IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v12y2024i24p3903-d1541515.html
   My bibliography  Save this article

Innovation Prioritization Decisions in the Product–Service Supply Chain: The Impact of Data Mining and Information Sharing Strategies

Author

Listed:
  • Jinfa Shi

    (School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

  • Wei Liu

    (School of Management and Economics, North China University of Water Resources and Electric Power, Zhengzhou 450046, China)

  • Yongqiang Su

    (School of Management Engineering, Henan University of Engineering, Zhengzhou 451191, China)

Abstract

Driven by the servicing and digital transformation of manufacturing enterprises, product and service innovation for manufacturers and service providers to promote integrated solutions collaboratively has become an important way for enterprises to maintain market competitiveness. Building on this foundation, this paper develops an innovation priority decision model for the product–service supply chain, which comprises manufacturers and service providers, considering the data mining and information sharing strategies of service providers. It analyzes the optimal decisions and profits of the members when product innovation is prioritized as well as when service innovation is prioritized, and subsequently explores the selection of innovation strategies for the product–service supply chain under varying conditions. The results of the study show that, firstly, service providers’ data mining and information sharing strategies are not always favorable to the innovation decisions of both parties. Only when data resources can be transformed into real innovation value at a reasonable cost can data mining and information sharing play the role of ‘external incentives’ to promote collaborative innovation between the two parties. Secondly, when service providers do not adopt data mining and information sharing strategies, the efficiency of product and service innovation plays a decisive role in innovation prioritization. The party with high innovation efficiency adopts the sub-priority innovation strategy, which can lead to a larger market share for the innovation results. Finally, under the service provider’s data mining and information sharing strategy, the innovation priority selection of the product–service supply chain depends on the information value transformation ability of the manufacturer and the service provider. Moreover, the profits of manufacturers and service providers under the same innovation priority do not always ‘advance or retreat together’, and there may be cases where one of them suffers a loss of profits. This study provides a theoretical basis for the choice of innovation strategies given to manufacturers and service providers, and promotes the development of collaborative innovation between them.

