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Dynamic modeling of web purchase behavior and e-mailing impact by Petri net

Author

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  • BALAGUE, Christine
  • LEE, Janghyuk

Abstract

In this article, the authors introduce Petri nets to model the dynamics of web site visits and purchase behaviors in the case of wish list systems. They describe web site activities and their transition with probability distributions and model the sequential impact of influential factors through links that better explain web purchase behavior dynamics. The basic model, which analyzes site connections and purchases to explain visit and purchase behavior, performs better than a classical negative binomial regression model. To demonstrate its flexibility, the authors extend the wish list Petri net model to measure the impact of e-mailing intervals on visit frequency and purchase.

Suggested Citation

  • BALAGUE, Christine & LEE, Janghyuk, 2004. "Dynamic modeling of web purchase behavior and e-mailing impact by Petri net," HEC Research Papers Series 804, HEC Paris.
    • Handle: RePEc:ebg:heccah:0804
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    References listed on IDEAS

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    1. Eric J. Johnson & Wendy W. Moe & Peter S. Fader & Steven Bellman & Gerald L. Lohse, 2004. "On the Depth and Dynamics of Online Search Behavior," Management Science, INFORMS, vol. 50(3), pages 299-308, March.
    2. Mandel, Naomi & Johnson, Eric J, 2002. "When Web Pages Influence Choice: Effects of Visual Primes on Experts and Novices," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 29(2), pages 235-245, September.
    3. Zauberman, Gal, 2003. "The Intertemporal Dynamics of Consumer Lock-In," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 30(3), pages 405-419, December.
    4. N Viswanadham & N R Srinivasa Raghavan, 2000. "Performance analysis and design of supply chains: a Petri net approach," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(10), pages 1158-1169, October.
    5. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    6. John G. Lynch , Jr. & Dan Ariely, 2000. "Wine Online: Search Costs Affect Competition on Price, Quality, and Distribution," Marketing Science, INFORMS, vol. 19(1), pages 83-103, April.
    7. Young-Hoon Park & Peter S. Fader, 2004. "Modeling Browsing Behavior at Multiple Websites," Marketing Science, INFORMS, vol. 23(3), pages 280-303, May.
    8. Gerald Häubl & Valerie Trifts, 2000. "Consumer Decision Making in Online Shopping Environments: The Effects of Interactive Decision Aids," Marketing Science, INFORMS, vol. 19(1), pages 4-21, May.
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    More about this item

    Keywords

    internet; wish list; e-mail; Petri net; dynamic model;
    All these keywords.

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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