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Maximizing the Influence of Innovative Green Product Propagation

Author

Listed:
  • Liang’an Huo

    (Business School, University of Shanghai for Science and Technology, Shanghai 200093, China)

  • Qianqian Wang

    (Business School, University of Shanghai for Science and Technology, Shanghai 200093, China)

  • Tingting Lin

    (School of Electrical and Information Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Hongguang He

    (Jiyang College of Zhejiang, A&F University, Shaoxing 311800, China)

Abstract

This article considers the competition between the propagation of traditional product information and innovative green product information, and it proposes a hybrid model with advertisement and promotion strategies. On this basis, an innovation green product information propagation model is developed through the optimization of the advertisement strategies of the adopters of innovative green product information and the promotion strategies of the adopters of traditional product information, according to Pontryagin’s maximum principle to seek the optimal strategies for maximizing the influence of innovative green product information, and using numerical calculations to simulate the propagation state of product information. The results show that advertisement strategies play a decisive role in the propagation of innovative green product information in the market. If the promotion strategies are also considered, the propagation effect of innovative green product information will be more effective.

Suggested Citation

  • Liang’an Huo & Qianqian Wang & Tingting Lin & Hongguang He, 2021. "Maximizing the Influence of Innovative Green Product Propagation," Sustainability, MDPI, vol. 13(8), pages 1-17, April.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:8:p:4110-:d:531569
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    References listed on IDEAS

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    1. Peres, Renana & Muller, Eitan & Mahajan, Vijay, 2010. "Innovation diffusion and new product growth models: A critical review and research directions," International Journal of Research in Marketing, Elsevier, vol. 27(2), pages 91-106.
    2. Christopher J. Easingwood & Vijay Mahajan & Eitan Muller, 1983. "A Nonuniform Influence Innovation Diffusion Model of New Product Acceptance," Marketing Science, INFORMS, vol. 2(3), pages 273-295.
    3. Frank M. Bass, 2004. "Comments on "A New Product Growth for Model Consumer Durables The Bass Model"," Management Science, INFORMS, vol. 50(12_supple), pages 1833-1840, December.
    4. Seierstad, Atle & Sydsaeter, Knut, 1977. "Sufficient Conditions in Optimal Control Theory," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 367-391, June.
    5. Rosa Dangelico & Devashish Pujari, 2010. "Mainstreaming Green Product Innovation: Why and How Companies Integrate Environmental Sustainability," Journal of Business Ethics, Springer, vol. 95(3), pages 471-486, September.
    6. Tashiro, Tohru, 2016. "Hierarchical Bass model: a product diffusion model considering a diversity of sensitivity to fashion," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 824-832.
    7. Rosa Maria Dangelico & Devashish Pujari & Pierpaolo Pontrandolfo, 2017. "Green Product Innovation in Manufacturing Firms: A Sustainability‐Oriented Dynamic Capability Perspective," Business Strategy and the Environment, Wiley Blackwell, vol. 26(4), pages 490-506, May.
    8. Li, Guangpei & Wang, Xiaoyu & Su, Shibin & Su, Yuan, 2019. "How green technological innovation ability influences enterprise competitiveness," Technology in Society, Elsevier, vol. 59(C).
    9. Shlomo Kalish, 1985. "A New Product Adoption Model with Price, Advertising, and Uncertainty," Management Science, INFORMS, vol. 31(12), pages 1569-1585, December.
    10. Bruce Robinson & Chet Lakhani, 1975. "Dynamic Price Models for New-Product Planning," Management Science, INFORMS, vol. 21(10), pages 1113-1122, June.
    11. Gao, Jingzhe & Xiao, Zhongdong & Cao, Binbin & Chai, Qiangfei, 2018. "Green supply chain planning considering consumer’s transportation process," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 109(C), pages 311-330.
    12. Frank M. Bass, 2004. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 50(12_supple), pages 1825-1832, December.
    13. Dong, Ciwei & Liu, Qingyu & Shen, Bin, 2019. "To be or not to be green? Strategic investment for green product development in a supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 131(C), pages 193-227.
    14. Dai, Rui & Zhang, Jianxiong, 2017. "Green process innovation and differentiated pricing strategies with environmental concerns of South-North markets," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 98(C), pages 132-150.
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