Suggested Citation

  • Jinfa Shi & Wei Liu & Yongqiang Su, 2024. "Innovation Prioritization Decisions in the Product–Service Supply Chain: The Impact of Data Mining and Information Sharing Strategies," Mathematics, MDPI, vol. 12(24), pages 1-30, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:24:p:3903-:d:1541515
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/12/24/3903/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/12/24/3903/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Gang & Gunasekaran, Angappa & Ngai, Eric W.T. & Papadopoulos, Thanos, 2016. "Big data analytics in logistics and supply chain management: Certain investigations for research and applications," International Journal of Production Economics, Elsevier, vol. 176(C), pages 98-110.
    2. Qiang Wang & Zhiqiang Wang & Xiande Zhao, 2015. "Strategic orientations and mass customisation capability: the moderating effect of product life cycle," International Journal of Production Research, Taylor & Francis Journals, vol. 53(17), pages 5278-5295, September.
    3. Di Wang & Weihua Liu & Yanjie Liang & Shuang Wei, 2023. "Decision optimization in service supply chain: the impact of demand and supply-driven data value and altruistic behavior," Annals of Operations Research, Springer, vol. 324(1), pages 971-992, May.
    4. Jiali Qu & Benyong Hu & Chao Meng, 2021. "Joint Innovation Investment and Pricing Decisions in Retail Supply Chains with Customer Value," Sustainability, MDPI, vol. 13(3), pages 1-14, January.
    5. Wolfgang Ulaga & Werner Reinartz, 2011. "Hybrid Offerings: How Manufacturing Firms Combine Goods and Services Successfully," Post-Print hal-00642039, HAL.
    6. Liu, Weihua & Wang, Di & Shen, Xinran & Yan, Xiaoyu & Wei, Wanying, 2018. "The impacts of distributional and peer-induced fairness concerns on the decision-making of order allocation in logistics service supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 116(C), pages 102-122.
    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. Di Wang & Weihua Liu & Yanjie Liang & Shuang Wei, 2023. "Decision optimization in service supply chain: the impact of demand and supply-driven data value and altruistic behavior," Annals of Operations Research, Springer, vol. 324(1), pages 971-992, May.
    2. Zhang, Mengdi & Yang, Wanting & Zhao, Zhiheng & Wang, Shuaian & Huang, George Q., 2024. "Do fairness concerns matter for ESG decision-making? Strategic interactions in digital twin-enabled sustainable semiconductor supply chain," International Journal of Production Economics, Elsevier, vol. 276(C).
    3. Anna Adamik & Michał Nowicki, 2019. "Pathologies and Paradoxes of Co-Creation: A Contribution to the Discussion about Corporate Social Responsibility in Building a Competitive Advantage in the Age of Industry 4.0," Sustainability, MDPI, vol. 11(18), pages 1-38, September.
    4. Dominik M. Wielgos & Christian Homburg & Christina Kuehnl, 2021. "Digital business capability: its impact on firm and customer performance," Journal of the Academy of Marketing Science, Springer, vol. 49(4), pages 762-789, July.
    5. Leonardo de Assis Santos & Leonardo Marques, 2022. "Big data analytics for supply chain risk management: research opportunities at process crossroads," Post-Print hal-03766121, HAL.
    6. Siri Jagstedt & Magnus Persson, 2019. "Using Platform Strategies In The Development Of Integrated Product-Service Solutions," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 23(04), pages 1-36, May.
    7. Rönnberg Sjödin, David & Parida, Vinit & Kohtamäki, Marko, 2016. "Capability configurations for advanced service offerings in manufacturing firms: Using fuzzy set qualitative comparative analysis," Journal of Business Research, Elsevier, vol. 69(11), pages 5330-5335.
    8. Wang, Kai & Ding, Peiqi & Zhao, Ruiqing, 2021. "Strategic credit sales to express retail under asymmetric default risk and stochastic market demand," Omega, Elsevier, vol. 101(C).
    9. Ashkan Faramarzi & Stefan Worm & Wolfgang Ulaga, 2024. "Service strategy’s effect on firm performance: A meta-analysis of the servitization literature," Journal of the Academy of Marketing Science, Springer, vol. 52(4), pages 1018-1044, July.
    10. Fu, Shuke & Ge, Yingchen & Hao, Yu & Peng, Jiachao & Tian, Jiali, 2024. "Energy supply chain efficiency in the digital era: Evidence from China's listed companies," Energy Economics, Elsevier, vol. 134(C).
    11. Vendrell-Herrero, Ferran & Bustinza, Oscar F. & Opazo-Basaez, Marco, 2021. "Information technologies and product-service innovation: The moderating role of service R&D team structure," Journal of Business Research, Elsevier, vol. 128(C), pages 673-687.
    12. Anhang Chen & Huiqin Zhang & Yuxiang Zhang & Junwei Zhao, 2024. "Manufacturers’ digital transformation under carbon cap-and-trade policy: investment strategy and environmental impact," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-14, December.
    13. Videsh Desingh & Baskaran R, 2022. "Internet of Things adoption barriers in the Indian healthcare supply chain: An ISM‐fuzzy MICMAC approach," International Journal of Health Planning and Management, Wiley Blackwell, vol. 37(1), pages 318-351, January.
    14. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    15. Ancillai, Chiara & Sabatini, Andrea & Gatti, Marco & Perna, Andrea, 2023. "Digital technology and business model innovation: A systematic literature review and future research agenda," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    16. Xianliang Shi & Genzhu Li & Ciwei Dong & Yefei Yang, 2020. "Value Co-Creation Behavior in Green Supply Chains: An Empirical Study," Energies, MDPI, vol. 13(15), pages 1-26, July.
    17. Piepponen, Amanda & Ritala, Paavo & Keränen, Joona & Maijanen, Päivi, 2022. "Digital transformation of the value proposition: A single case study in the media industry," Journal of Business Research, Elsevier, vol. 150(C), pages 311-325.
    18. Guangmei Cao & Yuesen Wang & Honghu Gao & Hao Liu & Haibin Liu & Zhigang Song & Yuqing Fan, 2023. "Coordination Decision-Making for Intelligent Transformation of Logistics Services under Capital Constraint," Sustainability, MDPI, vol. 15(6), pages 1-25, March.
    19. Sandrin, Enrico & Trentin, Alessio & Forza, Cipriano, 2018. "Leveraging high-involvement practices to develop mass customization capability: A contingent configurational perspective," International Journal of Production Economics, Elsevier, vol. 196(C), pages 335-345.
    20. Mohammadreza Akbari & John L. Hopkins, 2022. "Digital technologies as enablers of supply chain sustainability in an emerging economy," Operations Management Research, Springer, vol. 15(3), pages 689-710, December.

    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:jmathe:v:12:y:2024:i:24:p:3903-:d:1541515. 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.