IDEAS home Printed from https://ideas.repec.org/a/eee/iepoli/v65y2023ics0167624523000422.html
   My bibliography  Save this article

Search with two stages of information acquisition: A structural econometric model of online purchases

Author

Listed:
  • Gibbard, Peter

Abstract

This paper presents a methodology for estimating a sequential model of online consumer search. The literature on such structural econometric models typically assumes that, for each alternative, there is only one stage of optimal information acquisition. For many e-commerce websites, however, there are two stages: obtaining information from (1) the search results page and (2) clicking on an alternative. We develop a methodology for estimating a model with two stages, in which the consumer makes optimal decisions in each stage. The search problem is viewed as a variant of a multi-armed bandit problem. We estimate this model using a dataset of clicks and purchases on the website expedia.com. In contrast to models with one stage of optimal information acquisition, our model can be used to analyse not only the clicking and purchasing behaviour of consumers but also the extent to which they browse alternatives on the search results page.

Suggested Citation

  • Gibbard, Peter, 2023. "Search with two stages of information acquisition: A structural econometric model of online purchases," Information Economics and Policy, Elsevier, vol. 65(C).
    • Handle: RePEc:eee:iepoli:v:65:y:2023:i:c:s0167624523000422
    DOI: 10.1016/j.infoecopol.2023.101057
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167624523000422
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.infoecopol.2023.101057?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yuxin Chen & Song Yao, 2017. "Sequential Search with Refinement: Model and Application with Click-Stream Data," Management Science, INFORMS, vol. 63(12), pages 4345-4365, December.
    2. Raluca M. Ursu, 2018. "The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions," Marketing Science, INFORMS, vol. 37(4), pages 530-552, August.
    3. Sergei Koulayev, 2014. "Search for differentiated products: identification and estimation," RAND Journal of Economics, RAND Corporation, vol. 45(3), pages 553-575, September.
    4. J. J. McCall, 1970. "Economics of Information and Job Search," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 84(1), pages 113-126.
    5. Jun B. Kim & Paulo Albuquerque & Bart J. Bronnenberg, 2010. "Online Demand Under Limited Consumer Search," Marketing Science, INFORMS, vol. 29(6), pages 1001-1023, 11-12.
    6. Babur De los Santos & Sergei Koulayev, 2017. "Optimizing Click-Through in Online Rankings with Endogenous Search Refinement," Marketing Science, INFORMS, vol. 36(4), pages 542-564, July.
    7. Jun B. Kim & Paulo Albuquerque & Bart J. Bronnenberg, 2017. "The Probit Choice Model Under Sequential Search with an Application to Online Retailing," Management Science, INFORMS, vol. 63(11), pages 3911-3929, November.
    8. Tat Y. Chan & Young-Hoon Park, 2015. "Consumer Search Activities and the Value of Ad Positions in Sponsored Search Advertising," Marketing Science, INFORMS, vol. 34(4), pages 606-623, July.
    9. Grégory Jolivet & Hélène Turon, 2019. "Consumer Search Costs and Preferences on the Internet," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(3), pages 1258-1300.
    10. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, January.
    11. Rafael P. Greminger, 2019. "Optimal Search and Discovery," Papers 1911.07773, arXiv.org, revised Feb 2022.
    12. Weitzman, Martin L, 1979. "Optimal Search for the Best Alternative," Econometrica, Econometric Society, vol. 47(3), pages 641-654, May.
    13. Raluca Ursu & Stephan Seiler & Elisabeth Honka, 2023. "The Sequential Search Model: A Framework for Empirical Research," CESifo Working Paper Series 10264, CESifo.
    14. Zhenling Jiang & Tat Chan & Hai Che & Youwei Wang, 2021. "Consumer Search and Purchase: An Empirical Investigation of Retargeting Based on Consumer Online Behaviors," Marketing Science, INFORMS, vol. 40(2), pages 219-240, March.
    15. Dirk Lewandowski & Yvonne Kammerer, 2021. "Factors influencing viewing behaviour on search engine results pages: a review of eye-tracking research," Behaviour and Information Technology, Taylor & Francis Journals, vol. 40(14), pages 1485-1515, October.
