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Dynamic pricing and service customization strategy for IoT-based smart products

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Listed:
  • Xin, Baogui
  • Song, Yaping
  • Xie, Lei

Abstract

Digital technology has changed consumer behavior and expectations motivating firms to innovate customization strategies to meet the increasingly personalized needs of consumers. To explain this change, we first constructed a two-period game between a smart product manufacturer and a service customization platform. Second, we obtained optimal service customization strategies based on the impact of Internet of Things (IoT) technology. Finally, we implemented a case study of the smart product manufacturer ORVIBO and the Oppein MALL platform for whole-house customization to verify these optimal strategies. The results indicate that (i) firms should view the IoT's value in the long term, as their profits do not always increase as the IoT functionality level improves; (ii) the manufacturer's optimal strategy is to adopt service customization in both periods; (iii) service customization strategies can achieve Pareto optimality for the manufacturer and platform, resulting in a win-win state for consumers.

Suggested Citation

  • Xin, Baogui & Song, Yaping & Xie, Lei, 2024. "Dynamic pricing and service customization strategy for IoT-based smart products," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    • Handle: RePEc:eee:tefoso:v:199:y:2024:i:c:s004016252300731x
    DOI: 10.1016/j.techfore.2023.123046
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    1. Yiangos Papanastasiou & Nicos Savva, 2017. "Dynamic Pricing in the Presence of Social Learning and Strategic Consumers," Management Science, INFORMS, vol. 63(4), pages 919-939, April.
    2. Llopis-Albert, Carlos & Rubio, Francisco & Valero, Francisco, 2021. "Impact of digital transformation on the automotive industry," Technological Forecasting and Social Change, Elsevier, vol. 162(C).
    3. Yan Zhang & Yanyan He & Jinfeng Yue & Qinglong Gou, 2019. "Pricing decisions for a supply chain with refurbished products," International Journal of Production Research, Taylor & Francis Journals, vol. 57(9), pages 2867-2900, May.
    4. Can Sun & Yonghua Ji, 2022. "For Better or For Worse: Impacts of IoT Technology in e‐Commerce Channel," Production and Operations Management, Production and Operations Management Society, vol. 31(3), pages 1353-1371, March.
    5. Ding, Yi & Gao, Xing & Huang, Chao & Shu, Jia & Yang, Donghui, 2018. "Service competition in an online duopoly market," Omega, Elsevier, vol. 77(C), pages 58-72.
    6. Xu Guan & Yulan Wang & Zelong Yi & Ying‐Ju Chen, 2020. "Inducing Consumer Online Reviews Via Disclosure," Production and Operations Management, Production and Operations Management Society, vol. 29(8), pages 1956-1971, August.
    7. Wang, Xuerong & Leng, Mingming & Song, Jingpu & Luo, Chunlin & Hui, Sunyuen, 2019. "Managing a supply chain under the impact of customer reviews: A two-period game analysis," European Journal of Operational Research, Elsevier, vol. 277(2), pages 454-468.
    8. Wang, Yonggui & Tian, Qinghong & Li, Xia & Xiao, Xiaohong, 2022. "Different roles, different strokes: How to leverage two types of digital platform capabilities to fuel service innovation," Journal of Business Research, Elsevier, vol. 144(C), pages 1121-1128.
    9. Wang, Jiayun & Shum, Stephen & Feng, Gengzhong, 2022. "Supplier’s pricing strategy in the presence of consumer reviews," European Journal of Operational Research, Elsevier, vol. 296(2), pages 570-586.
    10. Wang, Jian & He, Shulin, 2022. "Optimal decisions of modularity, prices and return policy in a dual-channel supply chain under mass customization," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 160(C).
    11. Jost, Peter-J. & Süsser, Theresa, 2020. "Company-customer interaction in mass customization," International Journal of Production Economics, Elsevier, vol. 220(C).
    12. Zhao, Ju & Qiu, Ju & Zhou, Yong-Wu & Hu, Xiao-Jian & Yang, Ai-Feng, 2020. "Quality disclosure in the presence of strategic consumers," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    13. Andrei Hagiu & Julian Wright, 2015. "Marketplace or Reseller?," Management Science, INFORMS, vol. 61(1), pages 184-203, January.
    14. Fernandez-Lores, Susana & Crespo-Tejero, Natividad & Fernández-Hernández, Ruth, 2022. "Driving traffic to the museum: The role of the digital communication tools," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    15. Lou, Yaqi & Feng, Lipan & He, Shuguang & He, Zhen & Zhao, Xiukun, 2020. "Logistics service outsourcing choices in a retailer-led supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 141(C).
    16. Dmitri Kuksov & Ying Xie, 2010. "Pricing, Frills, and Customer Ratings," Marketing Science, INFORMS, vol. 29(5), pages 925-943, 09-10.
    17. Li, Wei & Chen, Jing, 2018. "Backward integration strategy in a retailer Stackelberg supply chain," Omega, Elsevier, vol. 75(C), pages 118-130.
    18. Man Yu & Laurens Debo & Roman Kapuscinski, 2016. "Strategic Waiting for Consumer-Generated Quality Information: Dynamic Pricing of New Experience Goods," Management Science, INFORMS, vol. 62(2), pages 410-435, February.
    19. Xin Yan & Xiaohua Han, 2022. "Optimal pricing and remanufacturing entry strategies of manufacturers in the presence of online reviews," Annals of Operations Research, Springer, vol. 316(1), pages 59-92, September.
    20. Monic Sun, 2012. "How Does the Variance of Product Ratings Matter?," Management Science, INFORMS, vol. 58(4), pages 696-707, April.
    21. Zhang, Shichen & Wei, Liqun & Zhang, Jianxiong, 2022. "Demand forecast sharing for a dominant retailer with supplier encroachment and quality decisions," European Journal of Operational Research, Elsevier, vol. 301(1), pages 39-50.
    22. Tsan-Ming Choi & T. C. E. Cheng & Xiande Zhao & Xiang Li & Yongjian Li & Xiaoqiang Cai & Jun Shan, 2016. "Service Channel Choice for Supply Chain: Who is Better Off by Undertaking the Service?," Production and Operations Management, Production and Operations Management Society, vol. 25(3), pages 516-534, March.
    23. Ruchi Chauhan & Varun Kumar & Tapas Kumar Jana & Arunava Majumder, 2021. "A Modified Customization Strategy in a Dual-Channel Supply Chain Model with Price-Sensitive Stochastic Demand and Distribution-Free Approach," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-25, September.
    24. Bin Dan & Shuguang Zhang & Maosen Zhou, 2018. "Strategies for warranty service in a dual-channel supply chain with value-added service competition," International Journal of Production Research, Taylor & Francis Journals, vol. 56(17), pages 5677-5699, September.
    25. Sander F. M. Beckers & Jenny Doorn & Peter C. Verhoef, 2018. "Good, better, engaged? The effect of company-initiated customer engagement behavior on shareholder value," Journal of the Academy of Marketing Science, Springer, vol. 46(3), pages 366-383, May.
    26. Youngsoo Kim & Ramayya Krishnan, 2015. "On Product-Level Uncertainty and Online Purchase Behavior: An Empirical Analysis," Management Science, INFORMS, vol. 61(10), pages 2449-2467, October.
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