IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v42y1996i1p65-84.html
   My bibliography  Save this article

A Theory of Cutoff Formation Under Imperfect Information

Author

Listed:
  • Fred M. Feinberg

    (Division of Management and Economics, Scarborough College, University of Toronto, Toronto, Ontario, Canada M1C 1A4)

  • Joel Huber

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708-0120)

Abstract

Numerous models in the Management Science literature contain constructions that are a variant of the following: A decision-maker must choose from a set of alternatives based on imperfect information as to their relative quality, while further evaluation, through costly, provides more accurate information. We examine decision heuristics in which the optimal search policy entails a screening strategy limiting the number of alternatives in the subsequent, costly evaluation. There are two general methods for accomplishing this screening: Quota cutoffs operate by selecting the optimal number of alternatives to evaluate; Level cutoffs operate by specifying a minimally-acceptable level of the imperfect screening indicator. The present paper has three main objectives. First, to define the Level and Quota cutoff methods, broadly characterize optimal behavior for each and determine what aspects of the decision environment of order statistics as a methodology for exploring decision problems when information is imperfectly known; and third, to discuss the pivotal role of default, or fallback, options in a broad class of search problems. Quota and Level strategies restrict the number of alternatives passing the cutoff-based screen. Because restrictive cutoffs reduce evaluation costs while lowering the expected quality of the item finally selected, changes in the decision environment making the evaluation process less beneficial or increasing its cost drive the optimal cutoff to be more restrictive. In particular, increases in unit evaluation cost, improvement in the quality of a fallback option, decreases in the total number of alternatives available or improvement in the precision of the final evaluation process all lead to more restrictive cutoffs at optimum. These results hold over a remarkably broad range of assumptions and conditions. We also find that a better screening indicator leads to more restrictive screening when evaluation costs are low but, surprisingly, to less restrictive screening when costs are high. Comparing the two strategies, we find the unexpected result that the Quota cutoff strategy is generally superior to the Level, except under on of two fairly uncommon set of circumstances: when evaluation cost is prohibitively high, or when there is a fallback option of very high quality.

Suggested Citation

  • Fred M. Feinberg & Joel Huber, 1996. "A Theory of Cutoff Formation Under Imperfect Information," Management Science, INFORMS, vol. 42(1), pages 65-84, January.
  • Handle: RePEc:inm:ormnsc:v:42:y:1996:i:1:p:65-84
    DOI: 10.1287/mnsc.42.1.65
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.42.1.65
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.42.1.65?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kartik Hosanagar, 2011. "Usercentric Operational Decision Making in Distributed Information Retrieval," Information Systems Research, INFORMS, vol. 22(4), pages 739-755, December.
    2. Zhixi Wan & Damian R. Beil, 2009. "RFQ Auctions with Supplier Qualification Screening," Operations Research, INFORMS, vol. 57(4), pages 934-949, August.
    3. T. Tony Ke & Zuo-Jun Max Shen & J. Miguel Villas-Boas, 2016. "Search for Information on Multiple Products," Management Science, INFORMS, vol. 62(12), pages 3576-3603, December.
    4. Nitin Mehta & Surendra Rajiv & Kannan Srinivasan, 2003. "Price Uncertainty and Consumer Search: A Structural Model of Consideration Set Formation," Marketing Science, INFORMS, vol. 22(1), pages 58-84, June.
    5. Alan L. Montgomery & Kartik Hosanagar & Ramayya Krishnan & Karen B. Clay, 2004. "Designing a Better Shopbot," Management Science, INFORMS, vol. 50(2), pages 189-206, February.
    6. Joffre Swait & Fred Feinberg, 2014. "Deciding how to decide: an agenda for multi-stage choice modelling research in marketing," Chapters, in: Stephane Hess & Andrew Daly (ed.), Handbook of Choice Modelling, chapter 26, pages 649-660, Edward Elgar Publishing.
    7. Parag Pendharkar & Marvin Troutt, 2014. "Interactive classification using data envelopment analysis," Annals of Operations Research, Springer, vol. 214(1), pages 125-141, March.
    8. Torres, Miguel Matos & Clegg, L. Jeremy & Varum, Celeste Amorim, 2016. "The missing link between awareness and use in the uptake of pro-internationalization incentives," International Business Review, Elsevier, vol. 25(2), pages 495-510.
    9. Ming Ding & Jehoshua Eliashberg, 2002. "Structuring the New Product Development Pipeline," Management Science, INFORMS, vol. 48(3), pages 343-363, March.
    10. Ruxian Wang & Ozge Sahin, 2018. "The Impact of Consumer Search Cost on Assortment Planning and Pricing," Management Science, INFORMS, vol. 64(8), pages 3649-3666, August.
    11. 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.

    Corrections

    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:42:y:1996:i:1:p:65-84. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.