    16. Peter Gibbard, 2022. "A Model of Search with Two Stages of Information Acquisition and Additive Learning," Management Science, INFORMS, vol. 68(2), pages 1212-1217, February.
    17. Rafael P. Greminger, 2022. "Optimal Search and Discovery," Management Science, INFORMS, vol. 68(5), pages 3904-3924, May.
    18. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2019. "Modeling Consumer Footprints on Search Engines: An Interplay with Social Media," Management Science, INFORMS, vol. 65(3), pages 1363-1385, March.
    19. Elisabeth Honka & Ali Hortaçsu & Maria Ana Vitorino, 2017. "Advertising, consumer awareness, and choice: evidence from the U.S. banking industry," RAND Journal of Economics, RAND Corporation, vol. 48(3), pages 611-646, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rafael P. Greminger, 2022. "Heterogeneous Position Effects and the Power of Rankings," Papers 2210.16408, arXiv.org, revised Dec 2023.
    2. Honka, Elisabeth & Seiler, Stephan & Ursu, Raluca, 2024. "Consumer search: What can we learn from pre-purchase data?," Journal of Retailing, Elsevier, vol. 100(1), pages 114-129.
    3. Rafael P. Greminger, 2022. "Optimal Search and Discovery," Management Science, INFORMS, vol. 68(5), pages 3904-3924, May.
    4. Dan Yavorsky & Elisabeth Honka & Keith Chen, 2021. "Consumer search in the U.S. auto industry: The role of dealership visits," Quantitative Marketing and Economics (QME), Springer, vol. 19(1), pages 1-52, March.
    5. Raluca M. Ursu & Qianyun Zhang & Elisabeth Honka, 2023. "Search Gaps and Consumer Fatigue," Marketing Science, INFORMS, vol. 42(1), pages 110-136, January.
    6. Hana Choi & Carl F. Mela, 2019. "Monetizing Online Marketplaces," Marketing Science, INFORMS, vol. 38(6), pages 948-972, November.
    7. Raluca M. Ursu, 2018. "The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions," Marketing Science, INFORMS, vol. 37(4), pages 530-552, August.
    8. Peter Gibbard, 2022. "A Model of Search with Two Stages of Information Acquisition and Additive Learning," Management Science, INFORMS, vol. 68(2), pages 1212-1217, February.
    9. Greminger, Rafael, 2019. "Optimal Search and Awareness Expansion," Other publications TiSEM ac47e6ff-42a4-4d70-addd-6, Tilburg University, School of Economics and Management.
    10. Rafael P. Greminger, 2019. "Optimal Search and Discovery," Papers 1911.07773, arXiv.org, revised Feb 2022.
    11. Greminger, Rafael, 2019. "Optimal Search and Awareness Expansion," Discussion Paper 2019-034, Tilburg University, Center for Economic Research.
    12. Raluca M. Ursu & Qingliang Wang & Pradeep K. Chintagunta, 2020. "Search Duration," Marketing Science, INFORMS, vol. 39(5), pages 849-871, September.
    13. Giovanni Compiani & Gregory Lewis & Sida Peng & Peichun Wang, 2024. "Online Search and Optimal Product Rankings: An Empirical Framework," Marketing Science, INFORMS, vol. 43(3), pages 615-636, May.
    14. Robert Donnelly & Ayush Kanodia & Ilya Morozov, 2024. "Welfare Effects of Personalized Rankings," Marketing Science, INFORMS, vol. 43(1), pages 92-113, January.
    15. Raluca Ursu & Stephan Seiler & Elisabeth Honka, 2023. "The Sequential Search Model: A Framework for Empirical Research," CESifo Working Paper Series 10264, CESifo.
    16. Jiarui Liu, 2021. "Sequential Search Models: A Pairwise Maximum Rank Approach," Papers 2104.13865, arXiv.org, revised Nov 2021.
    17. Wei Zhou & Zidong Wang, 2020. "Competing for Search Traffic in Query Markets: Entry Strategy, Platform Design, and Entrepreneurship," Working Papers 20-12, NET Institute.
    18. Yuxin Chen & Song Yao, 2017. "Sequential Search with Refinement: Model and Application with Click-Stream Data," Management Science, INFORMS, vol. 63(12), pages 4345-4365, December.
    19. Ilya Morozov & Stephan Seiler & Xiaojing Dong & Liwen Hou, 2021. "Estimation of Preference Heterogeneity in Markets with Costly Search," Marketing Science, INFORMS, vol. 40(5), pages 871-899, September.
    20. Chris Gu & Yike Wang, 2022. "Consumer Online Search with Partially Revealed Information," Management Science, INFORMS, vol. 68(6), pages 4215-4235, June.

    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:eee:iepoli:v:65:y:2023:i:c:s0167624523000422. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/505549 .

    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.