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Incorporating Consumer Price Expectations in Diffusion Models

Author

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  • Chakravarthi Narasimhan

    (Washington University)

Abstract

In this paper the sensitivity of the optimal price path of a new durable product to the price expectations of consumers is examined. Consumers enter the market every period in a diffusion type framework. During the initial periods more consumers enter the market due to word of mouth influence, but in the latter periods saturation effects set in. The entering set of cohorts form expectations about future prices; and in a stable equilibrium, these expectations are fulfilled. It is shown that the price path is cyclical with the following properties: at the beginning of the cycle, the price is at its highest level; it falls monotonically over time reaching a low price at the end of the cycle (equal to the reservation price of the consumers willing to pay less); the cycle lengths are not equal. The sensitivity of the optimal price path to model parameters is explored through a numerical procedure.

Suggested Citation

  • Chakravarthi Narasimhan, 1989. "Incorporating Consumer Price Expectations in Diffusion Models," Marketing Science, INFORMS, vol. 8(4), pages 343-357.
    • Handle: RePEc:inm:ormksc:v:8:y:1989:i:4:p:343-357
    DOI: 10.1287/mksc.8.4.343
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    Cited by:

    1. Baojun Jiang & K. Sudhir & Tianxin Zou, 2021. "Effects of Cost‐Information Transparency on Intertemporal Price Discrimination," Production and Operations Management, Production and Operations Management Society, vol. 30(2), pages 390-401, February.
    2. Amit Mehra & Gireesh Shrimali, 2008. "Introduction of Software Products and Services Through "Public" Beta Launches," Working Papers 08-11, NET Institute.
    3. Adil Baykasoğlu & İlker Gölcük & Derya Eren Akyol, 2017. "A fuzzy multiple-attribute decision making model to evaluate new product pricing strategies," Annals of Operations Research, Springer, vol. 251(1), pages 205-242, April.
    4. Juanjuan Zhang, 2010. "The Sound of Silence: Observational Learning in the U.S. Kidney Market," Marketing Science, INFORMS, vol. 29(2), pages 315-335, 03-04.
    5. Inseong Song & Pradeep Chintagunta, 2003. "A Micromodel of New Product Adoption with Heterogeneous and Forward-Looking Consumers: Application to the Digital Camera Category," Quantitative Marketing and Economics (QME), Springer, vol. 1(4), pages 371-407, December.
    6. Gonul, Fusun F., 1998. "Estimating price expectations in the OTC medicine market: An application of dynamic stochastic discrete choice models to scanner panel data," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 41-56, November.
    7. Peters, Kay & Albers, Sönke & Kumar, V., 2008. "Is there more to international Diffusion than Culture? An investigation on the Role of Marketing and Industry Variables," EconStor Preprints 27678, ZBW - Leibniz Information Centre for Economics.
    8. Federico Gaudenzi, 2020. "Bias in purchase decisions: correlation between expectations and procrastination in high and low involvement products," Working Papers hal-02560384, HAL.
    9. Zhang, Jie & Chiang, Wei-yu Kevin, 2020. "Durable goods pricing with reference price effects," Omega, Elsevier, vol. 91(C).
    10. Kalyanaram, Gurumurthy & Winer, Russell S., 2022. "Behavioral response to price: Data-based insights and future research for retailing," Journal of Retailing, Elsevier, vol. 98(1), pages 46-70.
    11. Subramanian Balachander & Kannan Srinivasan, 1998. "Modifying Customer Expectations of Price Decreases for a Durable Product," Management Science, INFORMS, vol. 44(6), pages 776-786, June.

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