IDEAS home Printed from
   My bibliography  Save this article

Pricing and Delivery-Time Performance in a Competitive Environment


  • Lode Li

    (Yale School of Management, Box 208200, New Haven, Connecticut 06520-8200)

  • Yew Sing Lee

    (Department of Information and Decision Sciences, The University of Illinois at Chicago, Chicago, Illinois 60607)


We present a model of market competition in which customer preferences are over not only price and quality but also delivery speed. This allows a study of market demand and firms' decisions on price, quality, technology and responsiveness in a competitive environment. When demand arises, a customer chooses the firm that maximizes its expected utility of price, quality and response time. The demand function for each firm is derived by analyzing a queueing system with competing servers. We then study price competition among firms with differentiated processing rates. In the equilibrium, the firm with a higher processing rate always enjoys a price premium, and, further, enjoys a larger market share when its opponent also has adequate processing rate to serve all the customers alone.

Suggested Citation

  • Lode Li & Yew Sing Lee, 1994. "Pricing and Delivery-Time Performance in a Competitive Environment," Management Science, INFORMS, vol. 40(5), pages 633-646, May.
  • Handle: RePEc:inm:ormnsc:v:40:y:1994:i:5:p:633-646

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. JS Armstrong & Fred Collopy, 2004. "Causal Forces: Structuring Knowledge for Time-series Extrapolation," General Economics and Teaching 0412003, EconWPA.
    2. Fildes, Robert & Lusk, Edward J, 1984. "The choice of a forecasting model," Omega, Elsevier, vol. 12(5), pages 427-435.
    3. Scott Armstrong, J., 1988. "Research needs in forecasting," International Journal of Forecasting, Elsevier, vol. 4(3), pages 449-465.
    4. Robert Carbone & JS Armstrong, 2004. "Evaluation of Extrapolative Forecasting Methods: Results of a Survey of Academicians and Practitioners," General Economics and Teaching 0412008, EconWPA.
    5. Robert Carbone & Spyros Makridakis, 1986. "Forecasting When Pattern Changes Occur Beyond the Historical Data," Management Science, INFORMS, vol. 32(3), pages 257-271, March.
    6. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    7. Sanders, NR & Ritzman, LP, 1990. "Improving short-term forecasts," Omega, Elsevier, vol. 18(4), pages 365-373.
    Full references (including those not matched with items on IDEAS)


    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:inm:ormnsc:v:40:y:1994:i:5:p:633-646. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc). General contact details of provider: .

    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.

    We have no references for this item. You can help adding them by using 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.