xitian Tian & Yongbiao Hu & Richard David Evans & Mingrui Tian & Rong Wang, 2017. "Manufacturing Process Innovation-Oriented Knowledge Evaluation Using MCDM and Fuzzy Linguistic Computing in an Open Innovation Environment," Sustainability, MDPI, vol. 9(9), pages 1-19, September.
    10. Erdogan, Nuh & Pamucar, Dragan & Kucuksari, Sadik & Deveci, Muhammet, 2021. "An integrated multi-objective optimization and multi-criteria decision-making model for optimal planning of workplace charging stations," Applied Energy, Elsevier, vol. 304(C).
    11. Loureiro, Sandra Maria Correia & Romero, Jaime & Bilro, Ricardo Godinho, 2020. "Stakeholder engagement in co-creation processes for innovation: A systematic literature review and case study," Journal of Business Research, Elsevier, vol. 119(C), pages 388-409.
    12. Dziallas, Marisa & Blind, Knut, 2019. "Innovation indicators throughout the innovation process: An extensive literature analysis," Technovation, Elsevier, vol. 80, pages 3-29.
    13. Wu, Qun & Liu, Xinwang & Qin, Jindong & Zhou, Ligang & Mardani, Abbas & Deveci, Muhammet, 2022. "An integrated multi-criteria decision-making and multi-objective optimization model for socially responsible portfolio selection," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    14. Humphrey, Steven J. & Verschoor, Arjan, 2004. "The probability weighting function: experimental evidence from Uganda, India and Ethiopia," Economics Letters, Elsevier, vol. 84(3), pages 419-425, September.
    15. Siaw, Christopher Agyapong & Sarpong, David, 2021. "Dynamic exchange capabilities for value co-creation in ecosystems," Journal of Business Research, Elsevier, vol. 134(C), pages 493-506.
    16. Bisma Mannan & Abid Haleem, 2017. "Understanding major dimensions and determinants that help in diffusion & adoption of product innovation: using AHP approach," Journal of Global Entrepreneurship Research, Springer;UNESCO Chair in Entrepreneurship, vol. 7(1), pages 1-24, December.
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